U.S. patent application number 17/323879 was filed with the patent office on 2021-09-02 for instant lending decisions.
This patent application is currently assigned to Intuit Inc.. The applicant listed for this patent is Intuit Inc.. Invention is credited to Joesph Timothy Callinan, JR., Eva Diane Chang, Richard N. Preece, Siddharth Ram, Kathy Tsitovich.
Application Number | 20210272195 17/323879 |
Document ID | / |
Family ID | 1000005586684 |
Filed Date | 2021-09-02 |
United States Patent
Application |
20210272195 |
Kind Code |
A1 |
Ram; Siddharth ; et
al. |
September 2, 2021 |
Instant Lending Decisions
Abstract
A method including training a machine learning algorithm by
iteratively adjusting, by a computer processor, adjusted matching
parameters to increase a correlation between approval statistics of
lending decisions and risk profiles. The risk profiles represent
probabilities of businesses defaulting on a loan. The probabilities
are derived from usage statistics of a business management
application (BMA) used by the businesses. Iteratively adjusting
continues until reaching a threshold correlation between the
approval statistics and the lending decisions and the risk
profiles. Training generates an updated machine learning algorithm.
An updated risk score for a business entity is generated using a
number of logins to the BMA made by the business entity.
Inventors: |
Ram; Siddharth; (Menlo Park,
CA) ; Preece; Richard N.; (San Diego, CA) ;
Callinan, JR.; Joesph Timothy; (Campbell, CA) ;
Tsitovich; Kathy; (Mountain View, CA) ; Chang; Eva
Diane; (Mountain View, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Intuit Inc. |
Mountain View |
CA |
US |
|
|
Assignee: |
Intuit Inc.
Mountain View
CA
|
Family ID: |
1000005586684 |
Appl. No.: |
17/323879 |
Filed: |
May 18, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16198599 |
Nov 21, 2018 |
11055772 |
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17323879 |
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13956281 |
Jul 31, 2013 |
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16198599 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 40/025
20130101 |
International
Class: |
G06Q 40/02 20060101
G06Q040/02 |
Claims
1. A method, comprising: training a machine learning algorithm by
iteratively adjusting, by a computer processor, adjusted matching
parameters of the machine learning algorithm to increase a
correlation between approval statistics of a plurality of lending
decisions and a plurality of risk profiles, wherein: the plurality
of risk profiles represent probabilities of a plurality of
businesses defaulting on a loan, the probabilities derived from
usage statistics of a business management application (BMA) used by
the plurality of businesses, the plurality of lending decisions are
received from a computing device of a first lender and represent
decisions made by the first lender whether to extend the loan to
the plurality of businesses based on the plurality of risk
profiles, iteratively adjusting continues until reaching a
threshold correlation between the approval statistics and the
plurality of lending decisions and the plurality of risk profiles,
and training generates an updated machine learning algorithm; and
updating a risk score of a risk profile for a business entity in
the plurality of businesses to generate an updated risk score,
wherein the risk score of the risk profile for the business entity
is updated using a number of logins to the BMA made by the business
entity.
2. The method of claim 1, further comprising: executing the updated
machine learning algorithm, taking as input the updated risk score,
and generating as output a probability that the business entity
will default on a loan.
3. The method of claim 1, wherein: the usage statistics comprises
at least one category selected from the group consisting of
business statistics, business financial data, online banking usage
statistics, accounting software trial details, marketing
interaction data, general setup statistics, payroll setup
statistics, customer support data, firmographics, product usage,
subscription details, subscription billing details, payroll
processing details, attrition details, customer statistics, pattern
changes, transaction statistics, chargebacks statistics, and age
statistics, and the machine learning algorithm comprises a rule
ensemble algorithm.
4. The method of claim 1, further comprising: obtaining loan
default statistics of the plurality of businesses; analyzing the
loan default statistics in relationship to the plurality of risk
profiles to generate a second correlation; and adjusting the
machine learning algorithm to increase the second correlation.
5. The method of claim 1, further comprising: providing the risk
profile to the business entity, wherein the business entity submits
the risk profile to a second lender to apply for a loan.
6. The method of claim 1, further comprising: extracting, using a
pre-determined clustering algorithm and based on a pre-determined
similarity measure, a cluster of similar risk profiles from the
plurality of risk profiles, wherein the cluster of similar risk
profiles corresponds to a subset of the plurality of businesses;
generating a loan proposal based on the cluster of similar risk
profiles; and presenting the loan proposal to at least one entity
selected from the group consisting of the first lender, a second
lender, and the subset of the plurality of businesses.
7. The method of claim 1, further comprising: obtaining a target
risk profile from a second lender; extracting, based on the target
risk profile, a cluster of similar risk profiles from the plurality
of risk profiles, wherein the cluster of similar risk profiles
corresponds to a subset of the plurality of businesses; and
presenting the cluster of similar risk profiles and the subset of
the plurality of businesses to the second lender, wherein the
second lender offers a loan program to the subset of the plurality
of businesses.
