Note: Descriptions are shown in the official language in which they were submitted.
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COMPUTER PROCESSING OF FINANCIAL PRODUCT
INFORMATION AND INFORMATION ABOUT CONSUMERS
OF FINANCIAL PRODUCTS
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation-in-part of Application No. 14/304,633,
filed
June 13, 2014, the entire contents of which are incorporated here by
reference.
BACKGROUND
A financial product includes a good or a service connected with a way in which
an individual manages and uses money. There are various types of financial
products,
including, e.g., checking and saving accounts, investment accounts, credit
cards,
mortgages, home, auto, and renters insurance, cell phone devices and plans,
cable,
interne, and phone plans, health insurance, loan products (e.g., student,
personal, auto),
and other household utility bills.
SUMMARY
In general, in an aspect, there is regularly received through a communication
network from providers of financial products or from an aggregator or both,
current
information about transactions that occur in accounts of consumers of
financial products
that are maintained with providers of the financial products. The received
current
transaction information is stored in a database of information about the
respective
consumers. Machine learning is applied to the stored transaction information
and other
information about the consumers in the database to generate model profiles of
transactions in accounts of corresponding categories of consumers for
corresponding
financial products. As current information about transactions is received,
transactions that
have occurred in the accounts of the consumers of the financial products are
analyzed
using the model profiles for the applicable categories of customers and
financial
products. Each of the consumers for whom transactions occurred that did not
conform to
the corresponding model profile is alerted through a communication network.
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Implementations may include one or combinations of two or more of the
following features. The analyzing of transactions includes analyzing large
transactions.
The alerting of each of the consumers includes prioritizing the large
transactions for
action by the consumer. The alerting of each of the consumers includes
delivering at least
one of email, text, push notification, or other messaging medium, or a
combination of any
two or more of them. It is confirmed with each of the consumers that the
transactions are
for accounts of the consumer. The analyzing of the transactions includes
generating a
profile of the consumers based on the database of information about the
respective
consumers and comparing the profiles of the consumer with the corresponding
model
profile. The model profiles reflect prices paid by consumers in the respective
categories
for particular products. The storing of the received transaction information
in the
database includes identifying financial product providers and transactions
that are
expected to correspond to the respective consumers and storing those
transactions in the
database in association with the respective consumers. The categories of the
consumers
are based on spending behaviors, locations, situations, other attributes, or
combinations
of two or more of them. The analyzing of the transactions includes identifying
an
appropriate one of the model profiles that corresponds to each of the
consumers. There is
training or tuning of a machine learning environment to improve accuracy of
the
identification of the appropriate model profile. Based on changes in the
information about
the respective consumers, transactions that have occurred in the accounts of
the
consumers of the financial products are again analyzed using the model
profiles for the
applicable categories of customers in financial products.
In general, in an aspect, current data related to prices available in a
competitive
market for a financial product is regularly received through a communication
network.
Information about a prospective customer of the financial product is stored in
a database.
The information includes attributes of the consumer that relate to the
financial product.
Based on the attributes of the consumer with respect to the market prices for
the financial
product, a putative price for the financial product is generated by computer.
The putative
price represents a price that the consumer ought to be willing to pay for the
product in the
competitive market.
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Implementations may include one or combinations of two or more of the
following features. The price includes fixed and variable costs to engage in a
transaction
for the financial product. The information about attributes of the consumer
that relate to
the financial product includes current products held by the consumer, credit
characteristics of the consumer, or anonymized characteristics and needs of
the
consumer, or combinations of any two or more of them. The putative price
includes an
optimum or likely lowest price. The putative price is regenerated as current
data about
prices is received. The regular receiving of current data includes receiving
current data
from multiple sources, the current data including market surveys, purchased
data,
wholesale prices, proprietary research data, or a combination of two or more
of them. The
regular receiving of current data includes use of an API, episodic direct file
transfer, a
software bridge, manual input, or a combination of any two or more of them.
The storing
in a database of information about a prospective consumer of the financial
product
includes receiving infoiniation from multiple sources, the infoiniation
including credit
bureau data, underwriting criteria, the consumer's product and personal
preferences, data
provided from third parties concerning the consumer's financial condition and
preferences, location-based information about the consumer, or a combination
of any two
or more of them. The stored information is transformed by computer into
pricing factors
based on the financial product. The transforming includes quantifying and
weighting
parts of the information and combining them into a score. The score is applied
by a
computer to a table to select the putative price. The generating of the
putative price
includes applying machine learning processes to cluster and group financial
products and
profiles of consumers.
In general, in an aspect, information is received from a consumer through a
communication network that is indicative of a request for a putative
underwriting
decision on a financial product. An access is made as a non-provider of the
financial
product, through a communication network, to a credit bureau, for certain
information
that a provider of the financial product would use in making an actual
underwriting
decision on the financial product for the consumer. The putative underwriting
decision is
generated by a computer using the information from the credit bureau and
personal
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information about the consumer that has been stored in a database. The
putative
underwriting decision simulates aspects of the actual underwriting decision in
the
financial product for the consumer. A report of the putative underwriting
decision is
reported to the consumer through a communication network.
Implementations may include one or combinations of two or more of the
following features. The accessing of the credit bureau as a non-provider of
the financial
product does not affect a credit reputation of the consumer. The financial
product
includes credit and the underwriting decision includes whether to extend the
credit to the
consumer. The personal information of the consumer that has been stored in the
database
includes name, address, income, last four digits of the Social Security
number, or a
combination of two or more of them. The putative underwriting decision is
based on
underwriting criteria that include debt to income ratio, past payment
performance, credit
score, open credit lines, number of inquiries from potential providers of the
financial
product, or a combination of two or more of them. The putative underwriting
decision is
generated algorithmically. A machine learning process is applied to cluster
and group
profiles of consumers and credit reports, and the profiles are matched to
approval
probabilities. The putative underwriting decision is made by matching an
optimal product
to a profile of the consumer. The personal information about the consumer that
has been
stored in a database includes information provided by the consumer required to
access a
credit bureau, information about the consumer's incumbent product and personal
preferences, information about the consumer's financial condition and
preferences,
location-based data, or a combination of two or more of them.
In general, in an aspect, current information is maintained in a database
about a
particular consumer. The current information is related to transactions in
financial
products in which the consumer has engaged or indicative of suitable future
transactions
in which the consumer may engage. A database of current information is
maintained
about financial products that are available in a competitive market. The
information
includes prices and features. A computer is used to generate current putative
prices for
financial products. The putative price for each of the financial products
represents a price
that the consumer ought to be willing to pay for the financial product in a
competitive
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market. A selection is made by computer, from the database of current
information about
financial products that are available in the competitive market, of a set of
financial
products that conform to the generated putative prices and to the current
information
about the particular consumer. Information about the selected set of financial
products
and their putative prices is provided to the consumer through a communication
network.
Implementations may include one or combinations of two or more of the
following features. The selecting of a set of financial products includes
selecting financial
products that have the lowest prices, or the best features, or are otherwise
optimal. The
selecting of the set of financial products is repeated in response to changes
in information
about the particular consumer or changes in financial products available in
the
competitive market, or both. The information about the particular consumer
includes
credit worthiness, geographic location, demographics, or a combination or two
or more of
them that correspond to possible selections of the set of financial products.
The
information about the particular consumer includes information about the
situation of the
consumer including needs for financial products, changes in financial
situation, changes
in financial products that belong to the consumer. The providing of the
information to the
consumer includes sending a text, email, push in-application message, or a
combination
of two or more of them. The selecting of a set of financial products includes
matching a
combination of preferences and putative prices to optimize putative prices and
features of
the financial products. The information about the particular consumer is
indicative of the
suitability for the consumer of certain products available in the competitive
marketplace.
The selecting of a set of financial products is done algorithmically. The
selecting of a set
of financial products includes applying a machine learning process to cluster
and group
profiles of consumers and products, and matching the profiles to products.
In general, in an aspect, interactive user interface elements are served from
a
server through a communication network to users of two different respective
websites.
The user interface elements portray financial products that are available on
the market to
particular customers, the suitability of the financial products for the
particular customers,
and putative prices for the financial products. The interactive user interface
elements are
presented with an appearance that conforms to respective branded appearances
of other
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user interface elements that are presented by the respective websites, the
respective
branded appearances being associated with two different host entities.
Implementations may include one or combinations of two or more of the
following features. The relationship between each of the host entities and its
particular
customers include employer and employee, financial advisors and customers
being
advised, or an entity whose purpose is to save money for its customers. The
user interface
elements included within the interactive of user interface are determined at
least in part
by each of the host entities. The server is operated so that personal
information about
customers using the websites cannot be accessed by or delivered to third
parties. A
secured API is exposed through which each of the host entities can access
information
stored at the server.
In general, in an aspect, input data is maintained that represents known
characteristics of consumers of financial products. Output data is generated
that
represents synthetic good decisions about acquiring or using available
financial products
or product features. The synthetic good decisions correspond to respective
clusters of the
consumers based on the input data. Machine learning techniques are applied to
develop
matching algorithms to match the input data to the output data that represents
synthetic
good decisions. The developed matching algorithms are used to suggest good
decisions
about financial products to consumers based on their characteristics.
These and other aspects, features, implementations, and combinations of them
will become apparent from the following description and from the claims.
These and other aspects, features, implementations and combinations of them
can
be expressed as methods, systems, components, methods of doing business,
software
products, user interfaces, databases, and in other ways.
DESCRIPTION
FIGS. 1A-12, 36-43, 49, 51, and 54 are diagrams of environments for providing
users with financial product information.
FIGS. 13-28, 45-48, and 53 are diagrams of graphical user interfaces for
providing users with financial product information.
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FIG. 29 is a block diagram of a system for analyzing financial products.
FIG. 30 is a block diagram of components of a system for analyzing financial
products.
FIGS. 31-35, 44, 50, and 52 are flow charts of processes executed by a system
for
analyzing financial products.
FIGS. 55 through 59 are screen shots of a mobile device.
FIG. 60 is a block diagram of machine learning.
In some implementations of what we describe below, users are provided with
automated, unbiased curating, matching, rating, or scoring (or combinations of
those) for
financial products and providers of financial products. In some
implementations, the
features or functions or applications (or combinations of them) described
below serve as
an automated, unbiased, and expert financial advisor or robot for users. In
some cases, the
features, functions, or applications are exposed to the user on a mobile
device through a
mobile app. In some examples, the automated, unbiased, and expert financial
advisor or
broker can be personified as a personified smart robot, for example, a robot
called Alex
who engages in a natural interaction with the user.
Thus, while some of what we describe below relates fundamentally to matching
of financial products to characteristics of users, these matching functions
can be
incorporated in and part of the foundation for the automated, unbiased, and
the first
financial advisor or robot mentioned above.
Referring to FIG. 1, graphical representation 2 of a personal profile is
shown.
Graphical representation 2 displays the various types of information that are
included in a
personal profile, including, e.g., personal information 4, business preference
information
6, current holding information 8, banking information 10, credit card
information 12,
mortgage information 14, home insurance information 16, and auto insurance
information
18.
In an example, a user completes a personal profile through a series of
graphical
user interface (GUI) screens. A personal profile includes information related
to a user's
personal information, information indicative of financial products associated
with the
user, and preferences for how the user likes to do business.
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We use the term "financial products" broadly to include for example, any
product
or service offered to or used by consumers that involve, for example, ongoing
periodic
payments by consumers to suppliers, or financial characteristics, or ongoing
transactional
relationships between consumers and suppliers or of any combination of two or
more of
them. Financial products, e.g., checking and saving bank accounts, investment
accounts,
credit cards, insurance policies, mortgages, home, auto, and renters
insurance, cell phone
devices and plans, cable, internet, and phone plans, health insurance, loan
products
(student personal, auto), and other household utility bills, to name a few.
Preferences include what products the user currently has, what the user needs
at a
given moment in time, how he/she likes to do business with current financial
product
providers, and how he/she would like to do business with financial product
providers in
the future. We use the term financial product providers broadly to include,
for example,
any party that provides any kind of financial product, or a financial product
embedded
within another product (e.g. a cell phone financing offer tied to the service;
a financing
offer tied to the purchase of a vehicle, etc.), not limited to banks,
insurance companies,
lenders, or other financial services companies and institutions.
The first GUI screen is for entering personal information 4, such as name,
address, zip code, occupation, income, and similar information that is
independent of
financial products.
On the next GUI screen, the user enters business preference information 6,
e.g.,
information specifying how the user likes to do business with financial
companies. This
type of information specifies whether the user prefers to do business in
person, at a
branch office, over the phone, via the Internet, etc.
The next GUI screen is for the user to enter current holding information 8
that
indicates which financial products the user currently holds. Financial
products would
include their current credit cards, bank accounts, mortgages, home insurance,
auto
insurance, and other financial products. Next, the user would enter banking
information
about the user's current bank accounts. This type of information would include
the
name of the banking institution, how they use their bank, and other details
about the
banking services the user currently receives. Next, the user enters credit
card information
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12 about the user's current credit cards. This type of information includes
the name of the
credit card and credit card institution, interest rates, current balances,
rewards systems,
how they use their credit cards, and other details about the credit cards the
user currently
uses.
The next GUI screen is for entering mortgage information 14 about the user's
current mortgages. Mortgage information includes property information
including
address and property value, mortgage information including lending
institution, original
mortgage amount, interest rate, mortgage terms, amount currently remaining on
mortgage, and other related mortgage information.
The next GUI screen in the process is for the user to enter home insurance
information 16 about the user's current home or renter's insurance. This type
of
information includes the name of the insurance provider, the current premium,
and the
current coverage.
The next GUI screen is for the user to enter current auto insurance
information 18,
including the name of the insurance provider, the premium amount, and the
current
coverage. In a variation, the personal profile includes other types of
information,
including, e.g., information about other products, such as investments, health
insurance,
utilities, and cell phone plans.
In some implementations, the kinds of infounation that are described above as
being entered by a user through a GUI screen need not be manually entered but
can be
picked up automatically by the system connecting to, for example, online
accessible
accounts of the user, with authorization from the user.
As shown in FIG. 1B, after some or all of the personal profile information is
completed by the user, this information is stored in a customer profile
database 22. The
schema for this database includes tables that correspond to each of the types
of
infointation included in the personal profile, including e.g., a personal
information table
24 for storing personal information 4, business preference table 26 for
storing business
preference information 6, current holding table 28 for storing current holding
information
8, banking information table 30 for storing banking information 10, credit
card table 32
for storing credit card information 12, mortgage information table 34 for
storing
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mortgage information 14, home insurance information 36 for storing home
insurance
information 16, and auto insurance information 38 for storing auto insurance
information
18. As discussed later, in some implementations, additional or different
information
related to personal profiles can be stored in tables of the customer profile
database 22 and
used by the system for various purposes. Such information can be gathered from
different
or other sources than the consumers themselves, as also discussed later.
