U.S. patent application number 14/168306 was filed with the patent office on 2014-05-29 for using commercial share of wallet in private equity investments.
This patent application is currently assigned to American Express Travel Related Services Company, Inc.. The applicant listed for this patent is American Express Travel Related Services Company, Inc.. Invention is credited to Arnab Biswas, Kathleen Haggerty, Charles Christopher Lyon, Benedict O. Okoh, Robert E. Phelan, Jon Kevin Ruterman, Geraldine A. Turner, Chao M. Yuan.
Application Number | 20140149179 14/168306 |
Document ID | / |
Family ID | 46325838 |
Filed Date | 2014-05-29 |
United States Patent
Application |
20140149179 |
Kind Code |
A1 |
Haggerty; Kathleen ; et
al. |
May 29, 2014 |
USING COMMERCIAL SHARE OF WALLET IN PRIVATE EQUITY INVESTMENTS
Abstract
Commercial size of spending wallet ("CSoSW") is the total
business spend of a business including cash but excluding bartered
items. Commercial share of wallet ("CSoW") is the portion of the
spending wallet that is captured by a particular financial company.
A modeling approach utilizes various data sources to provide
outputs that describe a company's spend capacity. Private equity
firms and other investors of small businesses can use the
CSoW/CSoSW modeling approach to more accurately evaluate small and
privately held companies, both during investment and for evaluating
prospective investments. Over-the-counter securities trading
systems can also use this modeling approach to provide more
accurate information and/or rankings of listed companies to their
customers.
Inventors: |
Haggerty; Kathleen; (Staten
Island, NY) ; Lyon; Charles Christopher; (Phoenix,
AZ) ; Okoh; Benedict O.; (New York, NY) ;
Phelan; Robert E.; (Mendham, NJ) ; Ruterman; Jon
Kevin; (Glendale, AZ) ; Turner; Geraldine A.;
(Sandy Hook, VA) ; Yuan; Chao M.; (Montclair,
NJ) ; Biswas; Arnab; (Calcutta, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
American Express Travel Related Services Company, Inc. |
New York |
NY |
US |
|
|
Assignee: |
American Express Travel Related
Services Company, Inc.
New York
NY
|
Family ID: |
46325838 |
Appl. No.: |
14/168306 |
Filed: |
January 30, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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|
13899403 |
May 21, 2013 |
8682770 |
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14168306 |
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|
13209035 |
Aug 12, 2011 |
8478673 |
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|
13899403 |
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|
12833708 |
Jul 9, 2010 |
8024245 |
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13209035 |
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11497521 |
Aug 2, 2006 |
7822665 |
|
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12833708 |
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11169588 |
Jun 30, 2005 |
7912770 |
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11497521 |
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10978298 |
Oct 29, 2004 |
7788147 |
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11169588 |
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60704428 |
Aug 2, 2005 |
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Current U.S.
Class: |
705/7.29 |
Current CPC
Class: |
G06Q 40/00 20130101;
G06Q 30/0201 20130101; G06Q 40/08 20130101; G06Q 40/06 20130101;
G06Q 40/025 20130101 |
Class at
Publication: |
705/7.29 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A method comprising: modeling, by a financial analysis
computer-based system, to create a model of industry spending
patterns, wherein the computer-based system comprises a processor
and a tangible, non-transitory memory; estimating, by the
computer-based system, a commercial size of spending wallet of the
company, wherein the commercial size of spending wallet comprises
the company's cost of goods sold plus cardable operating expenses
of the company, and wherein the cardable operating expenses of the
company are identified from total operating expenses of the
company; and outputting, by the computer-based system, the
estimated commercial size of spending wallet.
2. The method of claim 1, wherein the modeling further comprises
modeling industry spending patterns.
3. The method of claim 1, wherein the modeling further comprises
modeling industry spending patterns using at least one of
individual corporate data and aggregate corporate data.
4. The method of claim 1, wherein the modeling further comprises
modeling industry spending patterns using individual corporate data
and aggregate corporate data.
5. The method of claim 1, further comprising developing a strategy
for a private equity investment in the company based on the
commercial size of spending wallet of the company.
6. The method of claim 5, where the developing comprises deciding
whether to make a private equity investment in the company.
7. The method of claim 5, where the developing comprises deciding
whether to retain the company in a private equity portfolio.
8. The method of claim 5, where the developing comprises deciding
whether to make additional investment in the company.
9. The method of claim 5, wherein the developing further comprises
deciding how to structure the investment in the company.
10. The method of claim 1, further comprising using the commercial
size of spending wallet of the company to corroborate other company
data.
11. The method of claim 10, wherein the using comprises using the
commercial size of spending wallet of the company to corroborate
market data.
12. The method of claim 10, wherein the using comprises using the
commercial size of spending wallet of the company to corroborate
financial data.
13. The method of claim 10, wherein the using comprises using the
commercial size of spending wallet of the company to corroborate
industry data.
14. The method of claim 10, wherein the using comprises using the
commercial size of spending wallet of the company to corroborate
analyst data.
15. The method of claim 1, further comprising using the commercial
size of spending wallet of the company to evaluate the company.
16. The method of claim 1, wherein the estimating is based on, at
least, a model of industry spending patterns.
17. The method of claim 1, further comprising determining, by the
computer-based system, the total operating expenses of the
company.
18. The method of claim 1, further comprising identifying, by the
computer-based system, the cardable operating expenses of the
company from the total operating expenses of the company.
19. A system comprising: a processor for estimating spending
capacity of a company; a tangible, non-transitory memory
communicating with the processor, the tangible, non-transitory
memory having instructions stored thereon that, in response to
execution by the processor, cause the processor to perform
operations; a modeling module in communication with the processor
and configured to create a model of industry spending patterns,
wherein the computer-based system comprises a processor and a
tangible, non-transitory memory; an estimating module in
communication with the processor and configured to estimate a
commercial size of spending wallet of the company, wherein the
commercial size of spending wallet comprises the company's cost of
goods sold plus cardable operating expenses of the company, and
wherein the cardable operating expenses of the company are
identified from total operating expenses of the company; and an
output module in communication with the processor and configured to
output the estimated commercial size of spending wallet.
20. A tangible, non-transitory computer readable storage medium
comprising instructions stored thereon that, in response to
execution by a processor for estimating spending capacity of a
company, cause the processor to perform operations of: modeling, by
the computer-based system, to create a model of industry spending
patterns, wherein the computer-based system comprises a processor
and a tangible, non-transitory memory; estimating, by the
computer-based system, a commercial size of spending wallet of the
company, wherein the commercial size of spending wallet comprises
the company's cost of goods sold plus cardable operating expenses
of the company, and wherein the cardable operating expenses of the
company are identified from total operating expenses of the
company; and outputting, by the computer-based system, the
estimated commercial size of spending wallet.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of, claims priority to
and the benefit of, U.S. patent application Ser. No. 13/899,403
filed on May 21, 2013 and entitled "USING COMMERCIAL SHARE OF
WALLET IN PRIVATE EQUITY INVESTMENTS." The '403 application is a
continuation of, claims priority to and the benefit of, U.S. patent
application Ser. No. 13/209,035 filed on Aug. 12, 2011 and entitled
"USING COMMERCIAL SHARE OF WALLET IN PRIVATE EQUITY INVESTMENTS,"
which issued as U.S. Pat. No. 8,478,673 on Jul. 2, 2013. The '673
patent is a continuation of U.S. patent application Ser. No.
12/833,708 filed on Jul. 9, 2010 and entitled "USING COMMERCIAL
SHARE OF WALLET IN PRIVATE EQUITY INVESTMENTS," which issued as
U.S. Pat. No. 8,024,245 on Sep. 20, 2011. The '245 patent is a
continuation of, claims priority to and the benefit of, U.S. patent
application Ser. No. 11/497,521 filed on Aug. 2, 2006 and entitled
"USING COMMERCIAL SHARE OF WALLET IN PRIVATE EQUITY INVESTMENTS,"
which issued as U.S. Pat. No. 7,822,665 on Oct. 26, 2010. The '665
patent claims the benefit of U.S. Provisional Application No.
60/704,428, filed Aug. 2, 2005 and entitled "METHOD AND SYSTEM FOR
DETERMINING COMMERCIAL SHARE OF WALLET." The '665 patent is also a
continuation-in-part of, claims priority to and the benefit of,
U.S. patent application Ser. No. 11/169,588, filed Jun. 30, 2005
and entitled "METHOD AND APPARATUS FOR CONSUMER INTERACTION BASED
ON SPEND CAPACITY," which issued as U.S. Pat. No. 7,912,770 on Mar.
22, 2011. The '665 patent is also a continuation-in-part of, claims
priority to and the benefit of, U.S. patent application Ser. No.
10/978,298, filed Oct. 29, 2004 and entitled "METHOD AND APPARATUS
FOR ESTIMATING THE SPEND CAPACITY OF CONSUMERS," which issued as
U.S. Pat. No. 7,788,147 on Aug. 31, 2010. All the aforementioned
applications are incorporated by reference herein in their
entirety.
[0002] This application also shares common subject matter with and
is related to the following commonly owned, co-pending U.S. Patent
Applications, each of which is incorporated herein by
reference:
[0003] a. U.S. patent application Ser. No. 11/394,165, filed Mar.
31, 2006, entitled "Using Commercial Share of Wallet to Determine
Insurance Risk";
[0004] b. U.S. patent application Ser. No. 11/394,166, filed Mar.
31, 2006, entitled "Using Commercial Share of Wallet to Manage
Vendors";
[0005] c. U.S. patent application Ser. No. 11/394,169, filed Mar.
31, 2006, entitled "Using Commercial Share of Wallet to Rate
Business Prospects";
[0006] d. U.S. patent application Ser. No. 11/394,206, filed Mar.
31, 2006, entitled "Using Commercial Share of Wallet to Manage
Investments";
[0007] e. U.S. patent application Ser. No. 11/394,217, filed Mar.
31, 2006, entitled "Using Commercial Share of Wallet to Make
Lending Decisions";
[0008] f. U.S. patent application Ser. No.11/497,563, filed Aug. 2,
2006, entitled "Method and System for Determining Commercial Share
of Wallet";
[0009] g. U.S. patent application Ser. No. 11/497,529, filed Aug.
2, 2006, entitled "Using Commercial Share to Analyze Vendors in
Online Marketplaces";
[0010] h. U.S. patent application Ser. No. 11/497,530, filed Aug.
2, 2006, entitled "Using Commercial Share of Wallet in Financial
Databases";
[0011] i. U.S. patent application Ser. No. 11/497,562, filed Aug.
2, 2006, entitled "Using Commercial Share of Wallet to Rate
Investments"; and
[0012] j. U.S. patent application Ser. No. 11/497,527, filed Aug.
2, 2006, entitled "Using Commercial Share of Wallet to Compile
Marketing Company Lists".
BACKGROUND OF THE INVENTION
[0013] 1. Field of the Invention
[0014] This disclosure generally relates to financial data
processing, and in particular it relates to credit scoring,
customer profiling, customer product offer targeting, and
commercial credit behavior analysis and modeling.
[0015] 2. Background Art
[0016] For the purposes of this disclosure, middle market
commercial entities, service establishments, franchises, small
business corporations and partnerships as well as business sole
proprietorships will be referred to as businesses or companies.
These terms also includes principals of a business entity. It is
axiomatic that consumers and/or businesses will tend to spend more
when they have greater purchasing power. The capability to
accurately estimate a business's or a consumer's spend capacity
could therefore allow a financial institution (such as a credit
company, lender or any consumer or business services companies) to
better target potential prospects and identify any opportunities to
increase business to business ("B2B") or business to consumer
("B2C") transaction volumes, without an undue increase in the risk
of defaults. Attracting additional consumer and/or commercial
spending in this manner, in turn, would increase such financial
institution's revenues, primarily in the form of an increase in
transaction fees and interest payments received. Consequently, a
model that can accurately estimate purchasing power is of paramount
interest to many financial institutions and other financial
services companies.
[0017] A limited ability to estimate spend behavior for goods and
services that a business or consumer purchases has previously been
available. A financial institution can, for example, simply monitor
the balances of its own customers' accounts. When a credit balance
is lowered, the financial institution could then assume that the
corresponding customer now has greater purchasing power. However,
it is often difficult to confirm whether the lowered balance is the
result of a balance transfer to another account. Such balance
transfers represent no increase in the customer's capacity to
spend, and so this simple model of customer behavior has its
flaws.
[0018] In order to achieve a complete picture of any customer's
purchasing ability, one must examine in detail the full range of a
customer's financial accounts, including credit accounts, checking
and savings accounts, investment portfolios, and the like. However,
the vast majority of customers do not maintain all such accounts
with the same financial institution and the access to detailed
financial information from other financial institutions is
restricted by privacy laws, disclosure policies and security
concerns.
[0019] There is limited and incomplete consumer information from
credit bureaus and the like at the aggregate and individual
consumer levels. Since balance transfers are nearly impossible to
consistently identify from the face of such records, this
information has not previously been enough to obtain accurate
estimates of a consumer's actual spending ability.