8. A system for generating a risk profile of a business entity,
comprising: a computer processor; a business management application
(BMA) configured to obtain and store a plurality of usage
statistics of a plurality of businesses that use the BMA; memory
storing instructions executable by the processor, wherein the
instructions comprise: a risk profile generator configured to
update a risk score of a risk profile for a business entity in the
plurality of businesses to generate an updated risk score, wherein
the risk score of the risk profile for the business entity is
updated using a number of logins to the BMA made by the business
entity. a machine learning algorithm configured to be trained by
iteratively adjusting adjusted matching parameters of the machine
learning algorithm to increase a correlation between approval
statistics of a plurality of lending decisions and a plurality of
risk profiles, wherein: the plurality of risk profiles represent
probabilities of a plurality of business entities defaulting on a
loan, the probabilities derived from usage statistics of a business
management application (BMA) used by the plurality of business
entities, the plurality of lending decisions are received from a
computing device of a lender and represent decisions made by the
lender whether to extend the loan to the plurality of businesses
based on the plurality of risk profiles, iteratively adjusting
continues until reaching a threshold correlation between the
approval statistics and the plurality of lending decisions and the
plurality of risk profiles, and a repository configured to store
the trained machine learning algorithm.
9. The system of claim 8, wherein: the usage statistics comprises
at least one category selected from the group consisting of
business statistics, business financial data, online banking usage
statistics, accounting software trial details, marketing
interaction data, general setup statistics, payroll setup
statistics, customer support data, firmographics, product usage,
subscription details, subscription billing details, payroll
processing details, attrition details, customer statistics, pattern
changes, transaction statistics, chargebacks statistics, and age
statistics, and the machine learning algorithm comprises a rule
ensemble algorithm.
10. The system of claim 8, wherein the risk profile generator is
further configured to: obtain loan default statistics of the
plurality of businesses; analyze the loan default statistics in
relationship to the plurality of risk profiles to generate a second
correlation; and adjust the machine learning algorithm to increase
the second correlation.
11. The system of claim 8, wherein the risk profile generator is
further configured to: provide the risk profile to the business
entity, wherein the business entity submits the risk profile to a
second lender to apply for a loan.
12. The system of claim 8, wherein the risk profile generator is
further configured to: extract, using a pre-determined clustering
algorithm and based on a pre-determined similarity measure, a
cluster of similar risk profiles from the plurality of risk
profiles, wherein the cluster of similar risk profiles corresponds
to a subset of the plurality of businesses; generate a loan
proposal based on the cluster of similar risk profiles; and present
the loan proposal to at least one entity selected from the group
consisting of the first lender, a second lender, and the subset of
the plurality of businesses.
13. The system of claim 8, wherein the risk profile generator is
further configured to: obtain a target risk profile from a second
lender; extract, based on the target risk profile, a cluster of
similar risk profiles from the plurality of risk profiles, wherein
the cluster of similar risk profiles corresponds to a subset of the
plurality of businesses; and present the cluster of similar risk
profiles and the subset of the plurality of businesses to the
second lender, wherein the second lender offers a loan program to
the subset of the plurality of businesses.
14. The system of claim 8, further comprising: an adaptive matching
analyzer configured to execute the updated machine learning
algorithm, taking as input the updated risk score, and generating
as output a probability that the business entity will default on a
loan.
15. A non-transitory computer readable medium storing instructions
which, when executed by a computer processor, comprise
functionality for: training a machine learning algorithm by
iteratively adjusting, by a computer processor, adjusted matching
parameters of the machine learning algorithm to increase a
correlation between approval statistics of a plurality of lending
decisions and a plurality of risk profiles, wherein: the plurality
of risk profiles represent probabilities of a plurality of
businesses defaulting on a loan, the probabilities derived from
usage statistics of a business management application (BMA) used by
the plurality of businesses, the plurality of lending decisions are
received from a computing device of a first lender and represent
decisions made by the first lender whether to extend the loan to
the plurality of businesses based on the plurality of risk
profiles, iteratively adjusting continues until reaching a
threshold correlation between the approval statistics and the
plurality of lending decisions and the plurality of risk profiles,
and training generates an updated machine learning algorithm; and
updating a risk score of a risk profile for a business entity in
the plurality of businesses to generate an updated risk score,
wherein the risk score of the risk profile for the business entity
is updated using a number of logins to the BMA made by the business
entity.
16. The non-transitory computer readable medium of claim 15,
wherein the instructions further comprise functionality for:
executing the updated machine learning algorithm, taking as input
the updated risk score, and generating as output a probability that
the business entity will default on a loan.
17. The non-transitory computer readable medium of claim 15,
wherein the instructions further comprise functionality for:
obtaining loan default statistics of the plurality of businesses;
analyzing the loan default statistics in relationship to the
plurality of risk profiles to generate a second correlation; and
adjusting the machine learning algorithm to increase the second
correlation.
18. The non-transitory computer readable medium of claim 15,
wherein the instructions further comprise functionality for:
providing the risk profile to the business entity, wherein the
business entity submits the risk profile to a second lender to
apply for a loan.
19. The non-transitory computer readable medium of claim 15,
wherein the instructions further comprise functionality for:
extracting, using a pre-determined clustering algorithm and based
on a pre-determined similarity measure, a cluster of similar risk
profiles from the plurality of risk profiles, wherein the cluster
of similar risk profiles corresponds to a subset of the plurality
of businesses; generating a loan proposal based on the cluster of
similar risk profiles; and presenting the loan proposal to at least
one entity selected from the group consisting of the first lender,
a second lender, and the subset of the plurality of businesses.
20. The non-transitory computer readable medium of claim 15,
wherein the instructions further comprise functionality for:
obtaining a target risk profile from a second lender; extracting,
based on the target risk profile, a cluster of similar risk
profiles from the plurality of risk profiles, wherein the cluster
of similar risk profiles corresponds to a subset of the plurality
of businesses; and presenting the cluster of similar risk profiles
and the subset of the plurality of businesses to the second lender,
wherein the second lender offers a loan program to the subset of
the plurality of businesses.