Referring to FIG. 2, data about financial products is collected from multiple
external data sources 50, transferred via a network 54, and stored in a
financial product
information database 56 (through a system). In operation, a system (not shown)
collects
external financial product data 52 from various external and internal data
sources. This
data includes information about the products and pricing currently offered to
consumers
for bank accounts, credit cards, mortgages, home insurance, auto insurance,
and other
financial products. The system used to collect the data depends on its source.
External
data providers providing information about a specific product (e.g. taxes and
fees on a
specific mortgage in a specific location) may offer an API link to call the
required data
into the system, where it is stored and presented to the user through a
database and
computing software. Some information sources may be delivered as a Common
Separated Values (CSV) file, which allows column-based data sorting by a
spreadsheet
package or similar software program. Information collected manually (as
described
below) may be entered into electronic forms, which are then arrayed through a
software
interface into a relational database or CSV file. Each of these source inputs
on a given
product or provider are then transmitted to a centralized data storage through
a network
for further analysis and eventually, to be added to an inventory database as
potential
matches for the recommendation engine.
There are various types of data sources, including, e.g., databases purchased
from
third parties, data collected independently by staff, data gathered from
customers of
financial providers via surveys, telephone calls, and interviews, data
gathered from
interviews of financial product providers, data gathered from social media
sentiment, and
data from independent news and information sources. As discussed later,
additional or
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other data sources can be used to obtain information about financial products
and the
parties who provide them.
The network 54 includes the Internet and any other means of transferring this
data
into a financial product infoimation database 56.
This financial product information database 56 includes the following scheme
for
an individual financial product: a product name table 58, a product provider
table 60 for
specifying the name of the provider, address, contact information, and unique
internet
link to the provider's product webpages, a product pricing information table
62, which
includes the current and historical pricing or rate information for a product,
a description
of product table 66, which includes an overview about the product, its unique
characteristics designed for specific users, and any other information that a
consumer
may find interesting about the product, and other product information table
68, which
would include other information from internal and external financial product
data sources
that contain information required to or useful in making a specific match to a
specific
user, e.g., the availability of a branch office, which on the one hand, may be
a
requirement for User A, or alternatively, for User B, may be something she
neither needs
nor values.
There are various types of financial products including bank accounts, credit
cards, mortgages, home insurance, and auto insurance, as mentioned earlier.
Financial
products can include, for example, a financial product embedded within another
product
(e.g., a cell phone financing offer tied to the service; a financing offer
tied to the purchase
of a vehicle, etc.). In some implementations, discussed later, additional or
other tables can
be part of the product information database and be used for a variety of
purposes.
Referring to FIG 3, the information found in the financial product information
database 56 is appended (e.g., by a system) with scores and ratings from a
financial
product curation and scoring system 70, which is used later for generating a
unique rating
for each financial product. Financial product curation and scoring system 70
includes a
rating and score for attributes of individual financial products and financial
product
providers, including but not limited to ratings for location, financial value,
service
ratings, reputation, product features, and will go other ratings information.
Significantly,
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the financial product curation and scoring is done without bias towards any
type of
product, any product provider, or any other factor or parameter unrelated to
the
consumer. For that reason, the curation and scoring, and the resulting scores
and ratings
can garner a high level of trust in the marketplace and among users and
provide useful
and credible information for the marketplace and those users.
Location information rating 72 includes a rating score indicative of how
appropriate the product or provider being rated is for a consumer based on the
consumer's location.
Financial value rating 74 is a rating score based on the financial value
related to
pricing, rate and other financial considerations for the product that would be
relevant to a
consumer.
Service ratings 76 are ratings and scores related to how well the company
performs customer service over the telephone, in person, at branches and
offices, over the
internet, and any other service channels at which the company does business
and which
are determined that consumers find value.
Reputation information rating 78 includes rating and scores based on the
provider's reputation as determined by what other customers of a company say
about that
company and how it is to do business with the company, as well as third-party
ratings
systems and how they rate the company.
Other ratings information 80 includes ratings that are specific to product
types,
such as rating categories specific for a credit card that would not be
appropriate for other
financial products, or rating categories for a mortgage that might not be
appropriate for
other products.
Referring to FIG. 4, curated and scored financial product information database
84
is an updated version of database 56 (FIG. 3), in which the curation, scoring
and ratings
have been added. The system curates, scores, and rates products by starting
with the
universe of thousands of available products and providers in each product
category.
These are then filtered according to which locations (states, zip codes,
counties, cities) in
which each product is offered, then apply a filter through which pass only the
established
and legitimate providers, then we apply a further filter to identify companies
that do a
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significant amount of business in a given geographic location, and then we
filter on
criteria that determine customer value, such as price, interest rates, fees,
etc. Other filters
are also applied, including product incentives offered consumers, benefits
such as
warranties and insurance, and the overall reputation of the product and
provider. A
scoring rubric is then applied to determine a score on a scale of 0 to 100, as
described in
further detail below.
The curated and scored financial product information database 84 includes the
following tables for a particular financial product and/or financial product
provider.
These tables include: the product name table 84, the product provider table 86
for storing
the product provider name and information such as address and contact
information,
product pricing information table 88 for storing information indicative of
rates and prices,
product rate information table 90 for storing information including interest
rates and fees
charged for a product, description of product table 92 for storing information
indicative
of an overview description about a financial product or provider, location
information
table 94 for storing information specifying geographical locations such as
states, cities
and zip codes that the product is of the highest value and the rating for this
product in
certain geographical locations, the financial value rating table 96 which
specifies how the
product has been scored and rated, a service ratings table 98, which includes
the service
ratings, a reputation info ratings table 100, which would include the rating
and scoring
information entered into the system for a product reputation, and other
ratings and
inforniation table 102 which would include any other ratings entered into the
system.
This information from the curated and scored financial product information
database 82 is displayed on a client device as a consumer GUI 104 via a
network, which
may include the Internet or other networks that convey information to the
user. This
consumer GUI 104 is rendered via web pages, mobile phone applications and
website,
and other content accessible by consumers over public networks. Consumer GUI
104
displays highly rated financial products 106, e.g., financial products with
increased
relevance to the consumer, relative to relevance of other financial products
to the
consumer. In an example, a rating for a financial product is determined by
summing or
averaging a service rating or a financial value rating, as described in
further detail below.
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Highly rated products and companies are determined by a blended rating of the
product
and company value, incentives for consumers, benefits and perks for consumers,
and
overall reputation based on consumer opinions and expert ratings. The content
pages
listing highly rated financial products include the information stored in the
curated and
scored financial product information database 82 for the particular financial
product that
is highly rated. Depending on the user's specific needs, preferences, and
situation, these
provider and product ratings may have no, some, or significant impact to the
matching
system (described below). For example, a provider may be highly rated because
it offers
face-to-face services in branch locations, but that high rating is relevant
only to those
users that value face-to-face service interactions. The matching and
recommendation
system renders and displays to the user those idiosyncratic selection factors
based on her
preferences and situation.
FIG 5 shows a financial product recommendation process 120 in which the
customer profile database 22 and the curated and scored financial product
information
database 82 are combined through a financial product personalized
recommendation
system or engine 126 (including one or more algorithms), which in turn
generates
financial product recommendations and the reasons they were chosen, for a
specific
consumer.
Process 120 is implemented by a system (not shown). In operation, the system
retrieves (122) the attributes in the customer profile database 22 and
retrieves (124)
attributes of the curated and scored financial product information database
82,
eliminating products in the curated and scored financial product information
database 82
that are not appropriate for the consumer based on their customer profile
database 22, as
described in detail below. The resulting set of personalized financial product
recommendations 140 is a customized subset of the original curated and scored
financial
product information database 82.
Financial product personalized recommendation engine 126 generates these
recommendations 140. In operation, engine 126 analyses (128) customer location
information (for example, where the consumer lives and works as specified in
personal
information table 24) and analyses (130) the customer profile preferences (as
specified in
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business preference table 26). Engine 126 compares (132) customer location
information
and profile preferences to current customer products (if entered by the
consumer and as
specified in table 28) and attributes of various financial products and
applies (134) a
personalized pricing and credit filter, which eliminates products based on the
pricing and
credit situation and needs completed by the consumer and stored in the
customer profile
database 22 and the attributes of the financial products.
In this example, products are eliminated when there is a mismatch between a
consumer preference and an attribute of a financial product, e.g., when a
financial
product is unable to meet the needs and/or preferences of the consumer. In an
example,
financial products that the consumer does not currently or that the consumer
has indicated
a need for are recommended. Pricing needs may include the minimum savings
required
by the consumer to change product providers or product types, or modifications
to the
current product the consumer has, or other thresholds.
Matching is generated by an algorithm that takes into account the consumer's
location, budget, spending habits, credit score, family situation, preferences
for
conducting business both online and in person, occupation, age, debt
situation, and other
personal financial situations. This recommendation process would also be
duplicated
according to a schedule determined the consumer. For example, a consumer could
choose
to have products recommended monthly. This process, which is a monitoring
process,
would then occur each month, with the consumer receiving a notification that
new
recommendations have been generated by the system. Alternatively, based on
information provided by a mobile application, or through other data linked to
the user
(e.g., a change in shopping habits, or change in product usage perceived by
the system
via linkage to the account), the system will determine that the current
products held by
the user are a mismatch, perhaps resulting in a too-high price given the
change in location
or situation, of which the user may be totally unaware. The system perceives
that
mismatch and alerts the user proactively based on inputs the system has
passively
collected to help optimize the user's situation.
Engine 126 also applies (136) other recommendation filters. Based on
application
of the filters, engine 126 generates (138) a personalized financial product
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recommendation 140, which may be presented or transmitted to consumers on web
pages
listing personalized financial product recommendations 142, e-mail pages
listing
personalize financial product recommendations 144 as well as other software
applications
listing these recommendations. Process 120 is implemented periodically (e.g.,
on an
ongoing basis), based on the preferences of the consumer, changes to the
customer profile
database described above, changes to the curated and scored financial product
information database, and the logic programmed into the system.
Other approaches can be used to match the consumer's interests and needs to
products and services and to filter information to be presented to the
consumer, including
machine learning implementations that we describe later.
Referring to FIG. 6A, a graphical representation 160 of a consumer website or
a
mobile application (hereinafter "consumer website 160") is shown. (In many
cases, when
we refer to websites we also intend to refer to mobile applications.) Consumer
website
(or mobile application)160 allows consumers to maintain their anonymity and
avoid
providing contact information to financial product providers while they secure
accurate
quotes, quote estimates, and product pricing details that are necessary to
make an
informed final decision for those products that require an underwriting (e.g.,
insurance
and loan products) in which the provider determines the final price based on
user inputs,
such as location and creditworthiness, for example, user provided
creditworthiness. In
existing systems, users must provide personal details and contact information
to
providers, which in turn can be used to abuse the user's privacy with
telemarketing calls,
spam emails and texts, and inclusion into the provider's database of
prospects.
Consumer website 160 allows consumers to receive multiple price quotes at
once,
without filling out multiple forms or providing personal contact details. A
price quote is
the cost or payment for a financial product. For example, a price quote
includes the
monthly payment amount for auto insurance, or the payment information for a
new
mortgage. In this example, a consumer visits a consumer website 160 (or
similar software
application) to provide personal information to receive a price quote on a
financial
product from a financial product provider. For returning consumers, some or
all of this
information may be stored in the customer profile database 22 (FIG. 1B), so
that the
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consumer can avoid re-entering infoiniation, or sharing personal contact
information
directly with a provider. This website 160 lists financial product information
162 for
individual financial products. The website 160 also lists saved financial
products 164,
which are products the consumer has indicated an interest in during this or
previous usage
of the consumer website or similar software application. The consumer website
160 will
also generate a request 166, or multiple requests, for a personal price quote
from a
financial provider. This request is sent over a network to a recommendation
system. The
recommendation system associates, in database 22, the request with the
consumer's
profile. The system sends an email or text notification to a client device
used by the
consumer.
Referring to FIG. 6B, through the notification, the consumer is alerted to
visit a
consumer website 171 or similar software application to view the completed
quote
application 172. The consumer approves the completed quote application, which
may be
for multiple quotes. Information for these consumer quote applications is sent
to the
recommendation system, which stores the completed quote application in
database 22.
Referring to FIG. 6C, customer profile database 22 is updated with, complete
quote
application information table 174 to store the complete quote application
information. As
described later, in some implementations additional or other features can
enable the
consumer to provide other information that is stored in other tables in the
customer
profile database.
Referring to FIG. 7, networked environment 201 includes a financial provider
portal 206 that enables financial product providers to view anonymous consumer
quote
applications, and to append these quote applications with quotes and pricing
information
for the user's review, but without revealing the user's identity or contact
information as
described above. The recommendation system generates data for the financial
provider
portal 206, that when rendered on a display device of a financial product
provider
displays the portal. The recommendation system also sends messages to
consumers about
the status of financial quote requests, and allows consumers to review quote
and pricing
information provided by providers all in one place, and using a common set of
consumer
preferences. In this example, the recommendation system sends (via network
200) a
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message or alert 202 to specific financial providers selected by a consumer.
For example,
the consumer can specify which providers are to provide quotes. This message
may be an
e-mail message or an alert visible on a website or software application.
The provider connects over a network 204 to the financial provider portal 206.
The financial provider portal includes a secure financial provider portal
login 208
process. After logging into the portal 206, the financial provider can view
completed
consumer quote applications 210. Next, the financial provider can add quote
details 212
for the consumer to view.
The consumer is notified over the network 214 via a message or alert 216,
which
may be delivered via email, text, or instantly while the consumer is still on
the website or
consumer application described in the process on FIG. 6. This message will
instruct the
consumer to view their quote and other pricing information from the financial
provider.
The consumer visits a consumer website 219 or a mobile or other software
application (as
noted earlier, whenever we mention websites we also intend to include software
applications including mobile applications). The consumer then views his/her
completed
financial product quote information 220 that is received from the provider. In
this
example, networks 200, 204, 214, 218 include a same network, e.g., the
Internet. In
another example, networks 200, 204, 214, 218 may include differing networks.
Referring to FIG 8, networked environment 239 enables a consumer to visit a
consumer website 240 (or use a consumer software application) to make a full
application
for a specific product selected by the consumer. Full applications convey
consumer
information required to secure a product and includes information for a
provider to
approve, deny, and price a consumer application. This recommendation system
allows
consumers to apply for multiple financial products at once, without filing out
multiple
forms or sharing sensitive, personal information across multiple provider
application
websites leading to abusive situations described above. The consumer is able
to apply for
these financial products by providing less additional information about
himself/herself
because most of his/her information is already stored in the customer profile
database 22.
This process begins when a consumer visits a customer website 240 or similar
software application. This website includes financial product information for
individual
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financial products 242. The website 240 also includes information representing
saved
financial products 244, which is a list specifying products that have been
reviewed by the
consumer (e.g., either currently or previously). The consumer website 240
enables a
consumer to request 246 a consumer application for one or more financial
products. In
response, consumers are presented with an unpopulated (e.g., empty) financial
product
application 248 and the option to fill the application using the information
in the
customer profile database 22, which has been saved by the consumer previously.