[0020] Similarly, it would be useful for a financial institution to
identify spend availability for corporate consumers, such as
businesses and/or a principal of a business entity. Such an
identification would allow the financial institution to accurately
target the corporate businesses and/or principals most likely to
have spend availability, and those most likely to increase their
plastic spend on transactional accounts related to the financial
institution. However, there is also limited data on corporate spend
information, and identifying and predicting the size and share of a
corporate wallet is difficult.
[0021] Accordingly, there is a need for a method and apparatus for
modeling individual and corporate consumer spending behavior which
addresses certain problems of existing technologies.
BRIEF SUMMARY OF THE INVENTION
[0022] A method for modeling customer behavior can be applied to
both potential and actual customers (who may be individual
consumers or businesses) to determine their spend over previous
periods of time (sometimes referred to herein as the customer's
size of wallet) from tradeline data sources. The share of wallet by
tradeline or account type may also be determined. At the highest
level, the size of wallet is represented by a consumer's or
business' total aggregate spending and the share of wallet
represents how the customer uses different payment instruments.
[0023] In various embodiments, a method and apparatus for modeling
consumer or business behavior includes receiving individual and
aggregated customer data for a plurality of different customers.
The customer data may include, for example, time series tradeline
data, business financial statement data, business or consumer panel
data, and internal customer data. One or more models of consumer or
business spending patterns are then derived based on the data for
one or more categories of consumer or business. Categories may be
based on spending levels, spending behavior, tradeline user and
type of tradeline.
[0024] In various embodiments, a method and apparatus for
estimating the spending levels of an individual consumer is next
provided, which relies on the models of consumer behavior above.
Size of wallet calculations for individual prospects and customers
are derived from credit bureau data sources to produce outputs
using the models.
[0025] Balance transfers into credit accounts are identified based
on tradeline data according to various algorithms, and any
identified balance transfer amount is excluded from the spending
calculation. The identification of balance transfers enables more
accurate utilization of balance data to reflect spending.
[0026] When spending levels are reliably identified in this manner,
customers may be categorized to more effectively manage the
customer relationship and increase the profitability therefrom. For
example, share of wallet scores can be used as a parameter for
determining whether or not to guarantee a check. The share of
wallet can be used to differentiate between a low-risk customer who
is writing more checks because his income has probably increased,
and a high-risk customer who is writing more checks without a
corresponding increase in income or spend.
[0027] Similarly, company financial statement data can be utilized
to identify and calculate the total business spend of a company
that could be transacted using a commercial credit card. A
spend-like regression model can then be developed to estimate
annual commercial size of spending wallet values for customers and
prospects of a credit network. This approach relies on the High
Balance Reunderwriting Unit ("HBRU") database of
commercially-underwritten businesses and the publicly available tax
statistics section of the IRS website, among other sources, to
obtain accurate financial statement data for companies across
various industries. Once the size of a company's spending wallet
has been determined, the cardable share of the company's wallet may
also be estimated.
[0028] Private equity firms and other investors of small businesses
can use this information to more accurately evaluate small and
privately held companies, both during investment and for evaluating
prospective investments. Over-the-counter securities trading
systems can also use this information to provide more accurate
information and/or rankings of listed companies to their
customers.
BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES
[0029] Further aspects of the present disclosure will be more
readily appreciated upon review of the detailed description of its
various embodiments, described below, when taken in conjunction
with the accompanying drawings, of which:
[0030] FIG. 1 is a block diagram of an exemplary financial data
exchange network over which the processes of the present disclosure
may be performed;
[0031] FIG. 2 is a flowchart of an exemplary consumer modeling
process performed by the financial server of FIG. 1;
[0032] FIG. 3 is a diagram of exemplary categories of consumers
examined during the process of FIG. 2;
[0033] FIG. 4 is a diagram of exemplary subcategories of consumers
modeled during the process of FIG. 2;
[0034] FIG. 5 is a diagram of financial data used for model
generation and validation according to the process of FIG. 2;
[0035] FIG. 6 is a flowchart of an exemplary process for estimating
the spend ability of a consumer, performed by the financial server
of FIG. 1;
[0036] FIG. 7-10 are exemplary timelines showing the rolling time
periods for which individual customer data is examined during the
process of FIG. 6; and
[0037] FIG. 11-19 are tables showing exemplary results and outputs
of the process of FIG. 6 against a sample consumer population.
[0038] FIG. 20 is a flowchart of a method for determining common
characteristics across a particular category of customers according
to an embodiment of the present invention.
[0039] FIG. 21 is a flowchart of a method for estimating commercial
size of spending wallet ("SoSW") according to an embodiment of the
present invention.
[0040] FIG. 22 is a sample financial statement that may be analyzed
using the method of FIG. 21.
[0041] FIG. 23 is a chart displaying the distribution of commercial
SoSW among OSBN HBRU businesses.
[0042] FIG. 24 is a chart displaying the median and mean commercial
SoSW by industry.
[0043] FIG. 25 is a chart displaying a sample share of wallet
distribution among HBRU accounts.
[0044] FIG. 26 is a table describing the relationship between a
commercial SoSW model according to an embodiment of the invention
and business variables.
[0045] FIG. 27 is a graph comparing actual commercial SoSW results
to predicted commercial SoSW estimates according to an embodiment
of the present invention.
[0046] FIG. 28 is a graph comparing a commercial SoSW model
according to an embodiment of the present invention to a perfectly
random prediction.
[0047] FIG. 29 is a chart illustrating customer-level relationship
classifications according to an embodiment of the present
invention.
[0048] FIG. 30 is a chart illustrating the active number of OSBN
accounts by quintile according to an embodiment of the present
invention.
[0049] FIG. 31 is a table displaying customer counts in a scored
output file according to an embodiment of the present
invention.
[0050] FIG. 32 is a block diagram of an exemplary computer system
useful for implementing the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0051] While specific configurations and arrangements are
discussed, it should be understood that this is done for
illustrative purposes only. A person skilled in the pertinent art
will recognize that other configurations and arrangements can be
used without departing from the spirit and scope of the present
invention. It will be apparent to a person skilled in the pertinent
art that this invention can also be employed in a variety of other
applications.
[0052] In an aspect of this invention, the term "business" will
refer to non-publicly traded business entities, such as middle
market commercial entities, franchises, small business corporations
and partnerships, and sole proprietorships, as well as principals
of these business entities. One of skill in the pertinent art will
recognize that the present invention may be used in reference to
consumers, businesses, and publicly traded companies without
departing from the spirit and scope of the present invention.
[0053] As used herein, the following terms shall have the following
meanings. A consumer refers to an individual consumer and/or a
small business. A trade or tradeline refers to a credit or charge
vehicle issued to an individual customer by a credit grantor. Types
of tradelines include, for example and without limitation, bank
loans, credit card accounts, retail cards, personal lines of credit
and car loans/leases. For purposes here, use of the term credit
card shall be construed to include charge cards except as
specifically noted. Tradeline data describes the customer's account
status and activity, including, for example, names of companies
where the customer has accounts, dates such accounts were opened,
credit limits, types of accounts, balances over a period of time
and summary payment histories. Tradeline data is generally
available for the vast majority of actual consumers. Tradeline
data, however, does not include individual transaction data, which
is largely unavailable because of consumer privacy protections.
Tradeline data may be used to determine both individual and
aggregated consumer spending patterns, as described herein.
[0054] Consumer panel data measures consumer spending patterns from
information that is provided by, typically, millions of
participating consumer panelists. Such consumer panel data is
available through various consumer research companies, such as
comScore Networks, Inc. of Reston, Va. Consumer panel data may
typically include individual consumer information such as credit
risk scores, credit card application data, credit card purchase
transaction data, credit card statement views, tradeline types,
balances, credit limits, purchases, balance transfers, cash
advances, payments made, finance charges, annual percentage rates
and fees charged. Such individual information from consumer panel
data, however, is limited to those consumers who have participated
in the consumer panel, and so such detailed data may not be
available for all consumers.
[0055] Although embodiments of the invention herein may be
described as relating to individual consumers, one of skill in the
pertinent art(s) will recognize that they can also apply to small
businesses and organizations or principals thereof without
departing from the spirit and scope of the present invention.
I. Consumer Panel Data and Model Development/Validation
[0056] Technology advances have made it possible to store,
manipulate and model large amounts of time series data with minimal
expenditure on equipment. As will now be described, a financial
institution may leverage these technological advances in
conjunction with the types of consumer data presently available in
the marketplace to more readily estimate the, spend capacity of
potential and actual customers. A reliable capability to assess the
size of a consumer's wallet is introduced in which aggregate time
series and raw tradeline data are used to model consumer behavior
and attributes, and identify categories of consumers based on
aggregate behavior. The use of raw trade-line time series data, and
modeled consumer behavior attributes, including but not limited to,
consumer panel data and internal consumer data, allows actual
consumer spend behavior to be derived from point in time balance
information.
[0057] In addition, the advent of consumer panel data provided
through internet channels provides continuous access to actual
consumer spend information for model validation and refinement.
Industry data, including consumer panel information having consumer
statement and individual transaction data, may be used as inputs to
the model and for subsequent verification and validation of its
accuracy. The model is developed and refined using actual consumer
information with the goals of improving the customer experience and
increasing billings growth by identifying and leveraging increased
consumer spend opportunities.
[0058] A credit provider or other financial institution may also
make use of internal proprietary customer data retrieved from its
stored internal financial records. Such internal data provides
access to even more actual customer spending information, and may
be used in the development, refinement and validation of aggregated
consumer spending models, as well as verification of the models'
applicability to existing individual customers on an ongoing
basis.
[0059] While there has long been market place interest in
understanding spend to align offers with consumers and assign
credit line size, the holistic approach of using a size of wallet
calculation across customers' lifecycles (that is, acquisitions
through collections) has not previously been provided. The various
data sources outlined above provide the opportunity for unique
model logic development and deployment, and as described in more
detail in the following, various categories of consumers may be
readily identified from aggregate and individual data. In certain
embodiments of the processes disclosed herein, the models may be
used to identify specific types of consumers, nominally labeled
`transactors` and `revolvers,` based on aggregate spending
behavior, and to then identify individual customers and prospects
that fall into one of these categories. Consumers falling into
these categories may then be offered commensurate purchasing
incentives based on the model's estimate of consumer spending
ability.
[0060] Referring now to FIGS. 1-32, wherein similar components of
the present disclosure are referenced in like manner, various
embodiments of a method and system for estimating the purchasing
ability of consumers will now be described in detail.
[0061] Turning now to FIG. 1, there is depicted an exemplary
computer network 100 over which the transmission of the various
types of consumer data as described herein may be accomplished,
using any of a variety of available computing components for
processing such data in the manners described below. Such
components may include an institution computer 102, which may be a
computer, workstation or server, such as those commonly
manufactured by IBM, and operated by a financial institution or the
like. The institution computer 102, in turn, has appropriate
internal hardware, software, processing, memory and network
communication components that enables it to perform the functions
described here, including storing both internally and externally
obtained individual or aggregate consumer data in appropriate
memory and processing the same according to the processes described
herein using programming instructions provided in any of a variety
of useful machine languages.
[0062] The institution computer 102 may in turn be in operative
communication with any number of other internal or external
computing devices, including for example components 104, 106, 108,
and 110, which may be computers or servers of similar or compatible
functional configuration. These components 104-110 may gather and
provide aggregated and individual consumer data, as described
herein, and transmit the same for processing and analysis by the
institution computer 102. Such data transmissions may occur for
example over the Internet or by any other known communications
infrastructure, such as a local area network, a wide area network,
a wireless network, a fiber-optic network, or any combination or
interconnection of the same. Such communications may also be
transmitted in an encrypted or otherwise secure format, in any of a
wide variety of known manners.
[0063] Each of the components 104-110 may be operated by either
common or independent entities. In one exemplary embodiment, which
is not to be limiting to the scope of the present disclosure, one
or more such components 104-110 may be operated by a provider of
aggregate and individual consumer tradeline data, an example of
which includes services provided by Experian Information Solutions,
Inc. of Costa Mesa, Calif. ("Experian"). Tradeline level data
preferably includes up to 24 months or more of balance history and
credit attributes captured at the tradeline level, including
information about accounts as reported by various credit grantors,
which in turn may be used to derive a broad view of actual
aggregated consumer behavioral spending patterns.
[0064] Alternatively, or in addition thereto, one or more of the
components 104-110 may likewise be operated by a provider of
individual and aggregate consumer panel data, such as commonly
provided by comScore Networks, Inc. of Reston, Va. ("comScore").
Consumer panel data provides more detailed and specific consumer
spending information regarding millions of consumer panel
participants, who provide actual spend data to collectors of such
data in exchange for various inducements. The data collected may
include any one or more of credit risk scores, online credit card
application data, online credit card purchase transaction data,
online credit card statement views, credit trade type and credit
issuer, credit issuer code, portfolio level statistics, credit
bureau reports, demographic data, account balances, credit limits,
purchases, balance transfers, cash advances, payment amounts,
finance charges, annual percentage interest rates on accounts, and
fees charged, all at an individual level for each of the
participating panelists. In various embodiments, this type of data
is used for model development, refinement and verification. This
type of data is further advantageous over tradeline level data
alone for such purposes, since such detailed information is not
provided at the tradeline level. While such detailed consumer panel
data can be used alone to generate a model, it may not be wholly
accurate with respect to the remaining marketplace of consumers at
large without further refinement. Consumer panel data may also be
used to generate aggregate consumer data for model derivation and
development.