Description
RELATED APPLICATIONS
[0001] This application is a continuation application of U.S.
application Ser. No. 16/198,599, filed Nov. 21, 2018, now U.S. Pat.
No. ______; which is a continuation application of U.S. application
Ser. No. 13/956,281 filed Jul. 31, 2013; all of which are hereby
incorporated by reference.
BACKGROUND
[0002] Banks often have trouble lending to a small business because
they do not have an effective approach to assess the quality of a
small business, and often default to using the small business
proprietor's credit scores.
SUMMARY
[0003] In general, in one aspect, the one or more embodiments
relate to a method. The method includes training a machine learning
algorithm by iteratively adjusting, by a computer processor,
adjusted matching parameters of the machine learning algorithm to
increase a correlation between approval statistics of lending
decisions and risk profiles. The risk profiles represent
probabilities of businesses defaulting on a loan. The probabilities
are derived from usage statistics of a business management
application (BMA) used by the businesses. The lending decisions are
received from a computing device of a first lender and represent
decisions made by the first lender whether to extend the loan to
the businesses based on the risk profiles. Iteratively adjusting
continues until reaching a threshold correlation between the
approval statistics and the lending decisions and the risk
profiles. Training generates an updated machine learning algorithm.
The method also includes updating a risk score of a risk profile
for a business entity in the businesses to generate an updated risk
score. The risk score of the risk profile for the business entity
is updated using a number of logins to the BMA made by the business
entity.
[0004] The one or more embodiments also relate to a system for
generating a risk profile of a business entity. The system includes
a computer processor. The system also includes a business
management application (BMA) configured to obtain and store usage
statistics of businesses that use the BMA. The system also includes
memory storing instructions executable by the processor. The
instructions include a risk profile generator configured to update
a risk score of a risk profile for a business entity in the
businesses to generate an updated risk score. The risk score of the
risk profile for the business entity is updated using a number of
logins to the BMA made by the business entity. The instructions
include a machine learning algorithm configured to be trained by
iteratively adjusting adjusted matching parameters of the machine
learning algorithm to increase a correlation between approval
statistics of lending decisions and risk profiles. The risk
profiles represent probabilities of business entities defaulting on
a loan. The probabilities are derived from usage statistics of a
business management application (BMA) used by the business
entities. The lending decisions are received from a computing
device of a lender and represent decisions made by the lender
whether to extend the loan to the businesses based on the risk
profiles. Iteratively adjusting continues until reaching a
threshold correlation between the approval statistics and the
lending decisions and the risk profiles. The system also includes a
repository configured to store the trained machine learning
algorithm.
[0005] The one or more embodiments also provide for a
non-transitory computer readable medium storing instructions which,
when executed by a computer processor, perform functionality. The
functionality includes training a machine learning algorithm by
iteratively adjusting, by a computer processor, adjusted matching
parameters of the machine learning algorithm to increase a
correlation between approval statistics of lending decisions and
risk profiles. The risk profiles represent probabilities of
businesses defaulting on a loan. The probabilities are derived from
usage statistics of a business management application (BMA) used by
the businesses. The lending decisions are received from a computing
device of a first lender and represent decisions made by the first
lender whether to extend the loan to the businesses based on the
risk profiles. Iteratively adjusting continues until reaching a
threshold correlation between the approval statistics and the
lending decisions and the risk profiles. Training generates an
updated machine learning algorithm. The functionality also includes
updating a risk score of a risk profile for a business entity in
the businesses to generate an updated risk score. The risk score of
the risk profile for the business entity is updated using a number
of logins to the BMA made by the business entity.
[0006] Other aspects of the invention will be apparent from the
following description and the appended claims.
BRIEF DESCRIPTION OF DRAWINGS
[0007] FIG. 1 shows a block diagram of a system in accordance with
one or more embodiments of the invention.
[0008] FIG. 2 shows a flow chart of a method in accordance with one
or more embodiments of the invention.
[0009] FIG. 3 shows an example in accordance with one or more
embodiments of the invention.
[0010] FIG. 4 shows a computer system in accordance with one or
more embodiments of the invention.
[0011] FIGS. 5A, 5B, 5C, 5D, 5E, 5F, 5G, 5H, 5I, 5J, 5K, and 5L
show Table 1 in accordance with one or more embodiments.
[0012] FIGS. 6A, 6B, 6C, 6D, 6E, 6F, 6G, 6H, 6I, 6J, 6K, 6L, 6M,
and 6N show Table 2 in accordance with one or more embodiments.
DETAILED DESCRIPTION
[0013] Specific embodiments of the invention will now be described
in detail with reference to the accompanying figures. Like elements
in the various figures are denoted by like reference numerals for
consistency.
[0014] In the following detailed description of embodiments of the
invention, numerous specific details are set forth in order to
provide a more thorough understanding of the invention. However, it
will be apparent to one of ordinary skill in the art that the
invention may be practiced without these specific details. In other
instances, well-known features have not been described in detail to
avoid unnecessarily complicating the description.
[0015] In general, embodiments of the invention provide a method,
system, and computer readable medium to generate a risk profile of
a small business (SMB) based on accounting data and other third
party business management application (BMA) data of the SMB. In
particular, the accounting data and other third party BMA data are
retrieved from a business management application (e.g., accounting
application, payroll application, tax preparation application,
personnel application, etc.) as a software-as-an-service (SaaS)
used by the SMB. Specifically, the risk profile represents the
likelihood of the SMB to be delinquent and/or to default on a loan.