In an
example, a portion of the empty application is completed using contents of the
consumer
profile database 22, shared via the network 251. In this example, a partially
completed
application is transmitted via network 253 to a client device of the consumer,
e.g., for
display in consumer website 260. Through consumer website 260 (which is an
updated
version of consumer website 240), the consumer views the partially completed
financial
product application 254 and completes the application with extra information
needed to
complete the application. This completed financial product application 256 is
sent via
network 259 and new information is stored in the customer profile database 22,
in new
tables for completed quote and application info 174.
Referring to FIG 9, networked environment 279 enables a financial product
provider to interact with a provider portal and view full product
applications, approve,
deny, and price these applications, as well as provide information for
consumers about
the application status and decision. The recommendation system (not shown)
also allows
for messaging to consumers about the status of a full application over a
network. The
system transmits a message or alert 282 to financial providers sent over a
network 280
indicating a consumer has completed a full application for a specific
financial product.
This message may be an email message or an alert visible on a website or
software
application. The provider connects over a network 283 to a financial provider
portal 284.
The financial provider portal includes a secure financial provider portal
login 284
process. After logging into the recommendation system, the provider can view
completed
consumer financial product applications 288 for that provider's products. The
provider
can approve, deny, and/or price full applications and enter application
approval/denial
details 290 for the consumer to view. The consumer is notified over the
network 291 via
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a message or alert 292, which may be delivered via email, text, or instantly
while the
consumer is still on the website or consumer application described in the
process on FIG.
8. This message will instruct the consumer to view application information
from the
financial provider.
Referring to FIG. 10, networked environment 293 provides for a consumer to
receive approval from one or more financial product applications. In this
example, the
consumer selects (in a GUI) information indicative of the financial product,
and is
connected to the provider to complete the application process and to obtain
the financial
product.
On consumer website 294, the customer may view his/her application information
from financial product providers 296 and also decide (e.g., via selectable
portion 298) if
he/she would like to continue with an approved application. When the customer
decides
to continue with an application the financial provider is notified via the
network 299 by
receiving a message or alert 300 that the consumer would like to continue the
process.
The financial provider visits a financial provider portal 302 via the network
301,
logs into the financial provider portal using a secure financial provider
portal login 304.
Next, the financial provider views the consumer's approval details 306. The
application
process is now complete and the website is updated to display an application
process
complete notification 308. Following completion, the consumer may obtain the
financial
product. Via the network 309, the provider conveys the terms and conditions,
product
disclosures, and other collateral 312 through the system to the consumer
website 310.
The consumer website 310 provides a choice to the consumer to store this
information
314 and product details in the consumer profile database 22 (FIG. 1B).
Referring to FIG. 11, networked environment 321 enables generation of custom
financial products, based on the consumer's specific situation and needs.
Networked
environment 321 includes recommendation system 322 for generating custom
financial
products. For example, the recommendation system 322 determines that various
customers in the customer profile database 22 (FIG. 1B) live in the same zip
code and
needed to renew auto insurance. In this example, recommendation system 322
generates a
custom auto insurance policy with a discount for this group of consumers
and/or special
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localized features for this group. Financial products eligible for
customization include
bank accounts, credit cards, mortgages, home insurance, auto insurance, and
other
financial products.
In operation, recommendation system 322 analyzes consumer information stored
in the customer profile database 22. Based on the analysis, recommendation
system 322
identifies common characteristics of consumers and common product needs of
consumers
through a software matching algorithm. For example, recommendation system 322
may
determine a group of consumers with a car insurance deductible (as specified
in auto
insurance table 38) that is above a threshold amount and that live in a
particular
geographic location (e.g., as specified by personal information 24). Once a
need is
discovered (e.g., a need for car insurance with a lower deductible in a
particular
geographic location) for one or many customers in the system, recommendation
system
322 generates custom financial product 324, e.g.., by facilitating with
insurance providers
a group discount.
Custom products may be generated in many different ways based on the needs of
a group via computer algorithm to determine common characteristics in the base
of
consumers. For example, a single auto insurance product may be created for a
group of
consumers who all need to renew their auto insurance at the same time and/or
same
general location or type of auto. Or a credit card could be created for a
group of
consumers interested in the same type of rewards. Or the system could create a
product
that would receive the highest possible score in the rating system. Qualifying
consumers
are notified over a network 326 with a message 328 to customers who qualify
for custom
financial products 324. This message could be delivered as email, text
message, or other
communication methods. Consumers access a consumer website 332 or client
software
application to view details 334 about the custom financial product 324.
Consumer
website 332 includes a selectable portion 336, selection of which enables
consumers
interested in the custom financial product to apply for the product.
In some implementations, other approaches, such as machine learning, can be
used to determine common characteristics in the base of consumers.
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Referring to FIG. 12, networked environment 349 enables a consumer to generate
a digital image of a financial product statement or other consumer bill and
upload the
image of the financial statement through a network, where the image is then
processed
through a financial statement imaging process, relevant data is extracted from
the image,
the image is deleted and the relevant data is stored in the customer profile
database. This
allows the consumer to simplify the process of determining if a different
product would
better suit his or her needs.
Networked environment 349 includes client device 350 (e.g., a mobile phone or
personal computer device). Using client device 350, the consumer scans (352)
or takes
the photograph of a financial statement. Financial statements include bank
account
statements, credit card statements, mortgage statements, auto and home
insurance policy
statements and coverage details, and other similar financial statements, as
well as
periodic bills for consumer services such as mobile phone bills and plans.
Once the consumer has created an image of the statement, client device 350
uploads (354) the image of the financial statement over the network 356 and
into a
system 358 for financial statement image processing (e.g., the recommendation
system).
In a variation, the consumer uses a mobile phone application to convey the
image to the
system.
This financial statement image processing system 358 processes (360) the
user's
financial image statement, extracts (362) relevant user financial data and
personal profile
infonnation 362, and deletes (364) the financial statement image 364. The
extracted
relevant consumer profile information is stored in the customer profile
database 22 in the
appropriate tables in the customer profile database schema. This information
can then be
used in the future to help consumers find better financial products related to
the financial
product for which the original image statement was processed. The multiple
networks
included in each of FIGS. 7-12 may be a same network or different networks.
Referring to FIG. 13, GUI 400 enables a consumer to enter personal
information,
which is stored and used in the system described in FIG. 1 in which consumer
personal
and financial information is stored in a customer profile database 22. In some
instances
(not shown) a user can select to link and upload (via an API) to those third
parties (and
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third party aggregators of information) her checking and credit card account
information
and transactions, her credit report, her household makeup and cars owned, and
other
accounts; this action reduces the manual entry of the user's infolination
required by the
matching engine. A software application would convey this information to the
appropriate section of the customer profile database 22. GUI 400 includes a
section
labeled my profile 402, which includes an about me section 404, a personal
information
section 406, a how I like to do business section 408, a what I have now
section 410, a my
banking section 412, a my credit cards section 414, a my mortgages section
416, a my
home insurance section 418, and a my auto insurance section 420. In a
variation, a GUI
could also include sections for other financial products similar to bank
accounts, credit
cards, mortgages, home insurance, and auto insurance.
Using personal info section 406, a consumer enters in personal information,
such
as name 422, email address 424, zip code 426, occupation or job information
430, home
ownership status 430, credit score estimate 431 and events that may happen in
the future
432, such as purchasing a home, getting married, having children, starting a
new job,
moving, etc. GUI 400 is used to generate a profile of a consumer that will be
analyzed via
a computer algorithm to recommend financial products to the consumer. A
personal
profile is information specific to that consumer that promotes determination
of correct
financial product for his or her needs. Data entered into this GUI 400 would
not be
limited to the data fields illustrated here. These have been placed here for
examples, but
other general information about consumers could be added. Once a consumer
completes
this profile, he/she saves this information with the "next" button. This
personal
information is saved, by a recommendation system, in a customer profile
database 22 for
later use in assisting and recommending bank accounts, credit cards,
mortgages, home
insurance, auto insurance, and other financial products (and financial
products embedded
into another product offer, like cell phone financing offered through cell
phone service
agreements, or financing offers for a specific ear) to the consumer.
Referring to FIG. 14, GUI 439 is an updated version of GUI 400 in which the
how
I like to do business section is selected. GUI 439 enables a consumer to
indicate the
consumer's preferences when doing business with financial service providers.
GUI 439
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includes portion 440 and controls 442 for consumers to specify a level of
financial
organization. GUI 439 also includes section 444 for a consumer to rank various
attributes
444a-444d. Section 444 includes ranking list 445 for a consumer to rank
attributes 444a-
444d. Here the consumer would arrange the most important elements from top to
bottom,
most important to least important in terms of preference. Ranked attributes
would include
face to face interactions, highly recognized brands or companies, great
customer service,
and lowest prices. Data entered into this GUI 439 would not be limited to the
data fields
illustrated here. These have been placed here as examples, but other
attributes measuring
consumer preferences in selecting and working with financial product providers
could be
added. Once a consumer completes this profile, the consumer saves this
information with
the Next button 452. Following section of the Next button 452, the
recommendation
server receives a request to store the information entered into GUI 439 and
saves this
information in the customer profile database 22 for later use in recommending
bank
accounts, credit cards, mortgages, home insurance, auto insurance, and other
financial
products (and financial products embedded into another product offer, like
cell phone
financing offered through cell phone service agreements, or financing offers
for a specific
car) to the consumer.
Referring to FIG. 14, GUI 439 is an alternative or complementary method for
consumers to define their financial product needs by selecting a specific
situation (e.g.
having a baby, moving, traveling overseas) or lookalike profile (e.g. single
with new job,
newly married couple, first baby on the way, family starting a home search,
etc.) that will
determine needs and preferences in a financial product. Data entered into this
GUI 439
would not be limited the data fields illustrated here. These have been placed
here as
examples, but other attributes measuring consumer preferences in selecting and
working
with financial product providers could be added. Once a consumer completes
this profile,
the consumer saves this information with the Next button 452. Following
section of the
Next button 452, the recommendation server receives a request to store the
information
entered into GUI 439 and saves this information in the customer profile
database 22 for
later use in recommending bank accounts, credit cards, mortgages, home
insurance, auto
insurance, and other financial products (and financial products embedded into
another
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product offer, like cell phone financing offered through cell phone service
agreements, or
financing offers for a specific car) to the consumer.
Referring to FIG. 15, GUI 455 shows the elements that are displayed, following
selection of what I have now section 410. GUI 455 includes section 460 for
specifying
the consumer's current financial products via controls 462a-462e. Through
selection of
one or more of controls 462a-462e, the consumer indicates if the consumer
currently has
a bank account, credit cards, mortgages, home insurance, and/or auto
insurance,
respectively. This would enable this system to ask questions about these
financial
products later to enrich the profile. Alternatively, (not shown) a user can
select to link
and upload (via API) to those third parties (and third party aggregators of
information)
her checking and credit card account information and transactions, her credit
report, her
household makeup and cars owned, and other accounts; this action reduces the
manual
entry of the user's information required by the matching engine. A software
application
would convey this information to the appropriate section of the customer
profile database
22. Data entered into this GUI 455 would not be limited to the financial
products
illustrated here. These have been placed here as examples, but other financial
product
types could be listed in the future. Once a consumer completes GUI 455, the
consumer
causes the recommendation system to save this information through selection of
next
button 464. Infotmation specifying the products selected is saved in a
customer profile
database 22 for later use in assisting and recommending bank accounts, credit
cards,
mortgages, home insurance, auto insurance, and other financial products (and
financial
products embedded into another product offer, like cell phone financing
offered through
cell phone service agreements, or financing offers for a specific car) to the
consumer.
Referring to FIG. 16, GUI 465 is displayed, following selection of my credit
cards
section 414. GUI 465 includes section 470 for entering details about a
consumer's current
credit cards. Section 470 includes field 474 for entering information
specifying a name of
a credit card provider, field 476 for entering information specifying amount
spent per
month in dollars, field 478 for entering information specifying current
balance in dollars,
field 480 for entering information specifying current interest rate in
percentage, selectable
control 482 for selection a type of card rewards 482 (e.g., rewards given when
a credit
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cards is used, including, e.g., cash rebates, airline mile points, hotel
points, etc.), and
selectable control 483 for specifying credit card features that are most
important,
including, e.g., low interest rate, low annual fee, no foreign transaction
fees, and so forth.
Once a consumer completes this GUI 465, the consumer could finish entering
credit cards
by selecting finished control 486 or enter information about another credit
card by
selecting add another card control 484. The information entered here by the
consumer is
saved via a network in a customer profile database 22 for later use in
assisting and
recommending credit cards to the consumer. In some cases, for users who have
chosen to
link (via an API) provider accounts (and third party aggregators of
information)
containing his/her checking and credit card account information and
transactions, credit
report, household makeup and cars owned, and other product and information
accounts,
logic powered by one or more software algorithms aided by machine learning
software
can determine those products most in need of review. This will reduce the
reliance of the
system on user self-diagnosis and could reveal problems unknown to the user. A
software
application would convey this information to the appropriate section of the
customer
profile database 22. In a variation, there are similar GUI screens for the
consumer to enter
information about current bank accounts, debit card, mortgages, home
insurance, auto
insurance, and other financial products (and financial products embedded into
another
product offer, like cell phone financing offered through cell phone service
agreements, or
financing offers for a specific car).
FIG. 17, GUI 500 displays information to aid the consumer in selecting the
right
product (and is used by analysts researching and scoring products). GUI 500
includes title
portion 502 to specify that GUI 500 displays information that is based on
ranking data for
financial products that is entered into a financial products scoring & ranking
system. GUI
500 includes menu 501 for selecting various types of products for which scores
and ranks
are displayed. In this example, menu 501 displays scores and ranks for various
mortgage
products, following selection of mortgage section 508 of menu 501. Menu 51
also
includes banking section 504, credit card section 506, home insurance section
510, auto
insurance section 512, and other products section 514, selection of which
displays
rankings and scores for bank accounts, credit cards, home insurance, auto
insurance, and
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other financial products, respectively (and financial products embedded into
another
product offer, like cell phone financing offered through cell phone service
agreements, or
financing offers for a specific car).
In this example, GUI 500 includes product section 516 that lists various
products
516a-516n, e.g., mortgage products. A mortgage product is evaluated across
multiple
variables, including, e.g., price and so forth. For mortgages, variables
include: the quality
of the website, phone service, ease of application, quality of phone service,
availability of
local offices, closing rates, closing costs, denial rate, amount of loans the
provider sells to
third parties, percentage of customers who leave provider for another
provider, customer
reviews and commentary on other websites, expert opinions, percentage of
complaints to
the Consumer Finance Protection Board (CFPB), interest rates, among other
mortgage
attributes.
For credit cards, variables include, quality of credit card rewards, annual
percentage rate, annual fees, length of billing grace period, other fees,
additional
incentives, promotional interest rates, warranties, purchase protection, EMV
chip
presence, merchant acceptance, car rental insurance, travel insurance, CFPB
complaints,
expert reviews from third parties, and consumer reviews from social networks
and
websites.