[0065] Additionally, another source of inputs to the model may be
internal spend and payment history of the institution's own
customers. From such internal data, detailed information at the
level of specificity as the consumer panel data may be obtained and
used for model development, refinement and validation, including
the categorization of consumers based on identified transactor and
revolver behaviors.
[0066] Turning now to FIG. 2, there is depicted a flowchart of an
exemplary process 200 for modeling aggregate consumer behavior in
accordance with the present disclosure. The process 200 commences
at step 202 wherein individual and aggregate consumer data,
including time-series tradeline data, consumer panel data and
internal customer financial data, is obtained from any of the data
sources described previously as inputs for consumer behavior
models. In certain embodiments, the individual and aggregate
consumer data may be provided in a variety of different data
formats or structures and consolidated to a single useful format or
structure for processing.
[0067] Next, at step 204, the individual and aggregate consumer
data is analyzed to determine consumer spending behavior patterns.
One of ordinary skill in the art will readily appreciate that the
models may include formulas that mathematically describe the
spending behavior of consumers. The particular formulas derived
will therefore highly depend on the values resulting from customer
data used for derivation, as will be readily appreciated. However,
by way of example only and based on the data provided, consumer
behavior may be modeled by first dividing consumers into categories
that may be based on account balance levels, demographic profiles,
household income levels or any other desired categories. For each
of these categories in turn, historical account balance and
transaction information for each of the consumers may be tracked
over a previous period of time, such as one to two years.
Algorithms may then be employed to determine formulaic descriptions
of the distribution of aggregate consumer information over the
course of that period of time for the population of consumers
examined, using any of a variety of known mathematical techniques.
These formulas in turn may be used to derive or generate one or
more models (step 206) for each of the categories of consumers
using any of a variety of available trend analysis algorithms. The
models may yield the following types of aggregated consumer
information for each category: average balances, maximum balances,
standard deviation of balances, percentage of balances that change
by a threshold amount, and the like.
[0068] Finally, at step 208, the derived models may be validated
and periodically refined using internal customer data and consumer
panel data from sources such as comScore. In various embodiments,
the model may be validated and refined over time based on
additional aggregated and individual consumer data as it is
continuously received by an institution computer 102 over the
network 100. Actual customer transaction level information and
detailed consumer information panel data may be calculated and used
to compare actual consumer spend amounts for individual consumers
(defined for each month as the difference between the sum of debits
to the account and any balance transfers into the account) and the
spend levels estimated for such consumers using the process 200
above. If a large error is demonstrated between actual and
estimated amounts, the models and the formulas used may be manually
or automatically refined so that the error is reduced. This allows
for a flexible model that has the capability to adapt to actual
aggregated spending behavior as it fluctuates over time.
[0069] As shown in the diagram 300 of FIG. 3, a population of
consumers for which individual and/or aggregated data has been
provided may be divided first into two general categories for
analysis, for example, those that are current on their credit
accounts (representing 1.72 million consumers in the exemplary data
sample size of 1.78 million consumers) and those that are
delinquent (representing 0.06 million of such consumers). In one
embodiment, delinquent consumers may be discarded from the
populations being modeled.
[0070] In further embodiments, the population of current consumers
is then subdivided into a plurality of further categories based on
the amount of balance information available and the balance
activity of such available data. In the example shown in the
diagram 300, the amount of balance information available is
represented by string of `+` `0` and `?` characters. Each character
represents one month of available data, with the rightmost
character representing the most current months and the leftmost
character representing the earliest month for which data is
available. In the example provided in FIG. 3, a string of six
characters is provided, representing the six most recent months of
data for each category. The `+" character represents a month in
which a credit account balance of the consumer has increased. The
"0" character may represent months where the account balance is
zero. The "?" character represents months for which balance data is
unavailable. Also provided in the diagram is the number of
consumers who fall in to each category and the percentage of the
consumer population represented by that sample.
[0071] In further embodiments, only certain categories of consumers
may be selected for modeling behavior. The selection may be based
on those categories that demonstrate increased spend on their
credit balances over time. However, it should be readily
appreciated that other categories can be used. FIG. 3 shows the
example of two categories 302 and 304 of selected consumers for
modeling. These groups show the availability of at least the three
most recent months of balance data and that the balances increased
in each of those months.
[0072] Turning now to FIG. 4, therein is depicted an exemplary
diagram 400 showing sub-categorization of categories 302 and 304 of
FIG. 3 that are selected for modeling. In the embodiment shown, the
sub-categories may include: consumers having a most recent credit
balance less than $400; consumers having a most recent credit
balance between $400 and $1600; consumers having a most recent
credit balance between $1600 and $5000; consumers whose most recent
credit balance is less than the balance of, for example, three
months ago; consumers whose maximum credit balance increase over,
for example, the last twelve months divided by the second highest
maximum balance increase over the same period is less than 2; and
consumers whose maximum credit balance increase over the last
twelve months divided by the second highest maximum balance
increase is greater than 2. It should be readily appreciated that
other subcategories can be used. Each of these sub-categories is
defined by their last month balance level. The number of consumers
from the sample population (in millions) and the percentage of the
population for each category are also shown in FIG. 4.
[0073] There may be a certain balance threshold established,
wherein if a consumer's account balance is too high, their behavior
may not be modeled, since such consumers are less likely to have
sufficient spending ability. Alternatively, or in addition thereto,
consumers having balances above such threshold may be
sub-categorized yet again, rather than completely discarded from
the sample. In the example shown in FIG. 4, the threshold value may
be $5000, and only those having particular historical balance
activity may be selected, i.e. those consumers whose present
balance is less than their balance three months earlier, or whose
maximum balance increase in the examined period meets certain
parameters. Other threshold values may also be used and may be
dependent on the individual and aggregated consumer data
provided.
[0074] As described in the foregoing, the models generated in the
process 200 may be derived, validated and refined using tradeline
and consumer panel data. An example of tradeline data 500 from
Experian and consumer panel data 502 from comScore are represented
in FIG. 5. Each row of the data 500, 502 represents the record of
one consumer and thousands of such records may be provided at a
time. The statement 500 shows the point-in-time balance of
consumers accounts for three successive months (Balance 1, Balance
2 and Balance 3). The data 502 shows each consumer's purchase
volume, last payment amount, previous balance amount and current
balance. Such information may be obtained, for example, by page
scraping the data (in any of a variety of known manners using
appropriate application programming interfaces) from an Internet
web site or network address at which the data 502 is displayed.
Furthermore, the data 500 and 502 may be matched by consumer
identity and combined by one of the data providers or another third
party independent of the financial institution. Validation of the
models using the combined data 500 and 502 may then be performed,
and such validation may be independent of consumer identity.
[0075] Turning now to FIG. 6, therein is depicted an exemplary
process 600 for estimating the size of an individual consumer's
spending wallet. Upon completion of the modeling of the consumer
categories above, the process 600 commences with the selection of
individual consumers or prospects to be examined (step 602). An
appropriate model derived during the process 200 will then be
applied to the presently available consumer tradeline information
in the following manner to determine, based on the results of
application of the derived models, an estimate of a consumer's size
of wallet. Each consumer of interest may be selected based on their
falling into one of the categories selected for modeling described
above, or may be selected using any of a variety of criteria.
[0076] The process 600 continues to step 604 where, for a selected
consumer, a paydown percentage over a previous period of time is
estimated for each of the consumer's credit accounts. In one
embodiment, the paydown percentage is estimated over the previous
three-month period of time based on available tradeline data, and
may be calculated according to the following formula:
Pay-down %=(The sum of the last three months payments from the
account)/(The sum of three month balances for the account based on
tradeline data).
The paydown percentage may be set to, for example, 2%, for any
consumer exhibiting less than a 5% paydown percentage, and may be
set to 100% if greater than 80%, as a simplified manner for
estimating consumer spending behaviors on either end of the paydown
percentage scale.
[0077] Consumers that exhibit less than a 50% paydown during this
period may be categorized as revolvers, while consumers that
exhibit a 50% paydown or greater may be categorized as transactors.
These categorizations may be used to initially determine what, if
any, purchasing incentives may be available to the consumer, as
described later below.
[0078] The process 600, then continues to step 606, where balance
transfers for a previous period of time are identified from the
available tradeline data for the consumer. The identification of
balance transfers are essential since, although tradeline data may
reflect a higher balance on a credit account over time, such higher
balance may simply be the result of a transfer of a balance into
the account, and are thus not indicative of a true increase in the
consumer's spending. It is difficult to confirm balance transfers
based on tradeline data since the information available is not
provided on a transaction level basis. In addition, there are
typically lags or absences of reporting of such values on tradeline
reports.
[0079] Nonetheless, marketplace analysis using confirmed consumer
panel and internal customer financial records has revealed reliable
ways in which balance transfers into an account may be identified
from imperfect individual tradeline data alone. Three exemplary
reliable methods for identifying balance transfers from credit
accounts, each which is based in part on actual consumer data
sampled, are as follows. It should be readily apparent that these
formulas in this form are not necessary for all embodiments of the
present process and may vary based on the consumer data used to
derive them.
[0080] A first rule identifies a balance transfer for a given
consumer's credit account as follows. The month having the largest
balance increase in the tradeline data, and which satisfies the
following conditions, may be identified as a month in which a
balance transfer has occurred: [0081] The maximum balance increase
is greater than twenty times the second maximum balance increase
for the remaining months of available data; [0082] The estimated
pay-down percent calculated at step 306 above is less than 40%; and
[0083] The largest balance increase is greater than $1000 based on
the available data.
[0084] A second rule identifies a balance transfer for a given
consumer's credit account in any month where the balance is above
twelve times the previous month's balance and the next month's
balance differs by no more than 20%.
[0085] A third rule identifies a balance transfer for a given
consumer's credit account in any month where: [0086] the current
balance is greater than 1.5 times the previous month's balance;
[0087] the current balance minus the previous month's balance is
greater than $4500; and [0088] the estimated pay-down percent from
step 306 above is less than 30%.
[0089] The process 600 then continues to step 608, where consumer
spending on each credit account is estimated over the next, for
example, three month period. In estimating consumer spend, any
spending for a month in which a balance transfer has been
identified from individual tradeline data above is set to zero for
purposes of estimating the size of the consumer's spending wallet,
reflecting the supposition that no real spending has occurred on
that account. The estimated spend for each of the three previous
months may then be calculated as follows:
Estimated spend=(the current balance-the previous month's
balance+(the previous month's balance*the estimated pay-down % from
step 604 above).
The exact form of the formula selected may be based on the category
in which the consumer is identified from the model applied, and the
formula is then computed iteratively for each of the three months
of the first period of consumer spend.
[0090] Next, at step 610 of the process 600, the estimated spend is
then extended over, for example, the previous three quarterly or
three-month periods, providing a most-recent year of estimated
spend for the consumer.
[0091] Finally, at step 612, this in turn may be used to generate a
plurality of final outputs for each consumer account (step 314).
These may be provided in an output file that may include a portion
or all of the following exemplary information, based on the
calculations above and information available from individual
tradeline data:
[0092] (i) size of previous twelve month spending wallet; (ii) size
of spending wallet for each of the last four quarters; (iii) total
number of revolving cards, revolving balance, and average pay down
percentage for each; (iv) total number of transacting cards, and
transacting balances for each; (v) the number of balance transfers
and total estimated amount thereof; (vi) maximum revolving balance
amounts and associated credit limits; and (vii) maximum transacting
balance and associated credit limit.
[0093] After step 612, the process 600 ends with respect to the
examined consumer. It should be readily appreciated that the
process 600 may be repeated for any number of current customers or
consumer prospects.
[0094] Referring now to FIGS. 7-10, therein is depicted
illustrative diagrams 700-1000 of how such estimated spending is
calculated in a rolling manner across each previous three month
(quarterly) period. In FIG. 7, there is depicted a first three
month period (i.e., the most recent previous quarter) 702 on a
timeline 710. As well, there is depicted a first twelve-month
period 704 on a timeline 708 representing the last twenty-one
months of point-in-time account balance information available from
individual tradeline data for the consumer's account. Each month's
balance for the account is designated as "B#." B1-B12 represent
actual account balance information available over the past twelve
months for the consumer. B13-B21 represent consumer balances over
consecutive, preceding months.
[0095] In accordance with the diagram 700, spending in each of the
three months of the first quarter 702 is calculated based on the
balance values B1-B12, the category of the consumer based on
consumer spending models generated in the process 200, and the
formulas used in steps 604 and 606.
[0096] Turning now to FIG. 8, there is shown a diagram 800
illustrating the balance information used for estimating spending
in a second previous quarter 802 using a second twelve-month period
of balance information 804. Spending in each of these three months
of the second previous quarter 802 is based on known balance
information B4-B15.
[0097] Turning now to FIG. 9, there is shown a diagram 900
illustrating the balance information used for estimating spending
in a third successive quarter 902 using a third twelve-month period
of balance information 904. Spending in each of these three months
of the third previous quarter 902 is based on known balance
information B7-B18.