In one or more embodiments, the risk profile is provided to a
lender for making an expedient lending decision with respect to the
SMB. In one or more embodiments, statistics of lenders' lending
decisions based on provided risk profiles are analyzed to generate
a correlation. Accordingly, the algorithm(s) used to generate the
risk profile from the accounting data and other third party BMA
data are adjusted to maximize the correlation.
[0016] FIG. 1 shows a block diagram of a system (100) for
generating a risk profile based on third party BMA data for instant
lending decisions in accordance with one or more embodiments of the
invention. Specifically, the system (100) includes business
entities (e.g., business entity A (101a)), lenders (e.g., lender X
(102x)), a BMA (105) used by the business entities, and a risk
profile generation tool (160) that are coupled via a computer
network (110). In one or more embodiments of the invention, the
risk profile generation tool (160), or a portion thereof, may be
integrated with the BMA (105). In one or more embodiments of the
invention, one or more of the modules and elements shown in FIG. 1
may be omitted, repeated, and/or substituted. Accordingly,
embodiments of the invention should not be considered limited to
the specific arrangements of modules shown in FIG. 1.
[0017] In one or more embodiments of the invention, the computer
network (110) may include a cellular phone network, a wide area
network, a local area network, a public switched telephone network
(PSTN), or any other suitable network that facilitates the exchange
of information from one part of the network to another. In one or
more embodiments, the computer network (110) is coupled to or
overlaps with the Internet.
[0018] In one or more embodiments, each of the business entities
(e.g., business entity A (101a), business entity M (101m), business
entity N (101n)), the lenders (e.g., lender X (102x), lender Y
(102y)), the BMA (105), and the risk profile generation tool (160)
may include any computing device configured with computing, data
storage, and network communication functionalities. In one or more
embodiments, the BMA (105) may be an accounting application, a tax
preparation application, a payroll application, a personnel
application, or any business management application. In one or more
embodiments, the BMA (105) is provided by an application service
provider, such as a software as a service (SaaS). For example, the
BMA (105) may be operated by the application service provider (ASP)
and accessed by the business entities (e.g., business entity A
(101a), business entity M (101m), business entity N (101n)) on a
subscription basis.
[0019] In one or more embodiments, BMA data (e.g., BMA data (105b)
including user entered data (105c) and usage statistics (105d) of
the business entity A (101a)) is generated in response to the
business entities accessing the BMA (105). For example, the user
entered data (105c) may include profile/configuration information
specified by the business entity A (101a). In particular, such
profile/configuration information may be entered into the BMA (105)
by a user associated with the business entity A (101a), who may be
an employee, a consultant, a business owner, etc. of the business
entity A (101a). In one or more embodiments, at least a portion of
the user entered data (105c) represents a measure of business
activities performed by the business entity A (101a). In addition,
the usage statistics (105d) may include statistics or other
behavioral information representing how the BMA (105) is used by
the business entity A (101a). Examples of the BMA data (105b) are
shown in TABLE 1 and TABLE 2 below. In particular, TABLE 1, shown
in FIGS. 5A, 5B, 5C, 5D, 5E, 5F, 5G, 5H, 5I, 5J, 5K, and 5L, lists
a number of example BMA data each corresponding to a category of
BMA data items. TABLE 2, shown in FIGS. 6A, 6B, 6C, 6D, 6E, 6F, 6G,
6H, 6I, 6J, 6K, 6L, 6M, and 6N, provides definitions of each BMA
data item. Although the BMA data (e.g., BMA data (105b)) is shown
in FIG. 1 as stored within the BMA (105), in one or more
embodiments, the BMA data (e.g., BMA data (105b)) may not persist
within the BMA (105). In one or more embodiments, the user entered
data (105c) and usage statistics (105d) of the business entity A
(101a) are stored in a repository (123) of the risk profile
generation tool (160) as the user entered data A (140a) and usage
statistics A (141a). Similarly, the BMA data (105b) of the business
entity M (101m) and business entity N (101n) may also be stored in
the repository (123) as the user entered data M (140m)/usage
statistics M (141m) and user entered data N (140n)/usage statistics
N (141n), respectively. For example, information stored in the user
entered data A (140a)/usage statistics A (141a), user entered data
M (140m)/usage statistics M (141m), and user entered data N
(140n)/usage statistics N (141n) may be retrieved and used by the
risk profile generation tool (160), as needed, instead of
persisting within the BMA (105).
[0020] As shown in FIG. 1, the risk profile generation tool (160)
includes a risk profile generator (107), an adaptive matching
analyzer (108), and the repository (123) storing information used
and/or generated by the risk profile generator (107) and the
adaptive matching analyzer (108).
[0021] In one or more embodiments, the risk profile generator (107)
is configured to obtain the BMA data (105b) from the BMA (105) for
storing in the repository (123). For example, the user entered data
(105c)/usage statistics (105d) included in the BMA data (105b) may
be stored as the user entered data A (140a) and usage statistics A
(141a) in the repository (123). Similarly, other BMA data (105b)
associated with the business entity M (101m) and business entity N
(101n) may be stored as the user entered data M (140m)/usage
statistics M (141m) and user entered data N (140n)/usage statistics
N (141n), respectively in the repository (123).