For home and auto insurance variables include pricing, customer retention,
flexible claims procedures, location convenience, website quality, local agent
availability,
reviews and commentary from customers on other websites and social media,
expert
ratings, and state agency ratings.
Variables for checking and savings bank accounts include monthly fees,
minimum balance requirements, ATM fees, check ordering fees, overdraft fees,
interest
earned, only bill payment and check writing, website quality, ATM
availability, local
branch hours, phone service hours, CFPB complains expert reviews, and reviews
and
comments on other websites and social media.
In this example, GUI 500 includes variable portion 518 with fields 518a-518n
for
entry of values for the variable specified by variable portion 518 (i.e.,
price). For
example, a user enters a score in field 518a for product 516a, field 518b for
product 516b
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and so forth. In an example, for each mortgage, analysts enter several scores
that are
values for the variables, ranking each mortgage for that scoring attribute.
GUI 500 also
includes variable portions 520, 524, 526 and 528 and 529, each of which
includes
associated fields for entering values of the respective variable. Using the
values for the
various variables (as specified by the values entered into the fields of
variable portions
518, 520, 524, 526 and 528), the recommendation system calculates a propriety
score for
each of products 516a-516n. GUI 500 includes proprietary score section 522 for
display
of a calculated proprietary score, for each of products 516a-516n.
For example, the proprietary score may be based on a summation, an average, a
mean and so forth, of the scores of the variables for a product. Mathematical
formulas are
applied (by a system) to the values of the variables to generate the
proprietary score, as
described in further detail below. This score would be used to assist
consumers in
understanding which products are highly rated. Ranking and scoring systems are
associated with each financial product type, including bank accounts, credit
cards,
mortgages, home insurance, auto insurance, and other financial products (and
financial
products embedded into another product offer, like cell phone financing
offered through
cell phone service agreements, or financing offers for a specific car).
Referring to FIG. 18, GUI 530 displays a consumer view of highly ranked
mortgages, e.g., mortgages with proprietary scores that exceed a threshold
value and/or a
predefined number of mortgages that are associated with the highest propriety
score,
relative to proprietary scores of other mortgages. GUI 530 includes product
section 540
for display for highly ranked products (e.g., mortgages). Product section 540
includes
proprietary picks section 542 for displays of the highest rated mortgages from
the ranking
and scoring process shown in FIG. 17.
Proprietary picks section 542 includes product information 544a, 544b, 544c.
(For
implementations that use mobile apps, the pies may be handled in other ways.)
Using
product information 544a, 544b, 544c, consumers can view multiple mortgages
with
details for each mortgage to allow side-by-side comparison. Details include
the name of
the mortgage provider, details about the mortgage terms, and the proprietary
scores 546a-
546c, as determined by the scoring and ranking process described in FIG. 17.
FIG. 18
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shows this arrangement for showing highly ranked mortgages, but this view
would also
be available for other financial products such as bank accounts, credit cards,
mortgages,
home insurance, auto insurance, and other financial products (and financial
products
embedded into another product offer, like cell phone financing offered through
cell phone
service agreements, or financing offers for a specific car).
Referring to FIG. 19, GUI 560 displays tab 570 of a consumer view of highly
ranked recommended financial products. These recommendations are created based
on
the information completed by the consumer in the personal profile described in
FIGs. 13,
14, 15 and 16, and ranked and scored products described in FIG. 17. In some
examples
(not shown) a user can select to link and upload (via an API) to those third
parties (and
third party aggregators of information) her checking and credit card account
information
and transactions, her credit report, her household makeup and cars owned, and
other
accounts; this action reduces the manual entry of the user's information
required by the
matching engine. A software application would convey this information to the
appropriate section of the customer profile database 22. The consumer is shown
recommended products in a bank account category 574, a credit card category
586, and a
mortgage category 588. Categories for recommended products include bank
accounts,
credit cards, mortgages, home insurance, auto insurance, and other financial
products
(and financial products embedded into another product offer, like cell phone
financing
offered through cell phone service agreements, or financing offers for a
specific car). For
recommended product information 578 that represents a particular recommended
product,
GUI 560 displays product names and details 577 and proprietary score
information 582
that specifies the calculated proprietary score for the product. Consumers
also have the
ability to save a product to view later by selecting a save for later element
580 in the GUI
560. This would enable consumers to save multiple products to request pricing
and to
apply for the products. GUI 560 shows this arrangement for bank accounts,
credit cards,
and mortgages, but these recommendations are available for other financial
products such
as home insurance, auto insurance, and other financial products (and financial
products
embedded into another product offer, like cell phone financing offered through
cell phone
service agreements, or financing offers for a specific car).
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Referring to FIG. 20, GUI 600 displays a graphical representation 602 of a
consumer email message (hereinafter "email message 602"). This email message
602
includes header information 604 and communicates the consumer financial
product
recommendations described in FIG. 19. This message is sent when there were new
recommendations to communicate to the consumer, or periodically, as set by the
consumer. In some implementations, based on infounation provided by a mobile
application, or through other data linked to the user (e.g., a change in
shopping habits, or
change in product usage perceived by the system via linkage to the account),
the system
will determine that the current products held by the user are a mismatch,
perhaps
resulting in too-high price given the change in location or situation, totally
unbeknownst
to the user. The system perceives that and alerts the user proactively based
on inputs the
system has passively collected to help optimize the user's situation. The
email message
602 includes a message to the consumer 606 about the recommendations. These
recommendations are generated based on the information completed by the
consumer in
the personal profile described in FIG.s 13, 14, 15 and 16 and matched with
attributes of
the ranked and scored products described in FIG. 17.
A financial product is associated with various attributes, including, e.g., a
price, a
payment (a monthly payment), a product type, a geographic location, an income
requirement to qualify for the product and so forth. The body of email message
602
includes contents from GUI 560 (FIG. 19). For example, the consumer is shown
recommended products in each product category 574, 586, 588. Categories for
recommended products include bank accounts, credit cards, mortgages, home
insurance,
auto insurance, and other financial products. This figure shows this
arrangement for bank
accounts, credit cards, and mortgages, but these recommendations are available
for other
financial products such as home insurance, auto insurance, and other financial
products
(and financial products embedded into another product offer, like cell phone
financing
offered through cell phone service agreements, or financing offers for a
specific car).
Selecting any of these individual product recommendations would take the user
to the
consumer website or software application described in FIG. 19.
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Referring to FIG. 21, GUI 610 shows a listing of financial products which have
been saved for later by a consumer who has previously used this website or
software
application. GUI 610 includes saved for later tab 620 for display of products
that were
previously saved. In this example, saved for later tab 620 displays mortgage
infoiniation
628, 629. In this example, saved mortgages are shown, but other products
including bank
accounts, credit cards, home insurance, auto insurance, and other financial
products could
also be included (and financial products embedded into another product offer,
like cell
phone financing offered through cell phone service agreements, or financing
offers for a
specific car). For mortgage information 628, product name information and
other details
630 are displayed, along with a proprietary score 634 for the product.
Mortgage
information 628 includes checkbox 631, selection of which specifies that the
consumer
requests payment quotes or prequalification information for the product
represented by
mortgage information. Mortgage information 629 includes checkbox 633,
selection of
which specifies that the consumer requests payment quotes or prequalification
information for the product represented by mortgage information. GUI 610
includes
graphical representation 636 for display of information specifying a number
(if any) of
products for which a user has requested (via selection of one or more of
checkboxes 631,
633) to receive payment quotes or prequalifications. In this example, a user
has not
requested to receive payment quotes or prequalifications for any of the saved
product
information. Via control 638, consumers could choose to receive a pricing
quote from a
financial provider for the product. Via control 640, a consumer selects to
apply for the
product.
FIG. 22 shows GUI 642, which is an updated version of GUI 610. GUI 642
displays a listing of financial products, which have been saved for later by a
consumer
who has previously used a website or software application that displays GUI
642. In this
example, the consumer selects checkboxes 631, 633. The number of checkboxes
selected
is tallied and information indicative of the tallied number is displayed in
graphical
representation 636. Selection of one or more of checkboxes 631, 633 enables
the
consumer to choose to receive pricing quotes from financial providers and/or
apply for
multiple financial products by selecting the get multiple quotes button 654
and the get
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multiple prequalifications buttons 656. A prequalification includes an
application for
mortgages. GUI 641 shows saved mortgages information but this process would
also be
available for other financial products including bank accounts, credit cards,
home
insurance, auto insurance, and other financial products (and financial
products embedded
into another product offer, like cell phone financing offered through cell
phone service
agreements, or financing offers for a specific car).
Referring to FIG. 23, GUI 660 is displayed in a consumer website or a software
application. Through a process, a consumer initiates a request for a quote or
an
application to receive a financial product. This process includes single-
source quote and
application anonymous processing for multiple providers. GUI 660 includes
section 670
that displays information describing a type of action (i.e., the process for
applying or for
receiving a quote) to be performed via GUI 660. To promote completion of the
process,
GUI 660 includes personal information section 672 with necessary data fields
required
for the particular financial product for which the consumer is requesting a
price quote or
applying, labeled your info 672. These fields would first be filled
automatically for the
consumer by using the consumer's data stored in the customer profile database
22. The
consumer would then complete other fields if necessary. Some or all of this
data could
also be stored in the customer profile database 22. In some cases (not shown)
a user can
select to link and upload (via an API) to those third parties (and third party
aggregators of
information) her checking and credit card account information and
transactions, her credit
report, her household makeup and cars owned, and other accounts; this action
reduces the
manual entry of the user's information required by the matching engine. A
software
application would convey this information to the appropriate section of the
customer
profile database 22.
This example shows fields for a mortgage quote, but this process would also
apply to products such as bank accounts, credit cards, home insurance, auto
insurance,
and other financial products (and financial products embedded into another
product offer,
like cell phone financing offered through cell phone service agreements, or
financing
offers for a specific car), with the data fields changing for each product
type. In this GUI
660, personal information section 672 includes field 674 for a consumer to
indicate which
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product he/she is applying for, field 676 for the consumer to specify which
products are
of interest, field 678 to specify an amount in dollars the consumer wants to
borrow, field
680 to specify the value of the home, field 682 to specify the consumer's zip
code, field
684 for entry of the consumer's estimated credit score, control 686 for the
consumer to
specify whether or not the consumer is applying with a spouse or partner, and
field 688
for the consumer to specify the consumer's email address. GUI 660 displays a
temporary
email address 690 that is generated by a system (e.g., the recommendation
system), so
that the consumer's actual email address remains private in this system. GUI
660 displays
next control 692 for saving this entered data in the customer profile database
22, and for
notifying the financial providers that the consumer has applied for their
products.
Referring to FIG. 24, GUI 710 is rendered through a website or software
application for financial service providers. This website or software
application would
enable financial service providers to view, approve, deny, provide details,
and
communicate with consumers who have applied for price quotes and completed
applications for the financial service provider's products. Products include
bank accounts,
credit cards, mortgages, home insurance, auto insurance, and other financial
products.
GUI 710 provides login section 714 that enables a service provider to log into
a financial
service provider portal. Each financial service provider is given a unique
login identifier
and password to log in and view consumer quote requests and completed
applications for
their products. Login section 714 includes fields 716, 718 for entry of
username and
password information, respectively. Selecting login control 720 would log the
financial
service provider into the portal.
Referring to FIG. 25, GUI 730 is rendered by a website or software application
for financial service providers. This website or software application enables
financial
service providers to view, approve, deny, provide details, and communicate
with
consumers who have initiated price quote requests and completed applications
for the
financial service provider's products. Products include bank accounts, credit
cards,
mortgages, home insurance, auto insurance, and other financial products. GUI
730
includes pending quotes section 740 and pending applications section 752 that
lists
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quotes and/or applications by consumers for the products offered by the
financial service
provider who is using this website or software application.
Pending quotes section740 lists information indicative of quotes that have
been
sent to various consumers. Pending quotes section740 includes summary
information,
such as dates of quote information 744, application type infolination 742,
amounts
applied for information746 and links 748 for selection of other options, e.g.,
the ability to
view details, approve or deny a quote or application, or communicate with the
consumer
who has applied.
Pending applications section 752 lists information indicative of applications
for
the financial service provider to review. Pending applications section 740
includes
summary information, such as dates of quote information 756, application type
information 754, amounts applied for infoiination758 and links 760 for
selection of other
options, e.g., the ability to view details, approve or deny a quote or
application, or
communicate with the consumer who has applied.
Referring to FIG. 26, GUI 770 is rendered by a website or software application
for financial service provider employees. This website or software application
would
enable financial service providers to view, approve, deny, provide details,
and
communicate with consumers who have initiated price quotes requests and
completed
applications for the financial service provider's products. Products include
bank accounts,
credit cards, mortgages, home insurance, auto insurance, and other financial
products.
GUI 770 shows the details of a single consumer's request for a price quote or
completed
product application. GUI 770 includes details for a quote or application. In
this example,
GUI 770 includes quote details section 780 to display the details of a
quotation,
including, consumer user name information 782. The consumer's real name and/or
contact information would not be revealed to financial service providers until
the
consumer approved doing so. Other details 790 include the anonymous email
address,
and other details necessary for the financial provider to approve or deny a
consumer
quote application or product application. A consumer's actual email address,
phone
number, and physical address would not be provided unless approved by the
consumer.
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From here, a financial service provider could approve an application via
approval
control 786, contact the applicant via contact control 792, or decline the
application via
decline control 794. These actions would take place while keeping the real
identity of the
applicant anonymous. Although this example shows a mortgage pricing quote
example,
this process would also be developed for other financial products and
processes,
appropriate for giving price quotes and starting the application process for
bank accounts,
credit cards, mortgages, home insurance, auto insurance, and other financial
products
(and financial products embedded into another product offer, like cell phone
financing
offered through cell phone service agreements, or financing offers for a
specific car).
Referring to FIG. 27, GUI 800 displays a consumer email message. This email
message communicates the status of a consumer quote request or application
described in
FIG. 26. This message is sent when a financial provider has completed a quote
request or
has processed a financial product application. The email message is sent to a
consumer
specified by recipient information 802. The email message includes link 806,
selection of
which enables a user to view the status of a quote request. Email message also
include
body portion 804 to display the contents of the email. Although this example
shows a
mortgage pricing example, this process would also be developed for other
financial
products and processes, appropriate for communications about price quotes and
applications for bank accounts, credit cards, mortgages, home insurance, auto
insurance,
and other financial products.
Referring to FIG. 28, GUI 810 is displayed in a consumer website or software
application. GUI 810 shows the consumer the response from a financial service
provider
for a quote and financial product applications submitted by the consumer. The
consumer
could view multiple quote and application responses at once and in one simple
format,
versus responses from multiple providers, multiple formats, and multiple
calculation
methods. GUI 810 includes your quotes section 820 for display of information
indicative
of quotes that are provided to the viewer. Your quotes section 820 includes
mortgage
quote information 822, 830. Details for each quote shown here are examples,
and actual
quotes may show different data.