[0098] Turning now to FIG. 10, there is shown a diagram 1000
illustrating the balance information used for estimating spending
in a fourth previous quarter 1002 using a fourth twelve-month
period of balance information 1004. Spending in each of these three
months of the fourth previous quarter 1002 is based on balance
information B10-821.
[0099] It should be readily appreciated that as the rolling
calculations proceed, the consumer's category may change based on
the outputs that result, and, therefore, different formula
corresponding to the new category may be applied to the consumer
for different periods of time. The rolling manner described above
maximizes the known data used for estimating consumer spend in a
previous twelve month period 1006.
[0100] Based on the final output generated for the customer,
commensurate purchasing incentives may be identified and provided
to the consumer, for example, in anticipation of an increase in the
consumer's purchasing ability as projected by the output file. In
such cases, consumers of good standing, who are categorized as
transactors with a projected increase in purchasing ability, may be
offered a lower financing rate on purchases made during the period
of expected increase in their purchasing ability, or may be offered
a discount or rebate for transactions with selected merchants
during that time.
[0101] In another example, and in the case where a consumer is a
revolver, such consumer with a projected increase in purchasing
ability may be offered a lower annual percentage rate on balances
maintained on their credit account.
[0102] Other like promotions and enhancements to consumers'
experiences are well known and may be used within the processes
disclosed herein.
[0103] Various statistics for the accuracy of the processes 200 and
600 are provided in FIGS. 11-18, for which a consumer sample was
analyzed by the process 200 and validated using 24 months of
historic actual spend data. The table 1100 of FIG. 11 shows the
number of consumers having a balance of $5000 or more for whom the
estimated paydown percentage (calculated in step 604 above) matched
the actual paydown percentage (as determined from internal
transaction data and external consumer panel data).
[0104] The table 1200 of FIG. 12 shows the number of consumers
having a balance of $5000 or more who were expected to be
transactors or revolvers, and who actually turned out to be
transactors and revolvers based on actual spend data. As can be
seen, the number of expected revolvers who turned out to be actual
revolvers (80539) was many times greater than the number of
expected revolvers who turned out to be transactors (1090).
Likewise, the number of expected and actual transactors outnumbered
by nearly four-to-one the number of expected transactors that
turned out to be revolvers.
[0105] The table 1300 of FIG. 13 shows the number of estimated
versus actual instances in the consumer sample of when there
occurred a balance transfer into an account. For instance, in the
period sampled, there were 148,326 instances where no balance
transfers were identified in step 606 above, and for which a
comparison of actual consumer data showed there were in fact no
balance transfers in. This compares to only 9,534 instances where
no balance transfers were identified in step 606, but there were in
fact actual balance transfers.
[0106] The table 1400 of FIG. 14 shows the accuracy of estimated
spending (in steps 608-612) versus actual spending for consumers
with account balances (at the time this sample testing was
performed) greater than $5000. As can be seen, the estimated
spending at each spending level most closely matched the same
actual spending level than for any other spending level in nearly
all instances.
[0107] The table 1500 of FIG. 15 shows the accuracy of estimated
spending (in steps 608-612) versus actual spending for consumers
having most recent account balances between $1600 and $5000. As can
be readily seen, the estimated spending at each spending level most
closely matched the same actual spending level than for any other
spending level in all instances.
[0108] The table 1600 of FIG. 16 shows the accuracy of estimated
spending versus actual spending for all consumers in the sample. As
can be readily seen, the estimated spending at each spending level
most closely matched the same actual spending level than for any
other actual spending level in all instances.
[0109] The table 1700 of FIG. 17 shows the rank order of estimated
versus actual spending for all consumers in the sample. This table
1700 readily shows that the number of consumers expected to be in
the bottom 10% of spending most closely matched the actual number
of consumers in that category, by 827,716 to 22,721. The table 1700
further shows that the number of consumers expected to be in the
top 10% of spenders most closely matched the number of consumers
who were actually in the top 10%, by 71,773 to 22,721.
[0110] The table 1800 of FIG. 18 shows estimated versus actual
annual spending for all consumers in the sample over the most
recent year of available data. As can be readily seen, the expected
number of consumers at each spending level most closely matched the
same actual spending level than any other level in all
instances.
[0111] Finally, the table 1900 of FIG. 19 shows the rank order of
estimated versus actual total annual spending for all the consumers
over the most recent year of available data. Again, the number of
expected consumers in each rank most closely matched the actual
rank than any other rank.
[0112] Prospective customer populations used for modeling and/or
later evaluation may be provided from any of a plurality of
available marketing groups, or may be culled from credit bureau
data, targeted advertising campaigns or the like. Testing and
analysis may be continuously performed to identify the optimal
placement and required frequency of such sources for using the size
of spending wallet calculations. The processes described herein may
also be used to develop models for predicting a size of wallet for
an individual consumer in the future.
[0113] Institutions adopting the processes disclosed herein may
expect to more readily and profitably identify opportunities for
prospect and customer offerings, which in turn provides enhanced
experiences across all parts of a customer's lifecycle. In the case
of a credit provider, accurate identification of spend
opportunities allows for rapid provisioning of card member
offerings to increase spend that, in turn, results in increased
transaction fees, interest charges and the like. The careful
selection of customers to receive such offerings reduces the
incidence of fraud that may occur in less disciplined card member
incentive programs. This, in turn, reduces overall operating
expenses for institutions.
II. Model Output for Individual Consumers
[0114] As mentioned above, the process described may also be used
to develop models for predicting a size of wallet for an individual
consumer in the future. The capacity a consumer has for spending in
a variety of categories is the share of wallet. The model used to
determine share of wallet for particular spend categories using the
processes described herein is the share of wallet ("SoW") model.
The SoW model provides estimated data and/or characteristics
information that is more indicative of consumer spending power than
typical credit bureau data or scores. The SoW model may output,
with sufficient accuracy, data that is directly related to the
spend capacity of an individual consumer. One of skill in the art
will recognize that any one or combination of the following data
types, as well as other data types, may be output by the SoW model
without altering the spirit and scope of the present invention.
[0115] The size of a consumer's twelve-month spending wallet is an
example output of the SoW model. This type of data is typically
output as an actual or rounded dollar amount. The size of a
consumer's spending wallet for each of several consecutive
quarters, for example, the most recent four quarters, may also be
output.
[0116] The SoW model output may include the total number of
revolving cards held by a consumer, the consumer's revolving
balance, and/or the consumer's average pay-down percentage of the
revolving cards. The maximum revolving balance and associated
credit limits can be determined for the consumer, as well as the
size of the consumer's revolving spending.
[0117] Similarly, the SoW model output may include the total number
of a consumer's transacting cards and/or the consumer's transacting
balance. The SoW model may additionally output the maximum
transacting balance, the associated credit limit, and/or the size
of transactional spending of the consumer.
[0118] These outputs, as well as any other outputs from the SoW
model, may be appended to data profiles of a company's customers
and prospects. This enhances the company's ability to make
decisions involving prospecting, new applicant evaluation, and
customer relationship management across the customer lifecycle.
[0119] Additionally or alternatively, the output of the model can
be calculated to equal a SoW score, much like credit bureau data is
used to calculate a credit rating. Credit bureau scores are
developed from data available in a consumer's file, such as the
amount of lines of credit, payment performance, balance, and number
of tradelines. This data is used to model the risk of a consumer
over a period of time using statistical regression analysis. Those
data elements that are found to be indicative of risk are weighted
and combined to determine the credit score. For example, each data
element may be given a score, with the final credit score being the
sum of the data element scores.
[0120] A SoW score, based on the SoW model, may provide a higher
level of predictability regarding spend capacity and
creditworthiness. The SoW score can focus, for example, on total
spend, plastic spend and/or a consumer's spending trend. Using the
processes described above, balance transfers are factored out of a
consumer's spend capacity. Further, when correlated with a risk
score, the SoW score may provide more insight into behavior
characteristics of relatively low-risk consumers and relatively
high-risk consumers.
[0121] The SoW score may be structured in one of several ways. For
instance, the score may be a numeric score that reflects a
consumer's spend in various ranges over a given time period, such
as the last quarter or year. As an example, a score of 5000 might
indicate that a consumer spent between $5000 and $6000 in the given
time period.
[0122] Alternatively or additionally, the score may include a range
of numbers or a numeric indicator that indicates the trend of a
consumer's spend over a given time period. For example, a trend
score of +4 may indicate that a consumer's spend has increased over
the previous 4 months, while a trend score of -4 may indicate that
a consumer's spend has decreased over the previous 4 months.
[0123] In addition to determining an overall SoW score, the SoW
model outputs may each be given individual scores and used as
attributes for consideration in credit score development by, for
example, traditional credit bureaus. As discussed above, credit
scores are traditionally based on information in a customer's
credit bureau file. Outputs of the SoW model, such as balance
transfer information, spend capacity and trend, and revolving
balance information, could be more indicative of risk than some
traditional data elements. Therefore, a company may use scored SoW
outputs in addition to or in place of traditional data elements
when computing a final credit score. This information may be
collected, analyzed, and/or summarized in a scorecard. This would
be useful to, for example and without limitation, credit bureaus,
major credit grantors, and scoring companies, such as Fair Isaac
Corporation of Minneapolis, Minn.
[0124] The SoW model outputs for individual consumers or small
businesses can also be used to develop various consumer models to
assist in direct marketing campaigns, especially targeted direct
marketing campaigns. For example, "best customer" or "preferred
customer" models may be developed that correlate characteristics
from the SoW model outputs, such as plastic spend, with certain
consumer groups. If positive correlations are identified, marketing
and customer relationship management strategies may be developed to
achieve more effective results.
[0125] In an example embodiment, a company may identify a group of
customers as its "best customers." The company can process
information about those customers according to the SoW model. This
may identify certain consumer characteristics that are common to
members of the best customer group. The company can then profile
prospective customers using the SoW model, and selectively target
those who have characteristics in common with the company's best
consumer model.
[0126] FIG. 20 is a flowchart of a method 2000 for using model
outputs to improve customer profiling. In step 2002, customers are
segmented into various categories. Such categories may include, for
example and without limitation, best customers, profitable
customers, marginal customers, and other customers.
[0127] In step 2004, model outputs are created for samples of
customers from each category. The customers used in step 2004 are
those for whom detailed information is known.
[0128] In step 2006, it is determined whether there is any
correlation between particular model outputs and the customer
categories.
[0129] Alternatively, the SoW model can be used to separate
existing customers on the basis of spend capacity. This allows
separation into groups based on spend capacity. A company can then
continue with method 2000 for identifying correlations, or the
company may look to non-credit-related characteristics of the
consumers in a category for correlations.
[0130] If a correlation is found, the correlated model output(s) is
deemed to be characteristic and/or predictive of the related
category of customers. This output can then be considered when a
company looks for customers who fit its best customer model.
III. Modeling and Outputs for Commercial Consumers
[0131] Commercial size of spending wallet ("SoSW") may also be
predicted. Commercial SoSW is the total business-related spending
of a company including cash but excluding bartered items. In order
to determine commercial SoSW, data is needed from sources other
than consumer credit bureaus. This is because, according to market
studies, approximately 7% of small business spending occurs on
plastic. Thus, only a small portion of total business spend would
be captured by consumer credit bureaus. Company financial
statements, however, provide a comprehensive summary of business
spend.
[0132] Company financial statement data may be used in a top-down
method to estimate commercial SoSW. FIG. 21 is a flowchart of an
example method for estimating commercial SoSW. In step 2102,
company financial statement data is obtained. The company of
interest may be a customer and/or prospect in a credit network. An
example credit network is OPEN: The Small Business Network ("OSBN")
from American Express. Although credit network companies will be
referred to herein as OSBN companies, one of skill in the pertinent
art will recognize that any credit network may be used without
departing from the spirit and scope of the present invention. The
company financial statement data may be obtained from, for example,
the High Balance Reunderwriting Unit ("HBRU") database of
commercially underwritten OSBN businesses. The HBRU database
includes data on high-spending OSBN customers that are underwritten
at least annually. The database also includes business financial
statements, which are a standard requirement of the underwriting
process. Usually covering 12 months, these financial statements
provide detailed expense information that can be used to assess
potential plastic, or credit card, spend. Also included in the
database are over approximately 33,000 underwriting events for
approximately 16,000 unique OSBN businesses.
[0133] Detailed operating expenses ("OpEx") costs from the HBRU
database are available in hard copy only, making it difficult to
electronically differentiate different types of spend, such as
cardable (spend that could be put on plastic) and uncardable (spend
that could not be put on plastic). An example source for electronic
company financial statement data is the tax statistics section of
the Internal Revenue Service ("IRS") website. This section of the
IRS website includes business summary statistics based on a
stratified, weighted sample of approximately 500,000 unaudited
company tax returns and financial statements. Available fields in
the IRS website include OpEx details, which allow for electronic
distinction between cardable and uncardable spend. These summaries
are available at the industry and/or legal structure level. The
industry grouping is based on the North American Industry
Classification System ("NAICS"), which replaced the U.S. Standard
Industrical Classification ("SIC") system.