[0022] In one or more embodiments, the user entered data A
(140a)/usage statistics A (141a), user entered data M (140m)/usage
statistics M (141m), and user entered data N (140n)/usage
statistics N (141n) are analyzed by the risk profile generator
(107) to generate the risk profile A (142a) of the business entity
A (101a), the risk profile M (142m) of the business entity M
(101m), and the risk profile N (142n) of the business entity N
(101n), respectively. Specifically, the risk profile A (142a), risk
profile M (142m), and risk profile N (142n) represent a predicted
probability of the business entity A (101a), business entity M
(101m), and business entity N (101n), respectively, to be
delinquent on any loan payment or to default on a loan. In one or
more embodiments, the risk profile (e.g., risk profile A (142a),
risk profile M (142m), and risk profile N (142n)) includes one or
more of a probability of default, a probability of non-default, a
probability of delinquency, a probability of non-delinquency, a
probability of loan approval, and a probability of loan
declination, each represented by a number score, a percentage
score, a letter score, or other suitable type of score. For
example, payment delinquency (i.e., late payment) and/or loan
default (i.e., late payment exceeding a pre-determined duration
and/or frequency) may occur when the loan is serviced by one of the
lenders (e.g., lender X (102x), lender Y (102y)) or a loan service
entity associated with these lenders.
[0023] In one or more embodiments, the risk profiles (e.g., the
risk profile A (142a), risk profile M (142m), risk profile N
(142n)) are generated by the risk profile generator (107) using an
adaptively-determined matching algorithm such that the risk
profiles correlate with actual occurrences of payment delinquency
and/or loan default by the corresponding business entities (e.g.,
business entity A (101a), business entity M (101m), business entity
N (101n)) as borrowers, for example during a particular time
period. Accordingly, these risk profiles also indicate
probabilities that future payment delinquency and/or loan default
by the corresponding business entities may also occur. Generally,
actual occurrences of payment delinquency and/or loan default by
the borrowers are tracked and compiled by lenders (e.g., lender X
(102x), lender Y (102y)) as loan delinquency statistics. In one or
more embodiments, these loan delinquency statistics are obtained by
the risk profile generator (107) and stored in the repository (123)
as loan default statistics A (144a), loan default statistics M
(144m), and loan default statistics N (144n) corresponding to the
business entity A (101a), business entity M (101m), and business
entity N (101n), respectively. Note that each of the loan default
statistics A (144a), loan default statistics M (144m), and loan
default statistics N (144n) may be compiled over the same time
period for some business entities (e.g., business entity M (101m),
business entity N (101n)) and compiled or over different time
periods for other business entities (e.g., business entity A
(101a)).
[0024] In one or more embodiments, the aforementioned
adaptively-determined matching algorithm includes a machine
learning algorithm, such as a rule ensemble algorithm known to
those skilled in the art. For example, the risk profile A (142a)
may be generated by the risk profile generator (107) using the
machine learning algorithm that has been trained based on
risk-profile-to-loan-default correlation of other business
entities. As shown in FIG. 1, the risk profile M (142m), risk
profile N (142n), loan default statistics M (144m), and loan
default statistics N (144n) are generated/obtained prior to
generating the risk profile A (142a) and are used as part of a
training data set (140) for iteratively adjusting the machine
learning algorithm before generating the risk profile A (142a)
therewith. "Iteratively adjusting" is referred to as "training" in
the context of machine learning algorithm. In one or more
embodiments, the risk profile generator (107) is configured to
iteratively adjust (i.e., train) the adaptively-determined matching
algorithm during a training phase by at least (i) providing, during
an initial iteration of the training phase, the risk profile M
(142m) and risk profile N (142n), among other risk profiles in the
training data set (140) to one or more lenders (e.g., lender X
(101x), lender Y (101y)) for making lending decisions (e.g.,
approved or declined), such as represented by the loan approval
status M (143m), loan approval status N (143n), etc. with respect
to the respective business entity M (101m), business entity N
(101n), etc., (ii) obtaining the loan default statistics M (144m),
loan default statistics N (144n), etc. in response to these lending
decisions leading to an approval and initiation of the loans for
the business entity M (101m), business entity N (101n), etc., (iii)
analyzing the loan default statistics M (144m), default statistics
N (144n), etc. in relationship to the risk profile M (142m), risk
profile N (142n), etc. to generate a risk-profile-to-loan-default
correlation, and (iv) adjusting, prior to a subsequent iteration of
the training phase, the matching parameters (143) of the
adaptively-determined matching algorithm to increase (e.g.,
optimize or maximize) the risk-profile-to-loan-default correlation
for the subsequent iteration of the training phase.
[0025] In one or more embodiments, the training data set (140) may
further include the corresponding user entered data, usage
statistics, and loan approval statistics. In one or more
embodiments, in response to a pre-determined result of iteratively
adjusting (i.e., training) the adaptively-determined matching
algorithm based on the training data set (140), the risk profile
generator (107) is configured to analyze the user entered data A
(140a) and the usage statistics A (141a), using the adjusted
adaptively-determined matching algorithm, to generate the risk
profile A (142a) of the business entity A (101a). For example, the
pre-determined result may include an incremental change in the
risk-profile-to-loan-default correlation between two contiguous
iterations of the training phase being less a pre-determined amount
(e.g., less than 0.1% of the final risk-profile-to-loan-default
correlation). In other words, the matching parameters (143) may be
iteratively adjusted until any incremental percentage improvement
of the risk-profile-to-loan-default correlation is less than 0.1%
before the adaptively-determined matching algorithm is used to
analyze the user entered data A (140a) and the usage statistics A
(141a) for generating the risk profile A (142a) of the business
entity A (101a).