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In this example, mortgage quote information 822 displays mortgage name and
summary information 822, a proprietary score 828 (as computed from a scoring
and
ranking system), control 838 for selection of information specifying a purpose
of the
mortgage, the amount of the loan application information 840, information 842
specifying the monthly payment quoted by the financial product provider,
information
844 specifying the fees quoted by the financial product provider, and
information 846
specifying a calculated savings about for this product, which is the amount
the consumer
would save each month if the consumer replaced an existing product with this
one.
Mortgage quote information 822, 830 includes checkboxes 824, 825,
respectively,
selection of which enables the user to specify the products for which the
consumer
request prequalification via button 832, e.g., in order to continue the
process and apply
for multiple financial products at once ¨ independent of individually and
separately
filling out each application. Upon selection of button 832, the system
provides single
source application anonymous processing for multiple providers by: accessing,
from a
data repository, personal profile information of the consumer, generating
anonymous
consumer information by removing, from the personal profile information,
identifying
information of the consumer, and transmitting to the providers the anonymous
information for application processing. As specified by information 850, 854,
the
consumer's name, address, email and other personally identifiable information
remains
anonymous as part of this process. Although this example shows a mortgage
quote
process, this would also be the same process for pricing quotes and
applications for other
financial products, including bank accounts, credit cards, mortgages, home
insurance,
auto insurance, and other financial products (and financial products embedded
into
another product offer, like cell phone financing offered through cell phone
service
agreements, or financing offers for a specific car). In another example,
system 912
creates recommendations automatically by monitoring, e.g., at specified time
intervals. In
some implementations, based on information provided by a mobile application,
or
through other data linked to the user (e.g., a change in shopping habits, or
change in
product usage perceived by the system via linkage to the account), the system
will
determine that the current products held by the user are a mismatch, perhaps
resulting in
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too-high price given the change in location or situation, totally unbeknownst
to the user.
The system perceives that and alerts the user proactively based on inputs the
system has
passively collected to help optimize the user's situation.
Referring to FIG. 29, networked environment 900 includes network 902, client
devices 904, 908, system 912 and data repository 914. In this example, client
device 904
is associated with a user who is a financial service provider 906. For
example, financial
service provider 906 uses client device 904 to access the financial service
provider portal
and to view applications. Client device 908 is associated with user 910, e.g.,
a consumer.
In this example, user 910 uses client device 908 to view recommendations of
financial
products that are specific to the consumer, to request quotations of financial
products, to
view quotations of financial products, to request applications for financial
products, to
view applications for financial products, to apply for financial products, and
so forth.
System 912 is a system for generating recommendations of financial products
and
for implementing the techniques and operations described herein. For example,
system
912 includes the various systems described herein, e.g., financial product
curation and
scoring system 70, the recommendation system 126 and other systems described
herein.
The various systems included in system 912 may be implemented as an engine.
For
example, recommendation system 126 may be implemented as a recommendation
engine
within system 912. In this example, system 912 generates the provider portal
and
transmits information to client device 904 to promote approval of application
requests
and quotations. System 912 also transmits to client device 908 various types
of
information, including, e.g., information indicative of a recommended
financial product,
a score (e.g., a proprietary score) for the financial product, approval of
various financial
products, specialized offers for financial products, and so forth.
Database 914 includes the various databases that are described herein,
including,
e.g., customer profile database 22, curated and scored financial product
information
database 82, and so forth. In an example, the contents of customer profile
database 22 and
curated and scored financial product information database 82 are integrated
into a single
database and are included in database 914.
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Referring to FIG. 30, client devices 904, 908 can be any sort of computing
devices capable of taking input from a user and communicating over network 902
with
system 912 and/or with other client devices. For example, client devices 904,
908 can be
mobile devices, desktop computers, laptops, cell phones, personal digital
assistants
("PDAs"), iPhone, smart phones, iPads, servers, embedded computing systems,
and so
forth.
System 912 can be any of a variety of computing devices capable of receiving
data, such as a server, a distributed computing system, a desktop computer, a
laptop, a
cell phone, a rack-mounted server, and so forth. System 912 may be a single
server or a
group of servers that are at a same location or at different locations. The
illustrated
system 912 can receive data from client devices 904, 908 via input/output
(I/O')
interface 920. I/O interface 920 can be any type of interface capable of
receiving data
over a network, such as an Ethernet interface, a wireless networking
interface, a fiber-
optic networking interface, a modem, and so forth.
System 912 includes memory 924. a bus system 922, and a processing device 926.
Memory 924 can include a hard drive and a random access memory storage device,
such
as a dynamic random access memory, machine-readable media, or other types of
non-
transitory machine-readable storage devices. A bus system 922, including, for
example, a
data bus and a motherboard, can be used to establish and to control data
communication
between the components of system 912. Processing device 926 may include one or
more
microprocessors and/or processing devices. Generally, processing device 926
may
include any appropriate processor and/or logic that is capable of receiving
and storing
data, and of communicating over a network (not shown).
Referring to FIG. 31, system 912 implements process 950 in recommending a
financial product to a consumer. In operation, system 912 compares (952)
personal
profile information of a consumer to financial product information for
financial products.
The personal profile information includes information indicative of one or
more
preferences of the consumer, e.g., pricing preferences that specify a desired
price of the
financial product, payment preferences that specify an amount of a monthly
payment the
consumer can afford, geographic preferences that specify a desired geographic
location of
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a financial service provider, and so forth. The financial product information
includes
information indicative of attributes of the financial products, e.g., a price
attribute, a
payment amount attribute, a geographic location attribute, and so forth.
System 912 identifies (954), based on the comparing, one of the financial
products with a higher relevance to the consumer relative to relevances of
others of the
financial products. In an example, relevance is measured by a number of
matched
between preferences and attributes. System 912 also generates (956), based on
the
identified financial product, a financial product recommendation specifically
for the
consumer.
Referring to FIG. 32, system 912 implements process 960 in generating a
recommendation for a financial product. In operation, system 912 determines
(962) a
match between at least one of the one or more preferences and an attribute of
one of the
financial products. System 912 assigns (964) the one of the financial products
to a group
of candidate financial products that are candidates for recommendation to the
consumer.
System 912 also applies (966) a filter to attributes of the candidate
financial products in
the group, with the filter specifying one or more requirements for a financial
product to
be recommended to the consumer. System 912 removes (968), based on application
of
the filter, a candidate financial product from the group, with at least one of
the one or
more requirements being unsatisfied by an attribute of the removed candidate
financial
product. System 912 identifies (970), from the remaining candidate financial
products in
the group, a candidate financial product with a greater relevance to the
consumer, relative
to relevances of others of the candidate financial products.
Referring to FIG. 33, system 912 implements process 980 in performing single-
source application and/or anonymous processing for multiple service providers.
In
operation, system 912 receives (982) a request for single-source
application/quote
anonymous processing for multiple providers. In response to the request,
system 912
accesses (984), from a database 914, personal profile information of the
consumer.
System 912 anonymizes (986) at least a portion of the personal profile
information by
removing, from the personal profile information, identifying infoiniation of
the
consumer. System 912 transmits (988) to the providers the anonymous
information for
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application and/or quote processing. In response, system 912 receives (not
shown), from
devices associated with the multiple providers, information indicative of
results of the
application and/or quotation processing. Using this received information,
system 912
transmits (990), to a client device of the consumer, information indicative of
an outcome
of the single source processing. In an example where the single source
processing is
single source application processing, the outcome includes an outcome of the
application
processing, e.g., approval and/or denial of the application. In an example
where the single
source processing is single source quotation processing, the outcome includes
an outcome
of the quotation processing, e.g., price quotes for various financial products
of the
financial service providers.
Referring to FIG. 34, system 912 performs process 1000 in enabling a financial
service provider to interact with a provider portal. In operation, system 912
receives
(1002), from a client device (e.g., client device 904), a request to access a
financial
provider portal. The request includes login credentials, e.g., a user name and
a password.
Using the received login credentials, system 912 confirms (1004) that the
financial
service provider is authorized to access the portal. In response to
confiiiiiing, system 912
grants (1006) the service providers with access to the portal and with access
to
infoiniation within the portal that the service provider is authorized to view
(e.g.,
applications for products of the service provider).
In this example, system 912 receives (1008), through the financial provider
portal
and from the financial service provider, information that is responsive to one
or more
consumer related requests. The responsive information may include information
indicative of approval or denial of an application, information representing a
price
quotation, and so forth. System 912 transmits (1010), to a client device
(e.g., client device
908) information indicative of the response.
Referring to FIG. 35, system 912 implements process 1020 in curating financial
products. Through a curation process, financial products are narrowed from
thousands of
financial products (e.g., by assessing whether a financial service provider
meets baseline
performance hurdles) to a few financial products that are dynamically matched
to a user
and discretely ranked to form a recommendation. If system 912 determines that
a
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financial service provider does not meet baseline performance hurdles, then
system 912
ceases evaluation of that service provider. As shown in FIG. 35, system 912
curates
financial products by evaluating the financial product and/or provider along
criteria 1022-
1030. If a financial product and/or provider fails to satisfy one of the
criteria, system 912
cease the curation process for that product.
In an example, system 912 implements a series of operations for the curation
process, as shown in the below Table 1A.
Sequence Steps Data Sources
Funnel (number of providers)
Step 0 Define the specific mortgage options
available MBA 1000+
.Step 1 Secure the entire universe of mortgages
available MBA 300+
Step 2 Screen for legitimate providers MBA
50+
Step 3 Screen for geographic location MBA
data (down to the county level) 40+
Step 4 Score for pricing / value Company
Websites/Mystery Shopping 10-20
Step 5 Score application and closing process
Company Websites/Mystery Shopping/MBA data 5-15
Step 6 Score reputation attributes of provider
Mystery Shopping, Social Media, CFPB, BOB, Fl sites 5-15
Step 7 Calculate the total Cinch score to
determine Cinch Picks Cinch Scores 5-10
Step 3 Screen in for exceptions missed in the
data and/or computation Cinch Research 5+
Step 9 Build links to Cinch formulas to
calculate customer specific pricing Company Websites/Mystery Shopping 1-
3
Table lA
As described in the above Table 1A, system 912 implements steps 0-9 in
curating
financial products and providers. System 912 obtains data from various
sources,
including, e.g., mortgage banker's association (MBA) and the public filings
required by
the Home Mortgage Disclosure Act (HMDA). As shown above, the number of
providers
is narrowed or funneled from 1000+ providers to one or three providers, which
are then
scored. In this example, system 912 evaluates a financial provider based on
the criteria
specified in the various steps. For example, the scoring of the closing
process is
performed by system 912 through analysis of the closing attributes shown in
the below
Table 3. System 912 also evaluates other of the curation policies shown in
Tables 1, 2
based on the attributes shown in the below Table 3.
In an improved example, system 912 implements the series of operations for the
curation process shown in Table 1B, below:
Sequence Steps Data Sources Funnel (number of
providers)
Step 0 Identify the inventory of mortgage providers HMDA/Metssa
Data/Factset/FREC 7000+
Step 1 Screen out non-retail and specialty lenders liMDA/Melissa
Data/Factset/FFlEC 5000+
Step 2 Screen for legitimate providers HMDA/Melissa
Data/Factset/FREC 2000+
Step 3 Filter by product type recommendation HMDA/Mellissa
Data/Factset/FFIEC 1000+
Step 4 Filter by geographic relevance (down to the town level)
HMDA/Melissa Data/Factset/FFFEC 100+
Step 5 Filter by consumer mortgage preferences Company
Websites/Cinch Research 50+
Step 6 Rank (score) based on lender performance HMDA/Melissa
Data/Factset/FFI EC/Cinch Research 20+
Step 7 Rank (score) reputation Company Websites/Cinch Research 20+
Step 8 Rank (scare) business practices Company Websites/Cinch Research
Step 9 Screen in exceptions missed in the data and/or computation Cinch
Research 10+
Step 10 Recommend lenders or
expand geographic area and re run engine. Cinch sconng rubric 1-3
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Table 1B
As described in the above Table 1B, system 912 implements steps 0- 10 in
curating financial products and providers. System 912 obtains data from
various sources,
including, e.g., the public filings required by the Home Mortgage Disclosure
Act
(HMDA), third-party providers, and proprietary research. As shown above, the
number of
providers is narrowed or funneled from 7000 + providers to one or three
providers, which
are recommended based on their dynamic matching and discrete ranking (i.e.,
score). In
this example, system 912 evaluates a financial provider based on the criteria
specified in
the various steps. For example, the scoring of the provider's performance is
performed by
system 912 through analysis of the geo-located performance attributes shown in
the
below Table 3. System 912 also evaluates other of the curation policies shown
in Tables
1, 2 based on the attributes shown in the below Table 3.
As shown in the below Table 2A, system 912 implements various curation
screening and scoring policies for a financial product, e.g., a mortgage.
Curation Step Policy
Product Segments 2 products by Transaction type: Purchase and Refinance
Product Segments 3 products by Transaction amount: Conforming, Jumbo, FHA
Product Segments 3 .roducts b Product ,.e: 30 r. fixed, 15 r. fixed, 5/1
ARM
Providers lyrovider score will be unique by state due to value differences
at the state level
Providers Must right in the covered DMA's
Weighting Scores weight each segment equally
Weighting Scores attributes weighted based on relative importance
Weighting Scores Attributes where sentiment cant be derived receive a score
of 75
Legitimacy Scale at least 50bps market share
Legitimacy Close Rate above average
Legitimacy Denial Rate above average
Legitimacy Exclude correspondent and broker, i.e. focus only on retail
lenders
Geographic Coverage Per research and if unknown assume county of incorporation
for local providers
Value Assume <SO% LTV and adjust with fit score and screens for providers
that don't serve low DP consumers
Value Assume good credit (720) and adjust fit score accordingly for lower
credit
Value Use average loan amounts for conforming and jumbo to calculate fees
and expected pricing for score-5201,000 and $75,000
Value Cinch includes lender and third party fees- they have the same
impact on the consumer
Value Compare 0% or lowest point mortgages for each provider
Value Include cost of points if applicable as a component of the fee
Value Include all provider and third party fees
Value Amortize fees over at year time horizon to match average mortgage
duration
TABLE 2A
As shown in the above Table 2A, system 912 curates (e.g., categories) the
various
products by various critiera, e.g., product segments, providers, weighting
scores,
legitimacy, geographic coverage and value. For example, system 912 groups into
a
product segment various types of products, e.g., products with a similar
transaction
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=
amount or products with a denial rate about average, and so forth. That is,
system 912
analyzes the attributes of the various financial products and curates them
into various
group based on similiarities among the products. These curations are then used
in
answering the questions as shown in the above Table 1. In this example, the
curations
promote categorization of the various financial product and assist system 912
in
answering the questions shown in the above Table 1.
As shown in the above Table 2A, system 912 determines values based on various
policies. In this example, if a financial product fails to satisfy one of the
value policies,
system 912 determines that the financial product is not a good value. System
912
performs similar operations for the other curation policies.
An improved version of the process shown in Table 2A is set forth in Table 2B
below.