[0134] Additional sources of company financial statement data
include, for example and without limitation, trade credit data from
the Equifax Small Business Enterprise ("SBE") database, produced by
Equifax Inc. of Atlanta, Ga.; the Experian Business Information
Solutions ("BIS") database produced by Experian of Costa Mesa,
Calif.; and the Dun & Bradstreet database, produced by Dun
& Bradstreet Corp. of Short Hills, N.J. Trade credit data is
credit provided by suppliers to merchants at the supplier offices.
Trade credit has been associated with various repayment options,
including, for example, a 2% discount if paid back to the supplier
in 10 days, with the net amount due within 30 days. Such a
repayment term is usually referred to as 2/10 net 30.
[0135] In step 2104, total business spend that could be transacted
using a commercial credit card is identified and calculated. FIG.
22 is a sample financial statement that may be analyzed using the
commercial SoSW model. The SoSW model for a particular business
considers at least two components: cost of goods sold ("CoGS") and
operating expenses ("OpEx"). For purposes of this application, it
is assumed that 100% of CoGS spend can be converted to plastic.
Each OpEx component is classified as "cardable" or "uncardable".
These components may be distinguished in the statement, as is shown
in the example of FIG. 22. Only the cardable OpEx is included in
the commercial SoSW calculation. The total SoSW for a particular
business can be calculated by adding the CoGS and the cardable
OpEx:
SoSW=CoGS+Cardable OpEx
Thus, according to the sample financial statement in FIG. 22, the
CoGS equals $5,970,082, the total OpEx equals $285,467, and the
cardable OpEx equals $79,346 (28% of total OpEx). The total SoSW
for this business thus equals $6,049,428. Once the total SoSW has
been calculated, method 2100 proceeds to step 2106.
[0136] In step 2106, a spend-like regression model is used to
estimate annual commercial SoSW value for OSBN customers and
prospects. The industry-based summaries from the IRS website, for
example, may be used to calculate a cardable OpEx percentage for
each combination of industry and legal structure. This will be
referred to herein as the cardable OpEx ratio. Based on the
industry and legal structure of credit network customers in, for
example, the HBRU database, the relevant cardable OpEx ratio is
applied.
[0137] Industry-level commercial SoSW is calculated using the given
cost of goods sold, total operating expenses, and the cardable OpEx
ratio as derived from, for example, the IRS data:
SoSW=CoGS+(Total OpEx*Cardable OpEx Ratio)
These elasticities within the industries can then be analyzed to
derive business-level estimations of SoSW. FIG. 23 displays the
distribution of commercial SoSW estimates among the OSBN HBRU
businesses. This analysis is based on OSBN underwriting events over
approximately 2.5 years, resulting in 16,337 underwriting events
across 8,657 unique OSBN businesses.
[0138] Commercial SoSW differs significantly by industry. As shown
in FIG. 24, most industries include a small percentage of
high-potential businesses that drive a large discrepancy between
the mean and median SoSW values.
[0139] Commercial SoSW represents overall annual cardable
expenditures. As discussed above, share of wallet ("SoW")
represents the portion of the total spending wallet that is
allocated towards, for example, a particular financial institution.
Commercial share of wallet (SoW) can be measured by dividing annual
OSBN spend (from the global risk management system ("GRMS")) into
commercial SoSW. As shown in FIG. 25, over 51% of HBRU businesses
have a commercial SoW of less than 10%. This illustrates the
magnitude of the opportunity to capture additional spend.
[0140] FIG. 26 is a table that describes the relationship between
the commercial SoSW model and business variables. This information
is based on Dun & Bradstreet data, and the adjusted R.sup.2
value for the data analyzed is 0.3456. The commercial SoSW model
takes into consideration, for example and without limitation,
annual sales amount of the company, number of employees in the
company, highest credit amount of the company within the previous
13 months, total dollar amount of satisfactory financial
experiences by the company over the previous 13 months, and a
financial stress score percentile of the company, wherein a
percentile of 0 indicates highest risk, and a percentile of 100
indicates lowest risk. Annual sales amount, number of employees,
and highest credit amount within the last 13 months all have a
positive linear effect on a company's commercial SoSW. The total
dollar amount of satisfactory financial experiences over the last
13 months has a positive logarithmic effect on a company's
commercial SoSW. The financial stress score percentile has a
negative linear effect on a company's commercial SoSW.
[0141] The commercial SoSW model was validated based on actual data
from high-balance re-underwritten OSBN accounts. FIG. 27 is a graph
comparing actual commercial SoSW results to the predicted
commercial SoSW estimates. As shown in FIG. 27, this model performs
well as a rank-ordering tool.
[0142] FIG. 28 is a Lorenz-curve graph comparing the commercial
SoSW model to a perfectly random prediction. As shown in FIG. 28,
the top 10% of businesses, in terms of predicted commercial SoSW,
account for nearly 60% of the actual commercial SoSW.
[0143] In the data discussed above, the financial statements used
were only for high-balance customers, resulting in sample selection
bias. Nonetheless, the model assessment shows that this application
is effective on businesses with annual revenue of $1 million or
greater, based on Dun & Bradstreet data. This is a high-revenue
segment, and approximately 12% to 15% of the OSBN base meets this
high-revenue status. Although the examples incorporated herein
refer to this high-revenue segment, one of skill in the pertinent
art will recognize that a commercial SoSW metric may also be
developed for middle-market corporate consumers without departing
from the spirit and scope of the present invention, as will be
discussed below.
[0144] Predicted commercial SoSW values are quintiled into the
following ranges:
Q1: <$3.85 MM
Q2: $3.85 MM to $5.18 MM
Q3: $5.18 MM to $6.62 MM
Q4: $6.62 MM to $9.38 MM
Q5: >$9.38 MM
Although five classifications having the above values are referred
to herein, one of skill in the pertinent art will recognize that
fewer or more classifications may be used, and the classifications
may use a different range of values, without departing from the
spirit and scope of the present invention.
[0145] FIG. 29 is a chart illustrating the customer-level
relationship classifications, or quintiles. Each quintile is
separated into percentages of customers who only charge, only lend,
and both charge and lend. As shown, the proportion of OSBN charge
customers increases with the predicted commercial SoSW quintile.
However, as shown in FIG. 30, which illustrates the active number
of OSBN accounts by quintile, the proportion of charge customers
does not necessarily increase for average active number of OSBN
accounts by quintile.
[0146] The commercial SoSW model may output a scored output file.
FIG. 31 is a table that displays customer counts in the scored
output file. Customers in the higher SoSW and lower OSBN Spend
cells represent the greatest potential for converting plastic spend
outside of a financial company to spend related to the financial
company, as well as for converting non-plastic business spend to
spend related to the financial company. Higher SoSW and higher OSBN
Spend cells signify opportunities for growing OSBN spend among
higher-spending customers.
[0147] As discussed above, commercial SoW for an OSBN company can
be determined based on annual OSBN spend and commercial SoSW.
Various targets and predictors may be used to determine commercial
SoW for different commercial segments including and other than the
OSBN segment. For example, for OSBN companies having a revenue
above $1 million as reported, for example, by Dun & Bradstreet,
the commercial SoW model targets company financial statements using
Dun & Bradstreet's Credit Scoring Attribute Database ("CSAD")
as a predictor. A method of segmentation based on data availability
and ordinary least squares ("OLS") models can be used to output a
company-level SoW value, which can be used, for example, to analyze
prospects, new accounts, and customer management.
[0148] For OSBN companies with an Equifax SBE trade level balance
history, the commercial SoW model may target SBE time series
balance amounts using Equifax SBE as a predictor. A methodology
similar to the consumer SoW model can be used to output a
company-level SoW value, which can be used, for example, to analyze
new accounts and customer management.
[0149] For core OSBN companies, a "bottoms up" approach may be
used. Trade level detail on commercial bureaus and other external
data sources may be targeted using the Dun & Bradstreet CSAD,
Dun & Bradstreet Detailed Trade, Experian BIS, and Equifax SBE
databases as predictors. A method of segmentation based on data
availability and OLS models can be used to output a company-level
SoW value, which can be used, for example, to analyze prospects,
new accounts, and customer management.
[0150] For core OSBN companies, an industry inference approach may
also be used. Industry-level financial statement data is targeted
using the Dun & Bradstreet CSAD, Dun & Bradstreet Detailed
Trade, Experian BIS, and Equifax SBE databases as predictors. A
method of segmentation based on data availability and OLS models
can be used to output an industry-level SoW or a company-level SoW
value, which can be used, for example, to analyze prospects, new
accounts, and customer management.
[0151] For low revenue middle market companies, or for medium and
larger revenue middle market companies, company financial
statements may be targeted using the Dun & Bradstreet CSAD as a
predictor. The existing OSBN model is combined with new middle
market data to output an industry-level SoW or a company-level SoW
value, which can be used, for example, to analyze prospects, new
accounts, and customer management.
[0152] For other middle market companies, a "bottoms up" approach
may be used. Trade level detail on commercial bureaus and other
external data sources is targeted using the Dun & Bradstreet
CSAD as a predictor. A method of segmentation based on data
availability and OLS models can be used to output an industry-level
SoW or a company-level SoW value, which can be used, for example,
to analyze prospects, new accounts, and customer management.
[0153] For Global Establishment Services ("GES") companies that
overlap to the middle market or OSBN, the middle market or OSBN
value can be targeted using the middle market or OSBN data plus any
unique GES data as predictors. A method of segmentation based on
data availability and OLS models can be used to output a
company-level SoW value, which can be used, for example, to analyze
prospects, new accounts, and customer management.
[0154] For GES companies that do not overlap with the middle market
or OSBN, charge volume plus Dun & Bradstreet data and other
external data may be targeted using the GES and Dun &
Bradstreet as predictors. A method of segmentation based on data
availability and OLS models can be used to output a company-level
SoW value, which can be used, for example, to analyze prospects,
new accounts, and customer management. It can also be used to
output total business volume at a company-specific level and total
business volume at an industry-specific level.
[0155] Other data elements can be generated as well, such as a
transactor vs. revolver indicator, largest transactor balance data,
largest revolver balance data, and trade types and number of trade
types data. Thus, commercial SoW, including plasticable SoW (spend
that can be converted to plastic) and plastic SoW (spend that is
already on plastic) can be predicted for a wide range of companies
and industries.
IV. Applicable Market Segments/Industries for SoW
[0156] Outputs of the SoW model can be used in any business or
market segment that extends credit or otherwise needs to evaluate
the creditworthiness or spend capacity of a particular customer.
These businesses will be referred to herein as falling into one of
three categories: financial services companies, retail companies,
and other companies. Although the applicable market segments and
industries will be referred to herein with reference to consumers
and individual consumer SoW, one of skill in the art will recognize
that companies and commercial SoW may be used in a similar manner
without departing from the spirit and scope of the present
invention.
[0157] The business cycle in each category may be divided into
three phases: acquisition, retention, and disposal. The acquisition
phase occurs when a business is attempting to gain new customers.
This includes, for example and without limitation, targeted
marketing, determining what products or services to offer a
customer, deciding whether to lend to a particular customer and
what the line size or loan should be, and deciding whether to buy a
particular loan. The retention phase occurs after a customer is
already associated with the business. In the retention phase, the
business interests shift to managing the customer relationship
through, for example, consideration of risk, determination of
credit lines, cross-sell opportunities, increasing business from
that customer, and increasing the company's assets under
management. The disposal phase is entered when a business wishes to
dissociate itself from a customer or otherwise end the customer
relationship. This can occur, for example, through settlement
offers, collections, and sale of defaulted or near-default
loans.
A. Financial Services Companies
[0158] Financial services companies include, for example and
without limitation: banks and lenders, mutual fund companies,
financiers of leases and sales, life insurance companies, online
brokerages, and loan buyers.
[0159] Banks and lenders can utilize the SoW model in all phases of
the business cycle. One exemplary use is in relation to home equity
loans and the rating given to a particular bond issue in the
capital market. Although not specifically discussed herein, the SoW
model would apply to home equity lines of credit and automobile
loans in a similar manner.
[0160] If the holder of a home equity loan, for example, borrows
from the capital market, the loan holder issues asset-backed
securities ("ABS"), or bonds, which are backed by receivables. The
loan holder is thus an ABS issuer. The ABS issuer applies for an
ABS rating, which is assigned based on the credit quality of the
underlying receivables. One of skill in the art will recognize that
the ABS issuer may apply for the ABS rating through any application
means without altering the spirit and scope of the present
invention. In assigning a rating, the rating agencies weigh a
loan's probability of default by considering the lender's
underwriting and portfolio management processes. Lenders generally
secure higher ratings by credit enhancement. Examples of credit
enhancement include over-collateralization, buying insurance (such
as wrap insurance), and structuring ABS (through, for example,
senior/subordinate bond structures, sequential pay vs. pari passu,
etc.) to achieve higher ratings. Lenders and rating agencies take
the probability of default into consideration when determining the
appropriate level of credit enhancement.