[0026] In one or more embodiments, once generated, the risk profile
A (142a) is provided by the risk profile generator (107) to the
business entity A (101a). Accordingly, the business entity A (101a)
may submit the risk profile A (142a) to one or more lenders (e.g.,
lender X (102x), lender Y (102y)) to apply for a loan. If such loan
application is approved and initiated, the corresponding loan
servicing history may be tracked for compiling the payment
delinquency and/or default statistics to generate the loan default
statistics A (144a) associated with the business entity A (101a).
In one or more embodiments, the user entered data A (140a), the
usage statistics A (141a), the risk profile A (142a), the
corresponding loan approval status A (143a), and the resultant loan
default statistics A (144a) may be further included in the training
data set (140) to generate an updated version of the training data
set (140). Subsequently, this updated version of the training data
set (140) may be used to generate additional risk profiles for
other business entities and/or to update existing risk profiles
(e.g., the risk profile A (142a), risk profile M (142m), risk
profile N (142n), etc.) as references for future loan
applications.
[0027] In one or more embodiments, the matching parameters (143) of
the adaptively-determined matching algorithm are further adjusted
to maximize the correlation between the risk profiles (e.g., the
risk profile A (142a), risk profile M (142m), risk profile N
(142n), etc.) and the corresponding loan approval status (e.g.,
loan approval status A (143a), loan approval status M (143m), loan
approval status N (143n)). In one or more embodiments, the adaptive
matching analyzer (108) is configured to analyze approval
statistics in relationship to the risk profiles to generate a
risk-profile-to-loan-approval correlation, which is maximized
during the training phase of the adaptively-determined matching
algorithm by adjusting the matching parameters (143).
[0028] Returning to the discussion of the risk profile generator
(107), in one or more embodiments, the risk profile generator (107)
is further configured to generate a loan proposal based on similar
risk profiles shared by a group of business entities. Such loan
proposal may then be sent to one or more lenders that may be
interested in initiating loans based on the anticipated risk/return
characteristics represented by such loan proposal. Details of
generating the loan proposal based on similar risk profiles shared
by a group of business entities are described in reference to FIG.
2 below.
[0029] In one or more embodiments, the risk profile generator (107)
is further configured to identify a group of business entities
matching a target risk profile requested by a lender. Details of
identifying business entities matching a target risk profile are
described in reference to FIG. 2 below.
[0030] FIG. 2 shows a flow chart for generating a risk profile
based on third party business management application data for
instant lending decision in accordance with one or more embodiments
of the invention. In one or more embodiments of the invention, the
method of FIG. 2 may be practiced using the system (100) described
in reference to FIG. 1 above. In one or more embodiments of the
invention, one or more of the steps shown in FIG. 2 may be omitted,
repeated, and/or performed in a different order than that shown in
FIG. 2. Accordingly, the specific arrangement of steps shown in
FIG. 2 should not be construed as limiting the scope of the
invention.
[0031] Initially in Step 201, business management application (BMA)
data of business entities is obtained from the BMA. In one or more
embodiments, the BMA may be an accounting application, a tax
preparation application, a payroll application, a personnel
application, or any business management application. In one or more
embodiments, the BMA is provided by an application service
provider, such as a software as a service (SaaS). For example, the
BMA may be operated by the application service provider (ASP) and
accessed by the business entities on a subscription basis. In one
or more embodiments, the BMA data include user entered data and
usage statistics described in reference to TABLE 1 above.
[0032] In Step 202, loan approval status and loan default
statistics of the business entities are obtained from lenders
providing loans to the business entities. Generally, business
entities apply for business loans from such lenders who may approve
or decline the loan application. For those loan applications that
are approved, actual occurrences of loan payment delinquency and
loan default are tracked and compiled by the lenders as loan
default statistics. In one or more embodiments, the loan approval
status and loan default statistics of the business entities are
obtained from the lenders based on certain business agreements. For
example, the business entities may have the ability to opt-in as
part of the loan application to release such information to
business partners of the lenders.
[0033] In Step 203, an adaptively-determined matching algorithm is
iteratively adjusted to match risk profiles of the business
entities to the corresponding loan approval status and loan default
statistics. In one or more embodiments, the risk profile includes
one or more of a probability of default, a probability of
non-default, a probability of delinquency, a probability of
non-delinquency, a probability of loan approval, and a probability
of loan declination, each represented by a number score, a
percentage score, a letter score, or other suitable type of
score.
[0034] In one or more embodiments, the risk profiles are modeled as
a function of the BMA data of the business entities using the
adaptively-determined matching algorithm. In other words, the
adaptively-determined matching algorithm is used to analyze the BMA
data and generate the corresponding risk profiles. In one or more
embodiments, the adaptively-determined matching algorithm includes
a machine learning algorithm, such as a rule ensemble algorithm
known to those skilled in the art. For example, the training data
set of the machine learning algorithm includes the BMA data, loan
approval statistics, and loan default statistics of the business
entities. Accordingly, various parameters of the machine learning
algorithm are iteratively adjusted during a training phase to match
the modeled risk profile (e.g., predicted loan
approval/declination, predicted loan delinquency, and predicted
loan default) to the actual loan approval status and actual loan
default statistics in the training data set. Iteratively adjusting
the parameters of the machine learning algorithm is referred to as
"training" the machine learning algorithm. For example, training
the machine learning algorithm may be as described in reference to
the risk profile generator (107) depicted in FIG. 1 above.