Curation Step Policy
Screening Include only direct retail lenders that market
directly to consumers
Screening Include niche market providers and disrupters with
compelling value that have at least 2 years of business history
Screening Require baseline scale, denial, growth and
abandonment hurdles across at least one @ASA
Screening Exclude lenders based on regulatory and compliance
thresholds
Scoring Rubric- Dynamic Matching 2 by Transaction Type: Purchase and Refinance
Scoring Rubric- Dynamic Matching 2 by Transaction Amount: Conforming and Jumbo
Scoring Rubric- Dynamic Matching Lenders are included/excluded bascd on a
user's application preferences
Scoring Rubric- Dynamic Matching Lenders are included/excluded based on a
user's company preferences
Scoring Rubric- Discrete Ranking Relative ranking is used to determine
which if the lenders that made it through filtering will be shown
Scoring Rubric- Discrete Ranking Both quantitative and qualitative
attributes are included in the discrete ranking based on expert review
Scoring Rubric- Discrete Ranking Relocated performance constitutes 50% of
the ranking
Scoring Rubric- Discrete Ranking Lender reputation constitutes 30% of the
ranking rubric
Scoring Rubric- Discrete Ranking Business practices constitutes 2D% of the
ranking rubric
Scoring Rubric- Discrete Ranking Optimized to user's demographic scenario
(e.g. income)
Scoring Rubric- Discrete Ranking Lenders with scores below SO are
automatically excluded from the final results
Scoring Rubric- Discrete Ranking Only 3 lenders are ultimately recommended
Scaring Rubric- Discrete Ranking If no lenders score >75 in given geography-
region is expanded to county and or fleISA level
Scoring Rubric- Discrete Ranking Data is refreshed on a weekly, monthly,
quarterly and annual basis as available
Scaring Rubric- Discrete Ranking If historical data is incorporated the
look back period is no longer than 4 year
Recommendation Start at the most granular level, but dynamically
increase analysis location to drive more accurate results
TABLE 2A
As shown in the below Table 3, system 912 implements the below shown scoring
rubic, which is stored in a data repository.
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Mortgage Scoring Rubric
Weight
.Value 25%
lApplication 25%
1Closing 25%
Reputation 25%
Segment Attribute Weight Attribute Type Data Source
Application Easy to get started online with a simple
website ' 30% Company Cinch research
Easy to get started over the phone 20% Company cinch
research/mystery shopping
Simple application form and documentation checklist 20% Company Cinch
research
Dedicated point of contact (phone/email) 15% Company Cinch
research/mystery shopping
Local Branches or offices 15% Company Cinch research
Closing Close Rate 30% Segment MBA data
Closing Casts are easy to understand, transparent and competitive 20%
Company Cinch research/mystery shopping
Offers multiple dosing cost and point options 20% Company Cinch
research/mystery shopping
Denial Rate 20% Segment MBA data
Percentage of loan assets retained 10% Segment MBA data
Reputation Customer churn trend 40%
Company MBA data
Social Media Profile 40% Company Cinch Research
JD Power 10% Company Cinch Research
CFPB 10% Company JD Power and CFPB
data
'Value Pricing- Interest Rate 50% Product Cinch
research/mystery shopping
Lender Closing Costs 30% Product Cinch
research/mystery shopping
3rd Party Closing Costs 10% Product Cinch
research/mystery shopping
Table 3A
As shown in the above Table 3A, system 912 generates a score for a financial
product by evaluating the financial product in four segments, e.g., an
application
segment, a closing segment, a reputation segment and a value segment. In
calculating the
final score, system 912 assigns weight to each of the segments, as shown in
Table 3.
Additionally, system 912 evaluates each segment by various attributes and
individually
weights each attribute within a segment. For example, system 912 evalautes the
application among various attributes, including, e.g., an "easy to get started
online with a
simply website" attribute and other attributes as shown in Table 3. Each
attribute is
associated with an attribute type, e. .g, company, segment, product. There are
various data
sources for an attribute, including, e.g., internal research (i.e., Cinch
research) and
external data sources, including, e.g., MBA data. In this example, system 912
only
generates scores for particular financial products, e.g., financial products
that pass one or
more of the criteria of the curation process.
An improved version of the process shown in Table 3A is set forth in Table 3B
below:
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Mortgage Scoring Rubric
Attribute Type Segment Weight
Dynamic Matching Product Dynamic by User Type
Geoiocation Dynamic by User Type
Consumer Behavior Dynamic by User Tyne
Consumer Preferences Dynamic by User Type
Discrete Ranking Gelocated Performance 50%
Reputation 30%
Business Practices 20%
Attribute Type Segment Attribute Weight in Segment
Data Source
Dynamic Matching Product Transaction Type Dynamic by
User Type HMDA/Melissa Data/Factset/FFIEC
Transaction Amount Dynamic by User Type HMDA/Melissa
Data/Factset/FFIEC
Geoiocation Town Dynamic by User Type HMDA/Meiissa
Data/Factset/FFIEC
Consumer Behavior Application options Dynamic by User Type
Cinch Research
Mobile application Dynamic by User Type Cinch
Research
Branch vs. online Dynamic. by User Type Cinch
Research
Consumer Preferences Other products Dynamic by User Type Cinch
Research
Lender type Dynamic by User Type Cinch
Research/HMDA
Membership requirements Dynamic by User Type Cinch
Research
Loan servicing Dynamic by User Type HMDA/Melissa
Data/Factset/FFIEC
Product Breadth Dynamic by User Type HMDA/Melissa
Data/Factset/FFIEC
Discrete Ranking Geolocated Performance Origination growth
40% HMDA/Melissa Data/FactsetiFFIEC
Denial rate 30% HMDA/Melissa
Data/Factset/FFIEC
Abandonment rate 15% HMDA/Meassa
Data/Factset/FREC
Scale 15% HMDA/Melissa
Data/Factset/FFIEC
Reputation Cinch- insider reviews 50% Factset
Compliance history 33% Factset
Consumer reviews 8% Aggregated Data
Complaints B% CFP13/Aggregated Data
Business Practices Online functionality 50% Cinch Research
Phone/Branch accessibility 50% Cinch Research
Table 3B
As shown in the above Table 3B, system 912 generates a score for a financial
product by evaluating the financial product in seven only count six?]
segments, e.g., a
product segment, a geolocation segment, a consumer preference segment, a geo-
located
perfounance segment, a reputation segment, and a business practices segment.
In making
a recommendation, each financial product or provider or both is included or
excluded
using a dynamic matching logic and then filtered products or providers or both
are ranked
by calculating the final score. System 912 assigns a score based on the weight
of each
discrete ranking attribute, as shown in Table 3. Additionally, system 912
evaluates each
segment by various attributes and individually weights each attribute within a
segment.
For example, system 912 evaluates the reputation among various attributes,
including,
e.g., a "complaints" attribute and other attributes as shown in Table 3. Each
attribute is
associated with an attribute type, e. .g, geo-located, performance,
reputation, business
practices. There are various data sources for an attribute, including, e.g.,
internal research
(i.e., Cinch research) and external data sources, including, e.g., HMDA data.
In this
example, system 912 only generates scores for particular financial products,
e.g.,
financial products that pass one or more of the criteria of the curation
process.
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As shown in the below Table 4A, each attribute is associated with a score
range
and requirement.
' Segment Attribute Weight Score Range Requirement
'Reputation Customer churn trend 0.4 95 Increasing
market share
75 Flat'YOY
50 Decreasing Market Share
Social Media Pr Ale 0.4 95 Strong positive
75 Average/No sentiment
50 -Strong Negative sentiment
LID Power 0.1 95 Best in class
85 above average
75 AVergage!not coveeed
50 below average
CFPB 0.1 95 top 5 provider by complaints
85 above average
75 average/not included
50 below average
Application Easy0 get started online w oha simple website
03 95+ Mortgage rate estimator, easy to naviate ebsite, prequalification,
pre-approval options, easily evaluate loan and closing cost option
85 Mortgage Rate estimator, pre-qualification, pre-approval options,
explanation of the process, evaluate different loan options
75 Mortgage rate estimator, nine pre-approval, eHplanation of the process
50 Online application, eao 0 find contact number
50 Online applIcation
40 Contact us only- no online engagement
Easy to get started over the phone 02 95 [Went to mortgage loan officer
75 IVR leading to rnortage loan officer
50 IVR leading to inbound customer support queue
Simple application I orm and documentation checklist 0.2 95+
Online/Mobile App that allow s for transfer of documents
85 Easy to understand checklist that users can print out to prep f or the
application process
75 kiebpage with list that isn't in a checklist format
Dedicated point of contact (phone/email) 015 85 ,Dedicated Contact
65 Serviced by multi member team
Local Branches or offices 0.15 95 Branches throughout state
80 Branches in major metro areas
50 No Branches In the state
Closing Process Close Flare 03 95 top 5 provider
85 providers 6-15
75 providers 15+
Closing Costs ere easy to understand, transparent and competitive 02 950
Lists all the lender and 3rd party fees
80 Lists all the lender fees and some of the 3rd party f ees
60 Lists the lender f ees
50 Lists no fees
Offers multiple closing cost and point options 02 950 Includes No
Points/No Closing cost options in addition to other corribinakons
85 Multiple options for paying Olrint paying po,crsc th increasing credits f
or dif I erent rate tiers
50 Includes manditory origination fee even if points are eliminated
Denial Rate 02 95 top 5 provider
55 providers 6-15
75 providers 15+
Percentage of loan assets retained al loo 1000'. retention
85 60500
75 500-80%
65 <50%
Value Pricing- Interest Rate 06 0-100 Forced Rank within the
competitive set
Lender Closing Costs 03 0-100 Forced Rank w ithin the competitive
set
3.1Party Closing Costs 01 0-100 Forced Rank w ithin the competitive
set
Total Score
Table 4A
As shown in the above Table 4A, for the reputation segment, system 912
determines whether a service provider has an increasing market share (in which
case the
service provider is assiging a 95 for score range), and so forth. In this
example, system
912 determines a reputation score (e.g., a weighted reputation score) for each
reputation
attribute, in accordance with the below formula:
Reputation score = (wi) (customer churn trend score) +(w2) (social media
profile
score) + (w3) (JDPower score) +(w4) (CFPB score), where w is a weighted value.
System 912 uses similar techniques in computing a value score, a closing
process
score and an application score. System 912 then calculates a propriety score
in accorance
with the below equation:
Proprietary score = (wi) (reputation score) +(w2) (value score) + (w3)
(closing
process score) +(w4) (application score), where w is a weighted value.
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As shown above, the proprietary score is based on an aggregate of a reputation
score, a value score, a closing process score and an application score. In
other
implementations, the proprietary score is based on application one or more
various
mathematical operations to a reputation score, a value score, a closing
process score and
an application score. Also, in this example, the proprietary score is based on
a reputation
score, a value score, a closing process score and an application score. In
other examples,
the proprietary score is based on other types of scores that are based on
other types of
criteria, segments and attributes.
An improved version of Table 4A is shown below in Table 4B.
Segment Attribute Weight Score Range Requirement
Geolocated Performance Origination growth 20% 109 Fastest growth in
market f county and/or MSA)
75 Increasing market share
50 Flat markets share
25 Shrinking market share
Denial rate 15% 100 Lowest denial rate in
local market (town and/or county)
75 Top quartile denial rate
50 Average denial rate
25 Below average denial rate
Abandonment rate 19% 190 Lowest denial rate in
local market )town and/or county)
75 Top quartile denial rate
50 Average denial rate
25 Below average denial rate
Scale S% 100 Lowest denial rate in
local market flown and/or county)
75 Top quartile denial rate
50 Average denial rate
25 Below average denial rate
Reputation Compliance history 10% 100 No violations or
lawsuits to originations
75 Low ratio of violations or lawsuits to originations
50 Average ratio of violations and lawsuits to originations
25 High ratio of violations and lawsuits to originations
Consumer reviews 5% 100 Highest ratio of positive
reviews to originations
75 Above average ratio of positive reviews to originations
50 Average ratio of positive reviews to originations
25 Below average ratio of positive reviews to originations
Complaints 5% 100 Highest ratio of
complaints to originations
75 Above average ratio of complaints to originations
59 Average ratio of complaints to originations
25 Below average ratio of complaints to originations
Cinch- insider reviews 10% 100 Top lender ranking within
the relevant PISA
75 Strong lender ranking within the relevant MSA
50 Average lender ranking within the relevant MSA
25 Weak lender ranking within the relevant MSA
Business Practices Online functionality 19%, 100, Highest quality
DX, pre-approval process and application functionality
75 Simple preapproval process online
59 Includes Mine preapp royal and application functionality
25 No online preapprova i and/or application
Phone/Branch accessibility 10% 100 Large branch coverage
within the MSA and/or 24/7 phone support
75 Above average branch coverage within the MSA and/or extended hours phone
support
50 Average branch coverage and phone support
25 Below average branch coverage and phone support
Table 4B
The description of Table 4B is similar to the description for Table 4A except
that
the reputation score is defined as
Reputation score = (wi) (compliance history) +(w2) (consumer reviews) + (w3)
(complaints) +(w4) (Cinch- insider reviews), where w is a weighted value,
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and the proprietary score is defined as
Proprietary score = (wi) (geolocated performance) +(vv2) (reputation) + (w3)
(business practices), where w is a weighted value.
As shown in the below Table 5A, once a score (e.g., a proprietary score) is
calculated, system 912 uses the data in the below Table 5A to evaluate the
financial
product, e.g., relative to other finanial products.
'Scoring Criteria
90-100 In the top 10
80-90 Better than averagE,
70-80 Average
60-70 Below Average
<60 In the bottom quartile
I Segments weighted equally
Attributes within each segment weighted by relative importance
Table 5A
For example, a score 90-100 indicates that the financial product is in the top
10.
Depending on the user's specific needs, preferences, and situation, these
provider and
product ratings may have no, some, or significant impact to the matching
system. For
example, a provider may be highly rated because it offers face-to-face
services in branch
locations, but that high rating is only of relevance to those users that value
face-to-face
service interactions. The matching and recommendation system renders and
displays to
the user those idiosyncratic selection factors based on her preferences and
situation as
further described below.
An improved version of Table 5A is shown below as Table 5B:
Scoring Criteria
75400 Eligible for inclusion as top recommendatlon
50-75 Eligible for inclusion in recommendations
<50 Excluded from recommendations
Table 5B
The description of Table 5B is similar to the description of Table 5A except
that,
for example, a score 75-100 indicates that the financial product is eligible
for inclusion as
a top recommendation.
As shown in figure 36, in some implementations of the system that we are
describing here, the financial product information and pricing database 1120
can include
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information that is provided from multiple product data sources 1100. The
sources can
include market surveys 1102, wholesale pricing inputs 1104, proprietary
research 1106,
purchased data 1108, and other product data 1110, among others. The database
1120 can
include tables that capture a product type 1122, product names 1124, product
subtypes
1126, product geography 1128, product features 1130, product pricing 1132,
current
product pricing 1134 in past product pricing 1136, fees associated with the
product 1140,
current fees A 1142, current fees B 1144, past fees A 1146, and past fees B
1148.