[0161] During the acquisition phase of a loan, lenders may use the
SoW model to improve their lending decisions. Before issuing the
loan, lenders can evaluate a consumer's spend capacity for making
payments on the loan. This leads to fewer bad loans and a reduced
probability of default for loans in the lender's portfolio. A lower
probability of default means that, for a given loan portfolio that
has been originated using the SoW model, either a higher rating can
be obtained with the same degree of over-collateralization, or the
degree of over-collateralization can be reduced for a given debt
rating. Thus, using the SoW model at the acquisition stage of the
loan reduces the lender's overall borrowing cost and loan loss
reserves.
[0162] During the retention phase of a loan, the SoW model can be
used to track a customer's spend. Based on the SoW outputs, the
lender can make various decisions regarding the customer
relationship. For example, a lender may use the SoW model to
identify borrowers who are in financial difficulty. The credit
lines of those borrowers which have not fully been drawn down can
then be reduced. Selectively revoking unused lines of credit may
reduce the probability of default for loans in a given portfolio
and reduce the lender's borrowing costs. Selectively revoking
unused lines of credit may also reduce the lender's risk by
minimizing further exposure to a borrower that may already be in
financial distress.
[0163] During the disposal phase of a loan, the SoW model enables
lenders to better predict the likelihood that a borrower will
default. Once the lender has identified customers who are in danger
of default, the lender may select those likely to repay and extend
settlement offers. Additionally, lenders can use the SoW model to
identify which customers are unlikely to pay and those who are
otherwise not worth extending a settlement offer.
[0164] The SoW model allows lenders to identify loans with risk of
default, allowing lenders, prior to default, to begin anticipating
a course of action to take if default occurs. Because freshly
defaulted loans fetch a higher sale price than loans that have been
non-performing for longer time periods, lenders may sell these
loans earlier in the default period, thereby reducing the lender's
costs.
[0165] The ability to predict and manage risk before default
results in a lower likelihood of default for loans in the lender's
portfolio. Further, even in the event of a defaulted loan, the
lender can detect the default early and thereby recoup a higher
percentage of the value of that loan. A lender using the SoW model
can thus show to the rating agencies that it uses a combination of
tight underwriting criteria and robust post-lending portfolio
management processes. This enables the lender to increase the
ratings of the ABS that are backed by a given pool or portfolio of
loans and/or reduce the level of over-collateralization or credit
enhancement required in order to obtain a particular rating.
[0166] Turning to mutual funds, the SoW model may be used to manage
the relationship with customers who interact directly with the
company. During the retention phase, if the mutual fund company
concludes that a customer's spending capacity has increased, the
company can conclude that either or both of the customer's
discretionary and disposable income has increased. The company can
then market additional funds to the customer. The company can also
cross-sell other services that the customer's increased spend
capacity would support.
[0167] Financiers of leases or sales, such as automobile lease or
sale financiers, can benefit from SoW outputs in much the same way
as a bank or lender, as discussed above. In typical product
financing, however, the amount of the loan or lease is based on the
value of the product being financed. Therefore, there is generally
no credit limit that needs to be revisited during the course of the
loan. For this reason, the SoW model is most useful to lease/sales
finance companies during the acquisition and disposal phases of the
business cycle.
[0168] Life insurance companies can primarily benefit from the SoW
model during the acquisition and retention phases of the business
cycle. During the acquisition phase, the SoW model allows insurance
companies to identify those people with adequate spend capacity for
paying premiums. This allows the insurance company to selectively
target its marketing efforts to those most likely to purchase life
insurance. For example, the insurance company could model consumer
behavior in a similar manner as the "best customer" model described
above. During the retention phase, an insurance company can use the
SoW model to determine which of its existing clients have increased
their spend capacity and would have a greater capability to
purchase additional life insurance. In this way, those existing
customers could be targeted at a time during which they would most
likely be willing to purchase without overloading them with
materials when they are not likely to purchase.
[0169] The SoW model is most relevant to brokerage and wealth
management companies during the retention phase of the business
cycle. Due to convenience factors, consumers typically trade
through primarily one brokerage house. The more incentives extended
to a customer by a company, the more likely the customer will use
that company for the majority of its trades. A brokerage house may
thus use the SoW model to determine the capacity or trend of a
particular customer's spend and then use that data to cross-sell
other products and/or as the basis for an incentive program. For
example, based on the SoW outputs, a particular customer may become
eligible for additional services offered by the brokerage house,
such as financial planning, wealth management, and estate planning
services.
[0170] Just as the SoW model can help loan holders determine that a
particular loan is nearing default, loan buyers can use the model
to evaluate the quality of a prospective purchase during the
acquisition phase of the business cycle. This assists the loan
buyers in avoiding or reducing the sale prices of loans that are in
likelihood of default.
B. Retail Companies
[0171] Aspects of the retail industry for which the SoW model would
be advantageous include, for example and without limitation: retail
stores having private label cards, on-line retailers, and mail
order companies.
[0172] There are two general types of credit and charge cards in
the marketplace today: multipurpose cards and private label cards.
A third type of hybrid card is emerging. Multipurpose cards are
cards that can be used at multiple different merchants and service
providers. For example, American Express, Visa, Mastercard, and
Discover are considered multipurpose card issuers. Multipurpose
cards are accepted by merchants and other service providers in what
is often referred to as an "open network." This essentially means
that transactions are routed from a point-of-sale ("POS") through a
network for authorization, transaction posting, and settlement. A
variety of intermediaries play different roles in the process.
These include merchant processors, the brand networks, and issuer
processors. This open network is often referred to as an
interchange network. Multipurpose cards include a range of
different card types, such as charge cards, revolving cards, and
debit cards, which are linked to a consumer's demand deposit
account ("DDA") or checking account.
[0173] Private label cards are cards that can be used for the
purchase of goods and services from a single merchant or service
provider. Historically, major department stores were the
originators of this type of card. Private label cards are now
offered by a wide range of retailers and other service providers.
These cards are generally processed on a closed network, with
transactions flowing between the merchant's POS and its own
backoffice or the processing center for a third-party processor.
These transactions do not flow through an interchange network and
are not subject to interchange fees.
[0174] Recently, a type of hybrid card has evolved. This is a card
that, when used at a particular merchant, is that merchant's
private label card, but when used elsewhere, becomes a multipurpose
card. The particular merchant's transactions are processed in the
proprietary private label network. Transactions made with the card
at all other merchants and service providers are processed through
an interchange network.
[0175] Private label card issuers, in addition to multipurpose card
issuers and hybrid card issuers, can apply the SoW model in a
similar way as described above with respect to credit card
companies. That is, knowledge of a consumer's spend capability, as
well as knowledge of the other SoW outputs, could be used by card
issuers to improve performance and profitability across the entire
business cycle.
[0176] Online retail and mail order companies can use the SoW model
in both the acquisition and retention phases of the business cycle.
During the acquisition phase, for example, the companies can base
targeted marketing strategies on SoW outputs. This could
substantially reduce costs, especially in the mail order industry,
where catalogs are typically sent to a wide variety of individuals.
During the retention phase, companies can, for example, base
cross-sell strategies or credit line extensions on SoW outputs.
[0177] The SoW model may also be useful to merchants accepting
checks at a point of sale ("POS"). Before accepting a check from a
consumer at a POS as a form of payment, merchants typically
"verify" the check or request a "check guarantee". The verification
and/or guarantee are usually provided by outside service
providers.
[0178] Verification reduces the risk of the merchant's accepting a
bad check. When a consumer attempts to pay by check, the merchant
usually asks for a piece of identification. The merchant then
forwards details of the check, such as the MICR number, and details
of the identification (e.g., a driver's license number if the
driver's license is proffered as identification) to a service
provider. On a per transaction basis, the service provider searches
one or more databases (e.g., National Check Network) containing
negative and positive check writer accounts. The service provider
uses these accounts to determine if there is a match between
information in the database(s) and the specific piece of
information provided by the merchant. A match may identify whether
the check writer has a positive record or delinquent check-related
debts.
[0179] Upon notification of this match, the merchant decides
whether to accept or decline the check. The notification may be
provided, for example, via a coded response from the provider. If
the service provider is not a check guarantor, there is no
guarantee that the check will be honored by the check writer's bank
even when a search of the database(s) does not result in any
negative results. The service providers earn a transaction fee each
time the databases are searched.
[0180] Under a check guarantee arrangement, however, the service
provider guarantees a check to the merchant. If the check is
subsequently dishonored by the customer's bank, the merchant is
reimbursed by the service provider, which then acquires rights to
collect the delinquent amount from the check writer. The principal
risk of providing this service is the risk of ever collecting the
amount that the service provider guaranteed from a delinquent check
writer whose check was dishonored by his bank. If the service
provider is unable to collect the amount, it loses that amount.
[0181] Before guaranteeing a check, the service provider searches
several databases using the customer data supplied by the merchant.
The service provider then scores each transaction according to
several factors. Factors which may be considered include, for
example and without limitation, velocity, prior activity, check
writer's presence in other databases, size of the check, and prior
bad check activity by geographic and/or merchant specific
locations. Velocity is the number of times a check writer has been
searched in a certain period of time. Prior activity is based on
the prior negative or positive transactions with the check writer.
Check writer's presence in other databases looks at national
databases that are selectively searched based on the size of the
check and prior activity with the check writer. If the scoring
system concludes that the risk is too high, the service provider
refuses to guarantee the check. If the scoring system provides a
positive result, the service provider agrees to guarantee the
check.
[0182] Use of the SoW model thus benefits the service providers. At
the origination phase, service providers may use SoW scores as one
of the parameters for deciding whether or not to guarantee a check.
For example, the SoW score can be used to differentiate between a
low-risk consumer and a high-risk consumer. A low-risk consumer may
be, for example, a person who is writing more checks because his
income, as determined by the SoW model, has probably increased. In
this case, the check velocity is not necessarily a measurement of
higher risk. A high-risk consumer, on the other hand, may be a
person whose check velocity has increased without a corresponding
increase in income or spend capacity, as shown by the SoW
model.
[0183] On average, some service providers collect on only 50% to
60% of the checks that they guarantee and that subsequently become
delinquent. At the disposal phase of the business cycle, the
service providers may use the SoW model in a similar manner to
other financial institutions, as described above. For example,
service providers may use SoW to determine, for example, which
debts to collect in-house and which debts to sell. Thus, SoW helps
service providers make the collection process more efficient.
C. Other Companies
[0184] Types of companies which also may make use of the SoW model
include, for example and without limitation: the gaming industry,
charities and universities, communications providers, hospitals,
and the travel industry.
[0185] The gaming industry can use the SoW model in, for example,
the acquisition and retention phases of the business cycle. Casinos
often extend credit to their wealthiest and/or most active players,
also known as "high rollers." The casinos can use the SoW model in
the acquisition phase to determine whether credit should be
extended to an individual. Once credit has been extended, the
casinos can use the SoW model to periodically review the customer's
spend capacity. If there is a change in the spend capacity, the
casinos may alter the customer's credit line to be more
commensurate with the customer's spend capacity.
[0186] Charities and universities rely heavily on donations and
gifts. The SoW model allows charities and universities to use their
often limited resources more effectively by timing their
solicitations to coincide with periods when donors have had an
increase in disposable/discretionary income and are thus better
able to make donations. The SoW model also allows charities and
universities to review existing donors to determine whether they
should be targeted for additional support.
[0187] Communications providers, such as telephone service
providers often contract into service plans with their customers.
In addition to improving their targeted marketing strategies,
communications providers can use the SoW outputs during the
acquisition phase to determine whether a potential customer is
capable of paying for the service under the contract.
[0188] The SoW model is most applicable to hospitals during the
disposal phase of the business cycle. Hospitals typically do not
get to choose or manage the relationship with their patients.
Therefore, they are often in the position of trying to collect for
their services from patients with whom there was no prior customer
relationship. There are two ways that a hospital can collect its
fees. The hospital may run the collection in-house, or the hospital
may turn over responsibility for the collection to a collection
agent. Although the collection agent often takes fees for such a
service, it can be to the hospital's benefit if the collection is
time-consuming and/or difficult.
[0189] The SoW model can be used to predict which accounts are
likely to pay with minimal persuasion, and which ones are not. The
hospital can then select which accounts to collect in-house, and
which accounts to outsource to collection agencies. For those that
are retained in-house, the hospital can further segment the
accounts into those that require simple reminders and those
requiring more attention. This allows the hospital to optimize the
use of its in-house collections staff. By selectively outsourcing
collections, the hospital or other lender can reduce the
contingency fees that it pays to collection agencies and maximize
the amount collected by the in-house collection team.
[0190] Members of the travel industry can make use of the SoW data
in the acquisition and retention stages of the business cycle. For
example, a hotelier typically has a brand of hotel that is
associated with a particular "star-level" or class of hotel. In
order to capture various market segments, hoteliers may be
associated with several hotel brands that are of different classes.
During the acquisition phase of the business cycle, a hotelier may
use the SoW method to target individuals that have appropriate
spend capacities for various classes of hotels. During the
retention phase, the hotelier may use the SoW method to determine,
for example, when a particular individual's spend capacity
increases. Based on that determination, the hotelier can market a
higher class of hotel to the consumer in an attempt to convince the
consumer to upgrade.
[0191] One of skill in the relevant art(s) will recognize that many
of the above-described SoW applications may be utilized by other
industries and market segments without departing from the spirit
and scope of the present invention. For example, the strategy of
using SoW to model an industry's "best customer" and targeting
individuals sharing characteristics of that best customer can be
applied to nearly all industries.