[0035] In Step 204, subsequent to the training phase of the
adaptively-determined matching algorithm the adaptively-determined
matching algorithm is used to generate the risk profile of a
particular business entity based on the BMA data of the particular
business entity. In one or more embodiments, this particular
business entity is one of the business entities whose BMA data are
included in the training data set of the adaptively-determined
matching algorithm. In such embodiments, the risk profile generated
in the Step 204 is a updated version of a previous risk profile of
this particular business entity that was used as part of the
training set in the Step 203. In one or more embodiments, this
particular business entity is separate from those other business
entities whose BMA data are included in the training data set of
the adaptively-determined matching algorithm.
[0036] In Step 205, a determination is made as to whether the
particular business entity uses the risk profile to apply for a
loan. If the determination is YES, i.e., the particular business
entity submit a loan application based on the risk profile
generated in Step 204, the method returns to Step 202 where loan
approval status and any subsequent loan default statistic are added
to the training data set of the adaptively-determined matching
algorithm. If the determination is NO, i.e., the particular
business entity has not submitted any loan application based on the
risk profile generated in Step 204, the method proceeds to Step
206.
[0037] In Step 206, a loan proposal is generated based on similar
risk profiles of a group of business entities. In one or more
embodiments, a cluster of similar risk profiles are extracted from
a risk profile collection using a pre-determined clustering
algorithm and based on a pre-determined similarity measure.
Accordingly, a loan proposal is generated based on the cluster of
similar risk profiles. For example, the loan proposal may include a
range of loan amounts, interest rate terms, maturity time period,
borrower covenants, and other conventional financial parameters of
a loan. In one or more embodiments, a statistical return for a
lender is computed for the loan proposal based on characteristics
(e.g., probability of default, probability of non-default, etc.
each represented by a number score, a percentage score, a letter
score, etc.) of the similar risk profiles in the cluster. For
example, an effective average rate of return for a simple example
loan proposal may be computed by deducting a defaulted loan amount
multiplied by the probability of default from the anticipated
interest collection of a non-defaulted loan amount multiplied by a
simple fixed rate and the probability of non-default over the
maturity time period.
[0038] In one or more embodiments, the loan proposal is presented
to one or more lenders and the group of business entities
corresponding to the cluster of similar risk profiles. For example,
a lender may decide to offer a loan program based on the loan
proposal. In another example, the group of business entities may
jointly request a loan program from a lender based on the loan
proposal.
[0039] In Step 207, a target risk profile specified by one or more
lenders may be matched to business entities sharing similar risk
profiles. In one or more embodiments, one or more clusters of
similar risk profiles are extracted from a risk profile collection
using a pre-determined clustering algorithm and based on a
pre-determined similarity measure. In addition, at least one of
these clusters is selected as being similar to the target risk
profile. Accordingly, a list of business entities corresponding to
the selected at least one cluster are presented to the one or more
lenders. For example, a lender may decide to offer a loan program
based on the target risk profile and market the loan program to the
business entities on the list.
[0040] FIG. 3 shows an example flow (300) of generating a risk
profile based on third party business management application data
for instant lending decision in accordance with one or more
embodiments of the invention. Specifically, the flow (300) uses
business management application (BMA) data to build a model (303)
to predict delinquent behavior with a training data set. As shown
in FIG. 3, the flow (300) uses both user-entered data and
usage/behavioral data of the BMA data (301) to predict whether a
company has defaulted on a loan or has been past due at some point
during the life of the loan. The training data set includes a large
number (e.g., hundreds) of companies for whom historical delinquent
status (302) on a loan are known. Further, a large number of
user-entered data and usage/behavioral data (e.g., over one
hundred) are included for each company in the training set.
[0041] A rule ensemble algorithm is used to build the predictive
model (303) that is used to score a company on its likelihood of
exhibiting delinquent behavior. A "rules ensemble" is a particular
form of the machine learning methodology referred to as
"ensembling," where multiple simple models (base learners) are
combined into one complex model to improve accuracy. This type of
model can be described as an additive expansion of the form
F(x)=a.sub.0+a.sub.1*b.sub.1(x)+a.sub.2*b.sub.2(x)+ . . .
+a.sub.M*b.sub.M(x) where the b.sub.j(x)'s are the base-learners
and x is a vector [x.sub.1, x.sub.2, . . . x.sub.N] representing
the BMA data items (301). As noted above, N is a large number, such
as a number over one hundred.
[0042] In the case of a rules ensemble, the b.sub.j(x) terms are
conjunctive rules of the form "if x.sub.1>22 and x.sub.2>27
then 1 else 0" or linear functions of a single variable--e.g.,
b.sub.j(x)=x.sub.j. Using base-learners of this type is efficient
because they constitute easily interpretable statements about
attributes x.sub.j. They also preserve the desirable
characteristics of Decision Trees such as efficient handling of
categorical attributes, robustness to outliers in the distribution
of x, etc.
[0043] The example rules ensemble used in the flow (300) builds a
model (303), represented as F(x), in a three-step process: [0044]
a. Build a tree ensemble (one where the b.sub.j(x)'s are decision
trees), [0045] b. Generate candidate rules from the tree ensemble,
and [0046] c. Fit coefficients a.sub.j via regularized
regression.