Also, as shown in figure 37, in some implementations of the system they we are
describing here, the information in the customer profile database 1164 can be
obtained
from multiple customer profile data sources 1150, including external customer
credit
reports 1152, external customer financial transactions 1154, customer product
needs
1156, customer preferences 1158, currently held product data 1160, and other
customer
profile data 1162.
A wide variety of systems, features, and applications can be provided based on
combined uses of the two databases, the financial product information and
pricing
database 1120 in the customer profile database 1164.
For example, as shown in figure 38, a fair price algorithm 1200 can be applied
to
the two databases to produce fair price outputs 1202 that can be displayed to
users.
In some implementations the fair price (which we sometimes call a putative
price)
represents an estimate of pricing of the financial product (for example, the
interest rate
for a mortgage) and fees an estimate of fees for completing a transaction
associated with
the financial product (for example, origination fees, documentation fees, and
other fixed
and variable fees required to secure a financial product or loan). This is an
estimate of
pricing and fees that a specific anonymous user (we sometimes refer to the
user as a
consumer) in a highly competitive marketplace would pay for the specific
financial
product based on the user's specific situation. The user demand and demand
specific
situation may include identifications of current financial products held by
the user, credit
characteristics of the user, and certain anoilymized user characteristics and
needs. The
fair price establishes the optimum (for example, the likely lowest) pricing
and fees (or
range of pricing and fees) for a product given specific inputs. The fair price
is intended to
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help users negotiate and secure the best possible terms in a transaction to
acquire the
financial product, such as a loan, bank account, credit card, mortgage, cell
phone plan, or
other monthly bill.
The fair price can be rendered on a one-time basis, or on a periodic basis
with the
user alerted periodically to trigger a purchase alert that may affect the
user's decision to
engage in a transaction with respect to a particular financial product.
In some implementations, to generate a fair price, comprehensive pricing
inputs
are obtained by first creating a dynamic data set comprising a host of
sources, including
market surveys, purchased data, wholesale pricing inputs, proprietary research
data, each
of which may in change on a continuous basis and are monitored by an automated
and
manual system that takes the inputs and compares those values over a period of
time to
the new values resulting from a changing financial environment.
Among the multiple product data sources 1100, market surveys include pricing
trends that are collected from providers of financial products. Purchased data
include
reports that describe components factoring into a final price, such as fees
charged by
providers. Wholesale pricing inputs include prevailing interest rates for
specific risk and
product types. Proprietary research data include quantitative reports based on
data
collection and interviews with providers.
Among the data derived from the multiple customer profile data sources 1150,
are
current products that the user already owns 1180. These current products may
demonstrate preferences or needs related to the product currently needed. Data
about
demographic characteristics and needs may also be used. In some
implementations, these
two datasets 1120 and 1164 are then processed using software algorithms 1200
to render
the fair price for particular products. Each fair price is rendered especially
for a particular
user, for the specific situation and need being experienced by the user at
that time, and
taking account of external pricing input factors such as the cost of money to
lenders, the
general risk environment, and the product type.
The pricing inputs used by the algorithm are delivered into the datasets using
APIs, episodic direct file transfers, software bridges, and manual inputs into
a portal. The
data sets are updated on an automatic and manual basis.
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The user profile represented by the customer profile database schema 1164 is
comprised of some or all of the following five sources: 1) credit bureau data
received
through API links; 2) a user's answers to questions regarding, for example,
underwriting
criteria that are entered through a user interface; 3) if applicable,
information about the
user's product and personal preferences; 4) other enhancement data from third
parties
concerning the user's financial condition and preferences received through API
links, and
5) for users of mobile phones, location-based and other relevant data for,
e.g., the
underwriting inputs describing physical location. This information is stored
as part of the
unique user profile in the database 1164. A series of algorithms then
transfomis these
inputs into pricing factors for the product.
The algorithm takes each of these input factors described above for the user
and
creates a numerical value, or score, that is a mathematical expression of the
input factor.
A weighting is applied to the factor to establish relationships among the
factors so that
minor inputs can be distinguished from major inputs affecting the expected
interest rates
and fees for a financial product for a particular user. These factor
weightings can change
over time and vary with the situation at hand. In this way input factors are
combined and
weighted to generate an aggregate score for that user in that specific
situation for that
specific product as reflected by the profile inputs. This aggregate user
score, a numerical
value generated by the process described above, is then used to select
interest rate pricing
and fees (described above) from a table representing the most advantageous
pricing
offered in the marketplace for customers who belong to that specific profile
type. E
In some implementations of the system, machine learning software is used to
automatically first cluster, then group, consumer profiles and financial
products. Then
human experts match those consumer profiles to the specific financial products
(without
the creation of a decision tree algorithm, for example) through a user
interface to the
machine learning software. This process is used to aid, and may be combined
with or
replace, the decision tree algorithm development for matching the financial
products to
the customer profiles to reach an optimal financial product for a given
consumer profile.
Software then renders the output of the algorithm or the machine learning
process
into a visual design as shown in figure 45 includes information 1260. The
visual design
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can be transmitted to a computer, tablet, or smartphone screen, shared with
other
individuals using links to email, texting, and social media, as well as
printed. The system
can be programmed to re-run user profiles against financial products on a
periodic basis
to detect pricing changes of relevance to the user. An alert is automatically
triggered to
the user based on his or her communication preferences through email, text,
push
notification, or phone call.
As shown in figure 40, in some implementations, a feature that we sometimes
refer to as an automated financial diagnostic system 1219 analyzes a user's
large monthly
bills (e.g., mortgage, credit card, banking, lending, cell phone bills, or
cable/internet) and
provides an initial analysis of which financial products might be poorly
priced or not
aligned with the user's needs. The result of the analysis provides a starting
point and
prioritization of those financial products that require action to ensure they
are optimal for
the user. The analysis is intended as a simple scan of a user's largest bills
to further
engage the user in optimizing those financial products, and saving money. The
output of
the process is a snapshot report to the user delivered via email, text, push
notification or
other messaging service.
To support the analysis in the generation of the snapshot, the system obtains
the
user's transaction history for his or her primary and secondary payment
accounts (e.g.,
checking account, credit card, etc.). The transaction history data are
uploaded into the
system. On a real-time basis, that is, as current data is stored, the system
identifies the
large monthly and other bills that are contained in the larger body of data
and that
correspond to the mortgage payment, credit card payment, banking, lending,
cell phone,
cable/internet, and other large bills of the user. The system generates a list
of the accounts
and the providers of the financial products to the user to confirm with the
user that these
indeed are the providers of these particular services.
Once confirmed by the user, the system creates a current profile of the user
with
respect to those types of services and the large bills paid to those
providers. The current
profile is then compared to other aggregate, model profiles that are generated
on an
ongoing basis to represent typical large payments made to providers of these
types by a
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population of users. These aggregate profiles are used to compare, or
benchmark, the
amounts paid by the user for the products provided by those types of
providers.
For example, if the system reveals that the user is paying $500 for her cell
phone
plan, and similarly situated users are only paying $200 (as demonstrated by
the model
profiles), the cell phone service will be flagged for the user as a priority
item for further
analysis. Once analyzed by the system, the user is provided a snapshot report
detailing
these interim findings with respect to all of the relevant large bills and
providers, and
suggesting action to address those items.
To perform the analysis and generate the snapshot, the system starts by
identifying the user in a secure environment and requesting from the user the
identification information for the primary and secondary transaction accounts.
This account identification information is transmitted through a secure
software
linkage to a third party bill aggregator. The aggregator receives the secure
request and
provides the transaction information from the accounts to the system after the
user has
provided his or her credentials. A software matching process operated by our
system
identifies the likely product providers (based on names or in other ways) from
all of the
transaction data and conveys that to its personal profile of the user in the
database of the
system for storage and analysis. A software algorithm 1220 then matches the
user's
transaction profile (that is, the portion of the user's personal profile that
represents the
stored transactions from the aggregator) to an appropriate corresponding
aggregate model
profiles that have been stored in a comparison database of the system to
identify a best-
match similar aggregate model profile. The matching to identify the best
aggregate model
profile is done by comparing demographic and other attributes of the user with
demographic and other attributes of populations of users represented by the
aggregate
model profiles.
Over time, the system creates additional aggregate model profiles and fine-
tunes
existing aggregate model profiles based on its experience with users using
machine
learning techniques based on the spending behaviors, locations, situations,
and other
attributes of populations of users gleaned from the transaction accounts of
the users. As
shown in figure 41, the system uses a machine learning clustering process 1224
to cluster
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populations of users based on their attributes and to form aggregate model
profiles for the
respective clusters.
The machine learning techniques use specialized software to become more
accurate and refined at identifying the appropriate matching aggregate model
profile, and
can be further trained, or "tuned" 1230 by expert human operators 1228 to
discern more
sensitively the matches between the model profiles and user attributes.
Once a best-match aggregate model profile is identified, it is then compared
by an
automated matching system to the user's transaction profile, and the financial
product
configurations (e.g., the terms and features of the products) and pricing of
the user's
transaction profile in the best-match aggregate model profile are compared. A
meaningful
difference that is identified in the provider or the product pricing for the
individual user
compared to the best-match aggregate model profile is then flagged in the user
profile
database.
The system then uses software to generate the output 1230 of the matching
algorithm and machine learning system as a visual design that can then be
rendered on a
computer, tablet, or smartphone screen, shared with other individuals through
links to
email, texting, and social media, or printed. 0 an example of an online
presentation 1262
of the output 1230 is shown on figure 46.
The system can be programmed to repeat the analysis and snapshot generation
process based on current user inputs, transaction histories, and other
information. The
repetition can be done on a periodic basis to detect pricing changes of
relevance to the
user represented by updated aggregate model profiles; and an alert can be
automatically
triggered to the user based on his or her communication preferences through
email, text,
push notification or phone call.
In some implementations, the system provides what we refer to as a virtual
underwriter function. The virtual underwriter enables a user to simulate
portions of an
application for a financial product that requires underwriting and to get a
virtual approval
report back indicating whether the application would be underwritten. The
process is
arranged so that the submission of the virtual approval application will not
negatively
affect the user's credit score, or risk a "real" credit denial (which would be
logged at the
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financial institution and on the user's credit report and may negatively
impact the user's
credit score). As an output, the virtual underwriter feature generates a
user's virtual
approval report, which can be used to develop an affordable budget of the user
for
acquiring the financial product and for use in provider negotiations by the
user. This
feature extends the SafeQuote capability described above.
The virtual underwriter feature operates by emulating portions of the
underwriting
system of an actual lender or other financial product provider. As one source
of
information, the system accesses credit bureau information in the role of a
non-lender,
that is, in a role such that the credit bureau will not treat the interaction
as a "real.'
interaction by a lender making an actual underwriting decision. The system
does this by
providing the same types of information to the credit bureau that an
underwriter would
provide in the course of processing a real application for credit, but without
the system
being identified as a real lender. This results in no impact on the credit
report generated
by the credit bureau. The user's credit file received from the credit bureau
is imported
into a credit file profile portion of the user's personal profile.
As shown in figure 42, the credit file information serves as an input to a
virtual
underwriter algorithm 1232. The user's personal profile provides inputs to the
algorithm
that include name, address, income, and last 4 digits of a user's social
security number to
secure a credit report. The credit file, and these other profile inputs, are
transmitted to a
software system which contains the algorithm 1232 that simulates an
underwriting
process used by a typical provider of the financial product involved.
The algorithm includes underwriting criteria that, in this example, a lender
would
use to approve credit based on the type of loan and credit spectrum involved.
These
criteria could include debt to income ratios, past payment performance, credit
score, open
credit lines, and number of credit inquiries from potential lenders
(indicating a more
intense need for borrowing).
The output of the algorithm is then processed to render a virtual approval
report.
In combination with fair price discussed earlier, the user then has a fair
price (including
interest rate, fees, and product type) providing a representation of what a
good product
and price looks like, and knows the likelihood that a lender will approve the
user's
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application. In combination these reports provide the user with more
bargaining power in
negotiation with product providers, which will lead to lower prices or better
terms or
both.
As shown in figure 43, in some implementations of the system, machine learning
software 1236 is used to first cluster, then group, user profiles and user
credit reports, and
then human experts 1240 match those user profiles to approval probabilities
represented
by the relevant clusters and groups (without the creation of a decision tree
algorithm)
through a user interface 1238 to the machine learning software. This process
is used to
aid, and may replace, the decision tree algorithm development for matching the
optimal
financial product to a profile.
To render the virtual approval report 1242, underwriting inputs are conveyed
to
an underwriting profile portion of the user profile. The underwriting inputs
are obtained
from some or all of the following five sources: 1) credit bureau data
delivered through
API links; 2) a user's answers to questions required to access a credit report
listed above
that are entered through a user interface; 3) if applicable, information about
the user's
existing financial product and personal preferences; 4) other enhancement data
from third
parties concerning the user's financial condition and preferences delivered
through API
links, and 5) for users of mobile phones, location-based data for underwriting
inputs
describing physical location. This information is stored as part of the
underwriting profile
portion of the unique user profile in the database. An algorithm uses these
five input
sources to determine the pre-approval conditions rendered in the virtual
approval report.
Software then renders the algorithm output into a visual design which can then
be
rendered on a computer, tablet, or smartphone screen, shared with other
individuals
through links to email, texting, push notifications, and social media, as well
as printed.
An example of an online presentation includes the information in box 1264 and
the print
button 1265 shown in figure 47.
In some implementations, a dynamic matching system (DMS) allows a specific
user to get insight into which is the best possible financial product for the
user's specific
situation and needs. In addition, DMS regularly monitors the user's situation
and
financial product inventory to ensure that a given financial product is the
optimum
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product (for example, has the lowest price with the best features for the
user's needs)
given continuously changing personal situations and a shifting product
marketplace. This
means the user can always be sure the user has the best possible deal. The DMS
system is
robust because it can optimally match a specific product to a specific user in
a fully
dynamic and bias-free manner.
DMS operates by creating a user profile, generating a fair price (see above)
for a
given user profile, and then comparing the fair price to an inventory of
available vetted
products suitable for the user or a marketplace of providers willing to meet
the terms of
the fair price or both. User inputs can cover a broad range of possible user
types and
situations. Characteristics of user types would include various
creditworthiness profiles,
geographic locations, demographic profiles, and other defining features that
will
influence the financial products recommended. Situations would include the
context of
specific needs of the user, e.g., a first time homebuyer trying to get a
mortgage; an
unexpected expense that requires financing; or a purchase of a new car
requiring a new
insurance policy.
The output is a DMS report, provided through a software application, text,
email,
push in-application messaging, or similar communication medium, detailing
optimum
product matches.
In some implementations, product matches include a combination of preferences
and pricing generated by a computer algorithm that optimizes price and feature
tradeoffs.
The computer algorithm uses user profile inputs that result in either
including or
excluding certain products based on the inputs. For example, if a user is
attempting to get
a mortgage for a property in Massachusetts, mortgage underwriters that do not
write
mortgages in Massachusetts are eliminated from consideration by the algorithm.