[0192] SoW data can also be used across nearly all industries to
improve customer loyalty by reducing the number of payment
reminders sent to responsible accounts. Responsible accounts are
those who are most likely to pay even without being contacted by a
collector. The reduction in reminders may increase customer
loyalty, because the customer will not feel that the lender or
service provider is unduly aggressive. The lender's or service
provider's collection costs are also reduced, and resources are
freed to dedicate to accounts requiring more persuasion.
[0193] Additionally, the SoW model may be used in any company
having a large customer service call center to identify specific
types of customers. Transcripts are typically made for any call
from a customer to a call center. These transcripts may be scanned
for specific keywords or topics, and combined with the SoW model to
determine the consumer's characteristics. For example, a bank
having a large customer service center may scan service calls for
discussions involving bankruptcy. The bank could then use the SoW
model with the indications from the call center transcripts to
evaluate the customer.
V. Applicable Market Segments/Industries for Commercial SoW and
Commercial SoSW
A. Banks, Lenders, and Credit Providers
[0194] Banks, lenders, and credit providers (referred to
collectively herein as "lenders") lend money based on a borrower's
credit rating and collateral. Even when loans are secured by
collateral, though, there is no guarantee that the value of the
collateral will not depreciate over time to a value that is below
the outstanding loan balance. While a credit rating of the borrower
may be a good indicator of a borrower's willingness to repay, it is
not a good indicator of borrower's future ability to repay. By
predicting future spend, the commercial SoW and commercial SoSW
models provide a score that is, effectively, a proxy for predicting
a borrower's ability to repay.
[0195] In the acquisition stage of the customer lifecycle, lenders
can use commercial SoW and/or commercial SoSW models to determine
to whom they should lend, and to whom they should deny credit. The
commercial models may also be used for pricing loans and other
products in a dynamic way. By using the commercial models to
determine whose profits and/or spend is likely to increase, for
example, lenders can use the scores produced by the commercial
models as search criteria to identify which existing customers
should be targeted for both new and existing products. The scores
may also be used to identify companies who are not yet clients who
could be targeted for lender products.
[0196] In the retention stage of the customer lifecycle, lenders
can use the commercial models to determine which customers should
be retained. The models can also be used to segment existing
customers for cross-selling purposes. Additionally, the models can
be used to manage credit risk and/or exposure from existing loans.
For example, if the commercial models predict that a business is
undergoing or will undergo increased financial stress and/or credit
risk, the lender could revoke the business's unused lines of
credit.
[0197] In the disposal stage, the commercial models can be used to
determine which customers should be extended settlement offers by
the lender. The lender can also use the commercial models to
identify which business loans are likely to default. The lender can
thus sell these loans early-on to get a higher sale price. This is
useful since the loan seller gets fewer cents on the dollar as the
time that lapses between loan default and sale grows longer. The
lender can also use the commercial models to determine which loans
should be collected in-house, and which loans should be sent out to
collection agencies.
B. Investment Vehicles and Investment Vehicle Managers
[0198] Although mutual funds will be used herein as example
investment vehicles, one of skill in the relevant art(s) will
recognize that commercial SoW and commercial SoSW can benefit many
other types of investment vehicles, such as hedge funds.
[0199] Mutual funds, for example, that invest using a so-called
"top-down" approach identify stocks by first selecting industries
that match certain criteria, and then zeroing in on companies in
that industry that match other criteria. The other criteria may be,
for example and without limitation, size, revenue growth, profits,
price-earnings ratios, and revenue growth vs. expense growth. Funds
that use a so-called "bottom-up" approach identify securities by
zeroing in on companies that match specific criteria, without
starting at the industry level. Some managers also use analyst
reviews and credit agency reports, among other devices. Whether
using a top-down approach, a bottom-up approach, or a combination
of both, the fund managers rely on historical data. These data tend
to be disjointed and are not often connected.
[0200] The commercial SoW and/or commercial SoSW models may be used
to present fund managers with a simple yet robust score, which is a
quantitative measure that indicates whether or not a company is
expected to do well. This score may be of particular interest if
the mutual fund is about to buy securities of the company.
Typically, investors and fund managers use historical information.
When they invest, they assume that a historical trend will
continue. That is, they frequently assume that a company will
continue to be profitable. However, funds and other investors,
particularly those that invest in smaller companies, do not always
have access to reliable and accurate historical data and to a
single score that encapsulates a company's revenues, expenses, and
financial stress. The commercial models provide a score that
encompasses all of these.
[0201] In the acquisition stage of the customer lifecycle, mutual
funds can use a score produced by the commercial models as one of
the parameters to be considered when picking stocks and when
determining which stocks to buy, sell, or short.
[0202] The commercial models may also be used in the retention and
disposal stages. After buying stocks, money managers normally set a
price target at which to sell. The stocks are sold once the price
reaches that pre-set level. Alternatively, if it seems that the
price will never reach that preset level or prices fall instead of
rising as expected, the stock may be sold at a loss. Fund managers
can use the commercial models to predict which stocks in their
portfolio are likely to suffer a price fall.
[0203] In an example scenario, a mutual fund has purchased the
securities of a company. The company sells its products to other
companies in a certain industry. The mutual fund could use scores
produced by the commercial models to predict whether or not the
company's customers will be spending less in the future, thus
reducing the company's revenues and possibly its share price. In
addition, if a particular customer is one of the company's major
customers, the mutual fund could use scores produced by the
commercial model to determine and/or predict potential financial
trouble at the particular customer. With such knowledge, the mutual
fund could sell the company's shares before the price plummets.
Alternatively, if the scores produced by the commercial models show
that the particular customer will be doing better, the mutual fund
could buy more shares of the company.
C. Research Analysts
[0204] A research analyst provides a rating that summarizes the
analyst's opinion about the quality and/or prospects of the rated
company's securities. Such a rating might be "BUY," "HOLD," or
"SELL" for equity, or "A," "B," "C," or "JUNK" for debt. Whether
conducting analyses that would result in a rating for debt or
equity, analysts review a company's performance, management and
prospects, among other things.
[0205] While it is standard practice for rated companies to provide
analysts with factual historical data, the clients of such rated
companies have no obligation to give the analyst any data unless
the client is also rated by the same analyst. In the absence of
such information, the analysts projections about the future
prospects of the rated company, and any rating that is based on
such projections, is pure speculation.
[0206] With the commercial SoW and/or commercial SoSW models,
however, the analyst has a simple, yet comprehensive, indication of
the business prospects of the customers of the rated company. With
scores produced by the commercial models, therefore, the analyst is
then able to provide a much more meaningful rating that provides a
more accurate picture of the rated company.
[0207] As an example, an analyst follows a particular corporation.
He also rates the securities issued by the corporation. The main
customers of the corporation are companies in a specific industry.
The corporation has issued some bonds, and plans to service those
bonds with the revenues from selling to customers in the specific
industry. In this scenario, which is not unique, the analyst could
have access to public historical financial information from some
companies in the specific industry. These historical data, however,
are not forward-looking, and do not tell the analyst the prospects
of the companies in the specific industry.
[0208] However, with scores produced by the commercial models, the
analyst can predict whether or not the companies in the specific
industry intend to increase or decrease their spend. Thus, by
combining the predictive capabilities of the commercial models and
the analyst's knowledge of the corporation, the analyst can issue a
much more accurate and reliable rating for the securities issued by
the corporation. The analyst is able to use scores produced by the
commercial models to assign new ratings and change existing
ratings.
D. Government Agencies, Procurement Departments, and Others that
Patronize Small Businesses
[0209] Government departments and agencies and large publicly
traded firms are usually obliged by law or otherwise to patronize
small businesses. Such patronage takes various forms, including,
for example and without limitation, so-called 8(a) programs, small
business set aside programs, and disadvantaged business entity
programs. Once certified, a small business can bid as a sole source
provider for government contracts worth several million
dollars.
[0210] Certifying agencies rely on Dun & Bradstreet scores and
an array of self-reported data to certify a business as, for
example and without limitation, small, woman-owned, minority-owned,
or a disadvantaged business entity. To be certified as a
woman-owned business, for example, the certifying authority
basically certifies that the business is at least 51% owned by one
or more women. Such self-reported data, even when accurate, are
only required to be updated every year or so. Further, these data
do not have the inherent capability to provide an indication of
whether the particular small business is growing or shrinking, or
whether the particular industry served by such small business (the
small business's revenue source) is growing or shrinking.
[0211] Thus, while such certifications might level the playing
field by giving small businesses access to opportunities they might
not otherwise have, they also put those buying the services (the
government agencies, procurement departments, etc.) at risk. This
is because most small businesses fail within the first few years,
and small-business type certifications do not provide an indication
of the likelihood that a particular business would continue as a
going concern.
[0212] By using the commercial SoW and/or commercial SoSW models,
buyers of services can determine, before awarding and/or renewing
contracts, whether the vendor is on the upswing or on its last
breath. Such service buyers could also use a combination of the
commercial models and statistical analyses to predict the
likelihood that a particular small business will remain in
business.
[0213] In the acquisition stage of the customer lifecycle, the
agency or procurement department can use the commercial models to
determine to whom contracts should be awarded, and to whom business
should be denied. Further, to the extent that service buyers
require vendors that are small businesses to post performance
bonds, such service buyers could also use the commercial models to
determine whether or not a performance bond should be required and,
if so, the amount the performance bond should be. In addition to
using the commercial models as tools for determining to whom
contracts should be awarded, such service buyers, when appropriate,
can use scores produced by the commercial models to prepare a
shortlist of who to solicit proposals from. This may occur, for
example, when sending out requests for proposals that are not
broadcast to everyone.
[0214] In the retention stage, agencies or procurement departments
can use scores produced by the commercial models to manage their
approved vendor lists. In the disposal stage, they can use scores
produced by the commercial models to proactively determine which
vendors to remove from their approved vendor lists.
E. Insurance Companies
[0215] Insurance companies sell businesses a product called "key
man insurance." Basically, key man insurance is a life insurance
policy on the key/crucial/critical people in a business. In a small
business, this is usually the owner, the founder(s), or perhaps a
key employee or two (all collectively referred to herein as key
employee(s)). If something were to happen to these people, the
business would most probably sink. With key man term life
insurance, a company purchasing a life insurance policy on the key
employee(s) pays the premiums. That company becomes the beneficiary
of the policy. If the key employee(s) dies suddenly, the company
receives the insurance payoff In effect, the key man insurance
helps the insured company to mitigate the adverse impact of losing
the key employee(s). The company can use the insurance proceeds for
expenses until it hires a replacement, or, if necessary, settle
debts, distribute money to stakeholders, provide severance
packages, and wind down the business in an orderly manner.
[0216] To price such insurance policies, insurers rely on an array
of data, including the insured company's historical financials.
Some insurers might even go as far as analyzing the industry that
constitutes the customer base (and thus revenue source) of the
company buying key man insurance. Such analyses, however, tend to
be general at best. In addition, even if the insurance company
wants to analyze the business prospects of the insured company's
particular customers, such customers are not obligated to provide
any data, let alone accurate data, to the insurance company.
Consequently, insurers face significant danger of underpricing
risk. In extreme cases, this information asymmetry results in
outright fraud against the insurers.
[0217] With the commercial SoW and/or commercial SoSW models,
insurers can reduce the danger of underpricing risk, and thus price
their risk accordingly. For example, when pricing a key man policy,
the insurer can ask the insured for a list of its major customers
in addition to analyzing the historical financials of the insured
company. With such a list, the insurer can then factor into its
premium calculations the business prospects of each such customer.
In extreme cases, the insurer could even refuse to provide key man
insurance to a company, because it may not be reasonable to provide
insurance to a company that is about to go under.
[0218] In the acquisition stage of the customer lifecycle,
insurance companies can use the commercial models to decide whether
or not to sell insurance to a particular company. The commercial
models can also be used as a factor in determining what the
insurance should be. Additionally, the commercial models can be
used by the insurance company as a filter for identifying
prospective clients.
[0219] In the retention stage, insurance companies can use the
commercial models as a factor to decide whether to re-price the
premium on a policy, and also to decide whether to increase or
decrease the payout amount for a particular premium. In the
disposal stage, insurance companies can use the commercial models
to decide when to revoke the insurance policy for a particular
client.
F. Private Equity Firms and OTC Securities Trading Systems
[0220] It is difficult for private equity firms and others that
invest in small and privately held companies to obtain information
about such companies. Commercial SoW and commercial SoSW may be
used to calculate more accurate valuations of the small and
privately held companies than previously available, and may also
provide a parameter to evaluate prospective investments.
[0221] In the acquisition stage, where a private equity firm is
researching possible investment opportunities, commercial SoW and
commercial SoSW may be used in several different ways. For
instance, the commercial models can be used to identify industries,
such as growing industries, in which the private equity firm should
invest, as well as to pinpoint specific companies in which the firm
should invest. If the industry identified is a growing industry,
the commercial models can be used to identify companies that supply
those industries. The commercial models can also be used to
identify companies that are potential candidates for
acquisition.