[0047] The BMA data items are categories into several types of
variables and are evaluated to see which are most predictive of
default risk. These variable types include: [0048] a. Raw QBO
user-entered data (e.g., transactions, number of customers, . . .
), [0049] b. BMA usage behavior (e.g., browser used, number of
logins, length of time a QBO customer, . . . ), [0050] c. Computed
financial-health variables (e.g., net worth, EBITDA, inventory days
turnover, . . . ), and [0051] d. Summary data (e.g., total capital
dollar amount coming in to the company, total dollar amount going
out of the company, number of distinct vendors paid in last 12
months, . . . ).
[0052] For example, the following BMA data items are selected from
the above variable types as the most predictive power (based on the
training data set): [0053] a. Current ratio (current assets/current
liabilities), [0054] b. Year-over-year sales growth, [0055] c.
Number of online banking automatic downloads in a given month,
[0056] d. Number of transactions with money leaving the company
(e.g., bills paid) in a given month, [0057] e. Whether the company
is a current BMA subscriber or not, and [0058] f. Whether the
company is a customer for financial supplies (e.g., checks,
accounting forms, etc.) or not.
[0059] The output result of the model (303) includes a risk score
(313) from 0 to 1 that may be interpreted as the probability that
the company may default on a loan, the probability that the company
may be delinquent for one or more payments, and/or the probability
the company may be approved by a particular lender. Specifically,
the risk score (313) of a particular company is generated by using
the numerous BMA data items (311) of the particular company as
input variables of the model (303). The risk score (313) may be
used in a number of ways: [0060] a. Kept in its raw, continuous
format to be used in conjunction with other data to make a lending
decision by a lender, [0061] b. By trading off the relative "cost"
of incorrectly categorizing a business as risky when it is not,
versus incorrectly categorizing a business as not risky when it is,
a break point maybe determined where a company above that point is
categorized as risky and below is categorized as not risky.
Similarly, a number of breakpoints may be determined to create
tiers for low, medium, and high risk companies.
[0062] The risk score (313) may be given to a lender directly or
given to the particular company as a borrower and used at the
borrower's discretion when applying for a loan from the lender. In
addition, the risk score (313) may be dynamically update in real
time during the life of the loan as a leverage for the borrower to
negotiate better terms with the lender if the borrower's business
is doing well. Further, the risk score (313) may be dynamically
update in real time during the life of the loan for the lender to
measure the ongoing risk of the loan with respect to the borrower's
business reflected by the BMA data of the borrower.
[0063] Embodiments of the invention may be implemented on virtually
any type of computing system regardless of the platform being used.
For example, the computing system may be one or more mobile devices
(e.g., laptop computer, smart phone, personal digital assistant,
tablet computer, or other mobile device), desktop computers,
servers, blades in a server chassis, or any other type of computing
device or devices that includes at least the minimum processing
power, memory, and input and output device(s) to perform one or
more embodiments of the invention. For example, as shown in FIG. 4,
the computing system (400) may include one or more computer
processor(s) (402), associated memory (404) (e.g., random access
memory (RAM), cache memory, flash memory, etc.), one or more
storage device(s) (406) (e.g., a hard disk, an optical drive such
as a compact disk (CD) drive or digital versatile disk (DVD) drive,
a flash memory stick, etc.), and numerous other elements and
functionalities. The computer processor(s) (402) may be an
integrated circuit for processing instructions. For example, the
computer processor(s) may be one or more cores, or micro-cores of a
processor. The computing system (400) may also include one or more
input device(s) (410), such as a touchscreen, keyboard, mouse,
microphone, touchpad, electronic pen, or any other type of input
device. Further, the computing system (400) may include one or more
output device(s) (408), such as a screen (e.g., a liquid crystal
display (LCD), a plasma display, touchscreen, cathode ray tube
(CRT) monitor, projector, or other display device), a printer,
external storage, or any other output device. One or more of the
output device(s) may be the same or different from the input
device. The computing system (400) may be connected to a network
(412) (e.g., a local area network (LAN), a wide area network (WAN)
such as the Internet, mobile network, or any other type of network)
via a network interface connection (not shown). The input and
output device(s) may be locally or remotely (e.g., via the network
(412)) connected to the computer processor(s) (402), memory (404),
and storage device(s) (406). Many different types of computing
systems exist, and the aforementioned input and output device(s)
may take other forms.
[0064] Software instructions in the form of computer readable
program code to perform embodiments of the invention may be stored,
in whole or in part, temporarily or permanently, on a
non-transitory computer readable medium such as a CD, DVD, storage
device, a diskette, a tape, flash memory, physical memory, or any
other computer readable storage medium. Specifically, the software
instructions may correspond to computer readable program code that
when executed by a processor(s), is configured to perform
embodiments of the invention.
[0065] Further, one or more elements of the aforementioned
computing system (400) may be located at a remote location and
connected to the other elements over a network (412). Further,
embodiments of the invention may be implemented on a distributed
system having a plurality of nodes, where each portion of the
invention may be located on a different node within the distributed
system. In one embodiment of the invention, the node corresponds to
a distinct computing device. Alternatively, the node may correspond
to a computer processor with associated physical memory. The node
may alternatively correspond to a computer processor or micro-core
of a computer processor with shared memory and/or resources.
[0066] While the invention has been described with respect to a
limited number of embodiments, those skilled in the art, having
benefit of this disclosure, will appreciate that other embodiments
can be devised which do not depart from the scope of the invention
as disclosed herein. Accordingly, the scope of the invention should
be limited only by the attached claims.
* * * * *