Another
example would be the algorithm eliminating credit card products that require a
high
income for those users with a lower income.
For initial DMS reports, this matching process happens at the user's request.
For
those users requesting ongoing monitoring to ensure optimum fit and pricing,
the process
is repeated on a monthly (or other periodic) basis. Users may determine the
conditions
under which they wish to be notified on a pricing or fit basis. Users whose
circumstances
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change (e.g., a move to a different location) can re-initiate the process by
providing new
profile inputs, or by allowing the system to monitor key inputs (e.g.,
location) to generate
proactive reports on savings opportunities. In some applications, based on
information
provided by a mobile application, or through other data linked to the user
(e.g., a change
in shopping habits, or change in product usage perceived by the system via
linkage to the
account), the system will determine that the current products held by the user
are a
mismatch, perhaps resulting in too-high price given the change in location or
situation,
totally unbeknownst to the user. The system perceives that and alerts the user
proactively
based on inputs the system has passively collected to help optimize the user's
situation.
In some implementations of the system, machine learning software is used to
first
cluster, then group, profiles and products, and then human experts match those
profiles to
products (without the creation of a decision tree algorithm) through a user
interface to the
machine learning software. As shown in figure 48, the user interface lists
product clusters
1266 and customer profile clusters 1268 and provides the user the opportunity
to modify,
accept, or reject each of them. This process is used to aid, and may replace,
the decision
tree algorithm development for matching the optimal product to a profile.
As shown in figure 44, in the dynamic matching system process 1244, there is
dynamic and ongoing monitoring 1250 of user profiles and market information.
The
system engages in dynamic and ongoing checking for changes in the customer
profile
database 1246 and also engages in dynamic and ongoing checking for changes in
financial product information and pricing database 1248. Based on detected
changes in
the customer profile of the financial product databases or both, new fair
price outputs are
generated 1252. In addition, based on detected changes in the customer
profiles or the
financial product databases or both, new fair price outputs are generated.
To render the DMS report, user profile inputs are conveyed to the user profile
in
the database from some or all of the following five sources: 1) credit bureau
data
delivered through API links; 2) a user's answers to questions regarding
underwriting
criteria that are entered through a user interface; 3) if applicable,
information about the
user's incumbent product and personal preferences; 4) other enhancement data
from third
parties concerning the user's financial condition and preferences delivered
through API
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links, and 5) for users of mobile phones, location-based and other relevant
data inputs
useful to optimizing product matching. This information is stored as part of
the unique
user profile in the database. An algorithm uses these five input sources to
determine the
fair price (see above) for a particular user's situation and product need.
Software then
renders the algorithm output into a visual design which can then be rendered
on a
computer, tablet, or smartphone screen, shared with other individuals through
links to
email, texting, push notifications, and social media, as well as printed.
Although the system that we describe here and the techniques by which the
system directly provide information and guidance to the users helps its users
optimize
their financial products, their costs, and more generally their financial
lives. Effective
dissemination of the advantages of the system may be difficult, however, when
only a
single provider offers them. Such dissemination will be improved by better
engagement
with users and potential users.
In some implementations, the system and its advantages can be deployed on a
private label basis through trusted third parties, for example, third parties
whose
objectives are aligned with the goal of optimizing users' financial lives.
Examples of
trusted third parties that have relationships and engage effectively with end
users include
employers, personal financial management and investment companies, and other
organizations that have an interest in saving money for their constituents.
To disseminate the system on a private label basis, the system can be deployed
as
a partner platform through engagements with the trusted third parties. We use
the term
platform broadly to include, for example, any system capable of supporting
services
through such trusted third parties (which we sometimes also called
"partners"). This use
of the system is distinct from the use of the system by a single brand for its
own users,
which we sometimes refer to as a retail deployment.
In some implementations, a partner platform is realized using the following
features:
1. A user interface system capable of supporting and mimicking the brand,
look, and feel
of communications and presentations in use by a partner;
2. A modular design allowing a partner to modify the presentations with
respect to
financial products that have been analyzed or optimized by the system (e.g., a
bank
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deploying a partner platform for the benefit of its employees may want to
disable the
bank financial product optimizer features);
3. A system fully deployable behind a partner firewall so that the platform
partners do not
have to transmit personal information about their constituents to the system;
4. A software system that allows a partner to request data from the system
through an
application program interface (API) using security credentials provided to the
partner by
the system.
5. Features of parts of the host dynamic matching system (DMS):
Part 1: The user interface for a platform partner is identical to the host
system user
interface from a design perspective, but it allows for a platform partner to
add its own
brand, company logo, and color scheme to the user interface that it presents
to its users so
that it is clear the partner is endorsing use of the platform by the partner's
constituents.
Software code renders the user interface to display on a computer, tablet, or
phone
screen. A partner software development kit (SDK) is provided to the partner.
The SDK
includes software code and instructions to allow the partner to fully
customize its user
interface (with logos, trademarks, and color schemes relating to the partner).
That
software code renders the functional user interface using this customized
design, which is
then deployed by the partner to its constituents.
Part 2: The modular design allows the partner to select only those financial
products and types of financial products that it wants its constituents to
optimize (e.g., to
find the lowest price product with the best features). For example, a bank
wishing to
helps its employees save money on the bank's financial product and related
bills may not
want to suggest that its employees use another bank for their personal
checking account
needs. In such an implementation, the partner will access a partner control
panel to
determine which financial products and types of financial products it wishes
its
constituents to optimize or otherwise be exposed to through the user
interface. Software
code in the partner's system then modifies the financial product categories
and financial
products displayed in the user interface. In this example, the bank offering
the platform as
an employee benefit would de-select "Bank Accounts", and the resulting user
interface
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would be modified to eliminate this product category through software code
controlling
the rendering on the computer, tablet, or phone screen.
Part 3: Deploying the platfolin behind a partner's firewall: For privacy and
regulatory reasons, partners may not want to deploy the system in a way that
requires the
partner to transmit any personal details about its constituents to the system
hosts, or to
any other third party. The partner system allows the platform to be deployed
behind the
Cinch partner's firewall so that the system host's storage systems never house
the
partner's constituents' personally identifiable data, such as name, address,
and contact
information. This is effected by creating a partner user profile database
within the
partner's own system. Software linkages between this private profile database
and the
system host's DMS inventory and selection software would allow for operation
without
the system host housing personally identifiable information while allowing
full operation
of the DMS.
Part 4: An application program interface is provided as a software system that
allows the partner to securely access the Cinch system.
Part 5: The DMS described above.
In some implementations, a partner deploys the host system's user interface
for its
constituents using its own brand imagery, look, and feel. In all material
respects this
platform can be identical to the retail platform.
As described above, users create a personal profile. The profile draws
information
from some or all of the following six sources: I) credit bureau data delivered
through API
links; 2) a user's answers to questions regarding underwriting criteria that
are entered via
a user interface; 3) if applicable, information about the user's incumbent
product and
personal preferences; 4) other enhancement data from third parties concerning
the user's
financial condition and preferences delivered through API links, 4) for users
of mobile
phones, location-based and other relevant data inputs useful to optimizing
product
matching. This information is stored as part of the unique user profile in a
database, and
6) data from the partner's system that provides context on a user's situation
or
preferences.
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For example, a partner that is a brokerage company would have information
regarding the credit score of a partner user. In this situation, it would not
be necessary to
get a separate credit report on behalf of the user, because that information
is already part
of the partner's customer database. Unlike the retail platform, this partner
system resides
behind the firewall of the partner. This is done so that the host's system
does not receive
any personally identifiable information regarding the partner user. In this
way the privacy
policies and any regulatory restrictions on sharing information with third
parties is
avoided. In other respects, the host's system works as described above, for
example,
generating fair price and virtual underwriting reports and enabling dynamic
matching.
The software algorithms necessary to complete these operations are stored and
operate
outside of the partner firewall, but are completed on an anonymous basis using
matching
user codes generated by the partner system. In other respects the partner's
system
operates in a fashion similar to the retail platform.
As shown in figures 55 through 59, in some implementations, interaction with
an
end user is done through a user interface of a mobile device 1300. In some
examples, the
interaction is conducted in a simple, easy, and conversational way using a
digital assistant
1302 in the form of a helper called, for example, Alex. Messages 1303 from
Alex to the
user can appear in text on the screen of the mobile device or be spoken
through the
speaker of the mobile device or both. After the user logs in (registering
first, if necessary,
1302), Alex asks questions 1306 to prompt the user to enter personal
information that
will be used by the system in way similar to the ones described earlier. When
a specific
question is asked, a text entry box 1308 can be displayed in the user can
enter the
requested information 1310 through the keyboard. In some cases, the questions
can be
asked audibly through the speaker of the mobile device and the user can
respond by
speaking to the device. Alex acknowledges 1312 the user's entry and in general
conducts
an interactive conversation with the user that makes the use of the system
inviting,
pleasant, and accurate.
In the discussion above, we have frequently made reference to machine
learning.
As shown in figure 60, in some implementations of machine learning useful for
the
techniques we have described here, two datasets (known inputs 1302 and known
outputs
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1304) are used to create robust matching algorithms 1306 that "learn" and get
more
accurate with repeated iterations in producing current appropriate outputs for
given
current inputs. The matching between those two datasets, reflected in those
machine
learning algorithms, expresses the underlying "connections" between the known
inputs in
the known outputs.
In a movie recommendation system, for example, the known input data set could
contain data relating to characteristics of viewers and the known output data
set could
contain information about movies previously viewed by the viewers (in other
words, past
viewing habits of viewers). The main function of the movie recommendation
system
would be to provide (as current outputs 1308) suggestions for other movies the
viewer
might like.
These machine learning systems are a foundational element for most e-commerce
platforms.
For consumer financial products, the known input data includes demographic and
other information about the users and their circumstances and the known output
data
includes the products and features that they chose to acquire or use. The
typical machine
learning process described above does not work well or may be impossible,
because,
unlike the known outputs of which movies viewers decided to view, many, or
most
financial decisions consumers make about which products and features to
acquire or use
our erroneous because of information asymmetry, confusing mathematical
concepts (e.g.
the arithmetic difference between APR and interest rate), very advanced
financial
structures and lingo being beyond the ken of most people (e.g. 5/1 capped
ARMs), and
perceptual bias mistakes most consumers commit which are well documented by
behavioral economists (e.g., inability to properly evaluate risk; economic
impact of
interest charges, etc.). In other words, the known outputs (the consumer's
decisions about
products and features) can be matched in the machine learning system with the
demographics, characteristics, and circumstances of users but the matching is
not helpful
because what is learned is connections between known inputs and
disadvantageous
known outputs.
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This situation can lead to great inefficiency in the consumer financial
marketplace
and frequent disadvantageous mismatches between customers and financial
products.
To circumvent this situation, we use what we call a synthetic matching
development tool (SMDT) that enables machine learning techniques to be applied
to
matching of consumers and products in context in which consumer decisions have
the
potential of being incorrect or incomplete. The SMDT enables machine learning
to be
applied effectively in the face of information-asymmetric, confusing, and
behaviorally
complex product categories such as consumer lending, selection of products
with
multiple comparison dimensions, and goal-based savings decisions. The SMDT is
a
prototyping tool to develop matching engines than can then be put into
production.
As shown in figure 60, the SMDT 1310 operates by ingesting input data (known
inputs) describing a consumer's personal profile (past spending, demographic
detail such
as household size and composition, credit performance information like credit
score, and
other preferences and attributes that should be deterministic of good product
matches for
a given product type. These data are ingested from various sources that we
have
described, including API linkages with third party data providers, and
internal databases.
The ingested information is housed in a personal profile database 1312. The
process is
repeated to create a consistent dataset for thousands, or millions, of
customers.
A software computing process 1314 then finds clusters 1318 of profiles
corresponding to key dimensions that apply to a given situation and product
need. A
given profile 1316 may have membership in several clusters 1318. For example,
a given
profile may join a cluster of similarly situated profiles for the purposes of
optimizing a
family cell phone account and dataplan because the household sizes tend to be
similar, as
well as in another cluster based on the locations where the cell phone is
used.
These data comprise the SMDT input dataset. All profiles and cluster
memberships are stored in a relational or unstructured database 1312 with
corresponding
access tools to extract data.
To create a synthetic output dataset 1320 required to implement a machine
learning algorithm, financially correct, fully optimized product "decisions"
or optimized
outcomes, are loaded into the synthetic output database 1320 through a
network. The
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sources 1322 for the product decision outputs that are loaded into the known
outputs
1320 depend on the nature of the product, but may range from experts
assembling
product outputs, to specialized statistical algorithms that generate multiple
product sets,
each with varying features organized to express the optimal features
efficiently using
reductive mathematical principles.
For consumer debt products, for example, these inputs may be loaded directly
from a screened set of providers as discussed earlier, each of which is
annotated with
identifiers corresponding to optimized use situations (e.g., long-term debt
pay-down,
short-term cash need, etc.). The output data is stored in a relational or
unstructured
database.
These known inputs and synthetic outputs are then matched using a machine
learning algorithm, guided by a process utilizing product-specific experts
providing
guidance through an expert portal 1322 connected by API to the machine
learning
algorithm software service.
The result is an optimized matching process and algorithm using a normalized
and
correct approach to the outputs to eliminate error propagation. As the SNDT
algorithm
gains experience with actual profiles, and as other data sources are added,
certainty
continues to rise and the matching engine becomes more robust. This SMDT
system is
then connected by a network to a production matching software system for
implementation after the prototyping and algorithm development are completed.
Embodiments can be implemented in digital electronic circuitry, or in computer
hardware, firmware, software, or in combinations thereof. Apparatus of the
invention can
be implemented in a computer program product tangibly embodied or stored in a
machine-readable storage device for execution by a programmable processor; and
method
actions can be performed by a programmable processor executing a program of
instructions to perform functions of the invention by operating on input data
and
generating output. The invention can be implemented advantageously in one or
more
computer programs that are executable on a programmable system including at
least one
programmable processor coupled to receive data and instructions from, and to
transmit
data and instructions to, a data storage system, at least one input device,
and at least one
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output device. Each computer program can be implemented in a high-level
procedural or
object oriented programming language, or in assembly or machine language if
desired;
and in any case, the language can be a compiled or interpreted language.
Suitable processors include, by way of example, both general and special
purpose
microprocessors. Generally, a processor will receive instructions and data
from a read-
only memory and/or a random access memory. Generally, a computer will include
one or
more mass storage devices for storing data files; such devices include
magnetic disks,
such as internal hard disks and removable disks; magneto-optical disks; and
optical disks.
Storage devices suitable for tangibly embodying computer program instructions
and data
include all forms of non-volatile memory, including by way of example
semiconductor
memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic
disks
such as internal hard disks and removable disks; magneto-optical disks; and CD
ROM
disks. Any of the foregoing can be supplemented by, or incorporated in, ASICs
(application-specific integrated circuits).
Other embodiments are within the scope of the claims.
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