[0222] Investments in these small companies are typically made
through over-the-counter securities, also referred to as penny
stocks. Investors, such as private equity firms, who wish to invest
in these small companies often use an OTC securities trading
system, such as that provided by Pink Sheets LLC of New York, N.Y.
The companies listed in OTC securities trading systems typically
have one or more of the following attributes: thinly traded
securities, unwillingness or inability to be listed on the major
exchanges, and miniscule revenues. Unfortunately, since these small
companies do not need audited financial reports to be listed on the
trading systems, investments in these companies are often made with
insubstantial information, false information, or out-of-date
information. The data listed on the trading systems may not be
independently verifiable, and few analysts follow the companies
listed on the OTC securities trading systems.
[0223] These trading systems thus need accurate, up-to-date
information, as well as a tool to separate worthy companies from
unworthy companies and a tool to rank companies. Commercial SoW
and/or commercial SoSW can be used by the trading systems to
provide scores or data to evaluate the listed companies, enabling
the trading systems to rank the listed companies. The commercial
models may also be used to corroborate data already listed by the
trading systems. This allows the trading systems to separate
worthwhile companies out from bad companies, and offers a simple,
though robust, explanation of the rationale behind the
rankings.
[0224] In the retention stage, once a private equity firm has
established a portfolio of small companies in which the firm is
invested, commercial SoW and SoSW can be used to determine how the
investments should be maintained. For instance, the commercial
models can be used to determine which of the companies in the
portfolio warrant an increased or decreased investment. In the
disposal stage, private equity firms can use the commercial models
to determine when they should release their investment.
G. Online Marketplaces
[0225] Online marketplaces for small businesses may also benefit
from commercial SoW and commercial SoSW. These online marketplaces
allow small businesses or vendors to advertise their services. An
example online marketplace is TheKnot.com, which provides a space
for related businesses, such as wedding photographers, to advertise
their services.
[0226] Because the small businesses or vendors who use these online
marketplaces are usually unregulated, there typically is not a
central repository of information about them. Further, there is no
way to predict which of the small businesses will succeed and
remain in business, or which will go out of business. If an
advertised company suddenly goes out of business, visitors to the
marketplace who relied on the advertisement may end up losing
money.
[0227] With commercial SoW and commercial SoSW, online marketplaces
can provide a rating to each vendor listed on their sites that
gives an indication of the business prospects of the vendor.
Further, the online marketplaces could combine commercial SoW and
commercial SoSW with their own internal analytics to provide a
single holistic rating. For example, the marketplace could use an
alphanumeric rating scale for vendors listed on its site, where
ratings for the quality of references from previous customers are
combined with ratings based on the quality of the commercial SoW
and/or commercial SoSW score. In this example, the quality of
customer references ratings may range, for example and without
limitation, from A (high) to F (low) while the quality of the
commercial models score may range from 1 (low) to 5 (high).
[0228] Thus, a score of 5A may mean that the vendor has excellent
business prospects and excellent references from previous
customers. A score of 1A may mean that the vendor has excellent
references from previous customers, but that, according to the
commercial models, the vendor has dwindling or mediocre business
prospects. A score of 1F may mean that the vendor has dwindling or
mediocre business prospects along with very bad customer
references.
[0229] This type of rating minimizes the effects of asymmetry of
previously available information, and effectively allows an online
marketplace to operate similarly to a Better Business Bureau.
Consumers and prospective clients of the vendors can then use the
ratings as a factor when deciding whether or not to patronize a
particular vendor. In addition, the online marketplace can use the
ratings to determine when a vendor should be removed from its site
listing.
H. Marketing Companies
[0230] Certain marketing or research companies sell to customers
lists of people and/or businesses which meet certain criteria set
out by the customers. These lists are typically compiled by
searching one or more databases for names and/or businesses that
match the criteria. An example list may include, for example, all
the companies in a particular zip code that have revenues in a
given range, and which have a given number of employees.
[0231] These lists typically rely on static aggregations of
geographical and historical financial data. Companies who do
provide some sort of dynamic analysis do not, however, provide a
customized score that simultaneously encapsulates and predicts both
income statement and balance sheet items for each company on the
list. For example, predictive measurements offered by Dun &
Bradstreet allow users of the measurements to predict the
likelihood of late payment, financial stress, and future payment
habits. However, the measurements do not predict future spend
and/or revenues.
[0232] With commercial SoW and commercial SoSW, list sellers may
provide lists that show predicted spend and/or predicted revenues
for each company on a list, in addition to the same predictions
previously offered by existing list sellers. This type of "smart
list" is more valuable to list buyers, as it contains more useful
information than previously available lists. For example, the list
sellers may use a score based on the commercial models to rank
and/or rate each company on the list, or the list seller may
provide a list of companies that meet given requirements of
predicted spend and predicted revenue. The list buyers can then use
the rating to determine to whom they should market, to whom they
should lend and/or sell, who they should retain as clients, and
with whom they should sever relationships.
I. Mutual Fund Raters and Stock Screening Providers
[0233] Mutual fund rating companies (such as Morningstar, Inc., of
Chicago, Ill. and Standard & Poor's of New York, N.Y.) and/or
providers of stock screening tools (such as Microsoft Corp. of
Redmond, Wash. and Yahoo! Inc. of Sunnyvale, Calif.) review a
particular fund's historical performance and compare that
performance to several factors. These factors include, for example,
the performance of other funds in the same peer group of the rated
fund. Because mutual funds do not typically disclose their
holdings, the rating and screening companies are not always able to
analyze the individual stocks in a particular fund's portfolio.
Notwithstanding, these companies can use commercial SoW and/or
commercial SoSW to predict the performance of funds that invest in
a particular industry or sector.
[0234] Mutual funds often provide guidelines for selecting stocks.
For example, a focused fund might invest only in companies within a
certain size range and located in a particular geography. Thus, the
rating and screening companies can use standard statistical and
probability analyses to predict which companies are likely to be in
the mutual fund's portfolio. The rating and screening companies can
then combine the results of the probability analyses with
commercial SoW and/or commercial SoSW scores to predict the
performance of the companies in the fund's portfolio. The predicted
performance forms a basis for a rating assigned to the fund.
[0235] Alternatively, the rating and screening companies can
provide the commercial SoW and/or commercial SoSW scores side by
side with traditional fund ratings. This shows the prospects for
the industry that the rated fund invests in.
J. Providers of Company Information
[0236] Providers of company information, such as Dun &
Bradstreet Corp. and Hoover's, Inc., of Austin, Tex., provide a
wealth of information about companies and industries. With respect
to financial data, Hoover's database essentially repackages and
presents what companies have previously reported along with, where
applicable, analyst predictions. Unlike Dun & Bradstreet's
database, which has well-known ratings, Hoover's database does not
have any proprietary ratings. Instead, it simply aggregates and
reports the ratings supplied by other ratings companies. Further,
subscribers to Hoover's database can also search the Dun &
Bradstreet database.
[0237] In addition to the standard Dun & Bradstreet report
about companies, Dun & Bradstreet also provides a PAYDEX score.
This score is a dollar-weighted numerical indicator of a company's
bill payment routines over the previous year. This indicator is
based on vendor reports made to Dun & Bradstreet, and indicates
whether the company has a low, medium, or high risk of late
payment. Although the PAYDEX score summarizes payment history at
both the company and industry levels, it does not provide an
indication of how much the rated company is likely to spend in the
future.
[0238] By incorporating commercial SoW and/or commercial SoSW
scores into their reports, company information providers can
provide their subscribers with more valuable information in their
reports. For example, by including commercial SoW and/or commercial
SoSW scores in the typical Dun & Bradstreet reports, users of
the reports would receive an indication of how much the company is
likely to spend in the future, in addition to the company's payment
history and past financial performance.
VI. System Implementations
[0239] The present invention may be implemented using hardware,
software or a combination thereof and may be implemented in one or
more computer systems or other processing systems. However, the
manipulations performed by the present invention were often
referred to in terms, such as adding or comparing, which are
commonly associated with mental operations performed by a human
operator. No such capability of a human operator is necessary, or
desirable in most cases, in any of the operations described herein
which form part of the present invention. Rather, the operations
are machine operations. Useful machines for performing the
operation of the present invention include general purpose digital
computers or similar devices.
[0240] In fact, in one embodiment, the invention is directed toward
one or more computer systems capable of carrying out the
functionality described herein. An example of a computer system
3200 is shown in FIG. 32.
[0241] The computer system 3200 includes one or more processors,
such as processor 3204. The processor 3204 is connected to a
communication infrastructure 3206 (e.g., a communications bus,
cross-over bar, or network). Various software embodiments are
described in terms of this exemplary computer system. After reading
this description, it will become apparent to a person skilled in
the relevant art(s) how to implement the invention using other
computer systems and/or architectures.
[0242] Computer system 3200 can include a display interface 3202
that forwards graphics, text, and other data from the communication
infrastructure 3206 (or from a frame buffer not shown) for display
on the display unit 3230.
[0243] Computer system 3200 also includes a main memory 3208,
preferably random access memory (RAM), and may also include a
secondary memory 3210. The secondary memory 3210 may include, for
example, a hard disk drive 3212 and/or a removable storage drive
3214, representing a floppy disk drive, a magnetic tape drive, an
optical disk drive, etc. The removable storage drive 3214 reads
from and/or writes to a removable storage unit 3218 in a well known
manner. Removable storage unit 3218 represents a floppy disk,
magnetic tape, optical disk, etc. which is read by and written to
by removable storage drive 3214. As will be appreciated, the
removable storage unit 3218 includes a computer usable storage
medium having stored therein computer software and/or data.
[0244] In alternative embodiments, secondary memory 3210 may
include other similar devices for allowing computer programs or
other instructions to be loaded into computer system 3200. Such
devices may include, for example, a removable storage unit 3218 and
an interface 3220. Examples of such may include a program cartridge
and cartridge interface (such as that found in video game devices),
a removable memory chip (such as an erasable programmable read only
memory (EPROM), or programmable read only memory (PROM)) and
associated socket, and other removable storage units 3218 and
interfaces 3220, which allow software and data to be transferred
from the removable storage unit 3218 to computer system 3200.
[0245] Computer system 3200 may also include a communications
interface 3224. Communications interface 3224 allows software and
data to be transferred between computer system 3200 and external
devices. Examples of communications interface 3224 may include a
modem, a network interface (such as an Ethernet card), a
communications port, a Personal Computer Memory Card International
Association (PCMCIA) slot and card, etc. Software and data
transferred via communications interface 3224 are in the form of
signals 3228 which may be electronic, electromagnetic, optical or
other signals capable of being received by communications interface
3224. These signals 3228 are provided to communications interface
3224 via a communications path (e.g., channel) 3226. This channel
3226 carries signals 3228 and may be implemented using wire or
cable, fiber optics, a telephone line, a cellular link, a radio
frequency (RF) link and other communications channels.
[0246] In this document, the terms "computer program medium" and
"computer usable medium" are used to generally refer to media such
as removable storage drive 3214, a hard disk installed in hard disk
drive 3212, and signals 3228. These computer program products
provide software to computer system 3200. The invention is directed
to such computer program products.
[0247] Computer programs (also referred to as computer control
logic) are stored in main memory 3208 and/or secondary memory 3210.
Computer programs may also be received via communications interface
3224. Such computer programs, when executed, enable the computer
system 3200 to perform the features of the present invention, as
discussed herein. In particular, the computer programs, when
executed, enable the processor 3204 to perform the features of the
present invention. Accordingly, such computer programs represent
controllers of the computer system 3200.
[0248] In an embodiment where the invention is implemented using
software, the software may be stored in a computer program product
and loaded into computer system 3200 using removable storage drive
3214, hard drive 3212 or communications interface 3224. The control
logic (software), when executed by the processor 3204, causes the
processor 3204 to perform the functions of the invention as
described herein.
[0249] In another embodiment, the invention is implemented
primarily in hardware using, for example, hardware components such
as application specific integrated circuits (ASICs). Implementation
of the hardware state machine so as to perform the functions
described herein will be apparent to persons skilled in the
relevant art(s).
[0250] In yet another embodiment, the invention is implemented
using a combination of both hardware and software.
VII. Conclusion
[0251] While various embodiments of the present invention have been
described above, it should be understood that they have been
presented by way of example, and not limitation. It will be
apparent to persons skilled in the relevant art(s) that various
changes in form and detail can be made therein without departing
from the spirit and scope of the present invention. Thus, the
present invention should not be limited by any of the above
described exemplary embodiments, but should be defined only in
accordance with the following claims and their equivalents.
[0252] In addition, it should be understood that the figures and
screen shots illustrated in the attachments, which highlight the
functionality and advantages of the present invention, are
presented for example purposes only. The architecture of the
present invention is sufficiently flexible and configurable, such
that it may be utilized (and navigated) in ways other than that
shown in the accompanying figures.
[0253] Further, the purpose of the foregoing Abstract is to enable
the U.S. Patent and Trademark Office and the public generally, and
especially the scientists, engineers and practitioners in the art
who are not familiar with patent or legal terms or phraseology, to
determine quickly from a cursory inspection the nature and essence
of the technical disclosure of the application. The Abstract is not
intended to be limiting as to the scope of the present invention in
any way.
* * * * *