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. Insurance companies
can use CSoW/CSoSW to determine risk levels of clients and identify
prospective clients. CSoW/CSoSW can also be used to determine pricing
structures for clients.
1. A method of determining insurance risk, comprising: (a) modeling
industry spending patterns using individual and aggregate corporate
data, including financial statement data; (b) estimating a commercial
size of spending wallet of a company based on financial statement
data of the company, total business spend of the company, and the
model of industry spending patterns; and (c) determining an insurance
risk based on the commercial size of spending wallet of the company.
2. The method of claim 1, further comprising: (d) determining whether
to sell insurance to the company based on the insurance risk.
3. The method of claim 1, further comprising: (d) determining the
amount of insurance to sell to the company based on the insurance
4. The method of claim 1, further comprising: (d) determining the
premium amount for insuring the company based on the insurance risk.
5. The method of claim 1, further comprising: (d) determining a
payout amount for an insurance premium based on the insurance risk.
6. The method of claim 1, further comprising: (d) determining when
to revoke an insurance policy held by the company based on the insurance
7. The method of claim 1, wherein the insurance is key man insurance.
8. The method of claim 1, wherein the company is a customer of
a provider, and the insurance risk is determined for the provider.
9. The method of claim 1, wherein step (b) comprises: outputting
the commercial size of spending wallet as a score.
10. The method of claim 1, further comprising: estimating a share
of wallet for the company, wherein step (c) comprises determining
an insurance risk based on the share of wallet for the company.
11. A system for determining insurance risk, comprising: a processor;
and a memory in communication with the processor, the memory for
storing a plurality of processing instructions for directing the
processor to: model industry spending patterns using individual
and aggregate corporate data, including financial statement data;
estimate a commercial size of spending wallet of the company based
on financial statement data of the company, total business spend
of the company, and the model of industry spending patterns; and
determine an insurance risk based on the commercial size of spending
wallet of the company.
12. The system of claim 11, further comprising instructions for
directing the processor to: determine whether to sell insurance
to the company based on the insurance risk.
13. The system of claim 11, further comprising instructions for
directing the processor to: determine the amount of insurance to
sell to the company based on the insurance risk.
14. The system of claim 11, further comprising instructions for
directing the processor to: determine the premium amount for insuring
the company based on the insurance risk.
15. The system of claim 11, further comprising instructions for
directing the processor to: determine a payout amount for an insurance
premium based on the insurance risk.
16. The system of claim 11, further comprising instructions for
directing the processor to: determine when to revoke an insurance
policy held by the company based on the insurance risk.
17. The system of claim 11, wherein the insurance is key man insurance.
18. The system of claim 1 1, wherein the company is a customer
of a provider, and the insurance risk is determined for the provider.
19. The system of claim 11, wherein the instructions for directing
the processor to estimate comprise instructions for directing the
processor to: output the commercial size of spending wallet as a
20. The system of claim 11, further comprising instructions for
directing the processor to: estimate a share of wallet for the company,
wherein the instructions for directing the processor to determine
an insurance risk comprise instructions for directing the processor
to determine an insurance risk based on the share of wallet for
CROSS-REFERENCE TO RELATED APPLICATIONS
 This application claims the benefit of U.S. Provisional
Application No. 60/704,428, filed Aug. 2, 2005, which is incorporated
by reference herein in its entirety. This application is also a
continuation-in-part of U.S. patent application Ser. No. 11/169,588,
filed Jun. 30, 2005, which is a continuation-in-part of U.S. patent
application Ser. No. 10/978,298, filed Oct. 29, 2004, each of which
is incorporated by reference herein in its entirety.
BACKGROUND OF THE INVENTION
 1. Field of the Invention
 This disclosure generally relates to financial data processing,
and in particular it relates to credit scoring, customer profiling,
customer product offer targeting, commercial credit behavior analysis
 2. Background Art
 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
defined as businesses. The term "businesses" 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.
 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.
 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.
 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.
 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.
 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
 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.
 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.
 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.
 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.
 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.
 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.
 Insurance companies can use this information to determine
risk levels of clients and identify prospective clients. This information
can also be used to determine pricing structures for clients.
BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES
 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:
 FIG. 1 is a block diagram of an exemplary financial data
exchange network over which the processes of the present disclosure
may be performed;
 FIG. 2 is a flowchart of an exemplary consumer modeling
process performed by the financial server of FIG. 1;
 FIG. 3 is a diagram of exemplary categories of consumers
examined during the process of FIG. 2;
 FIG. 4 is a diagram of exemplary subcategories of consumers
modeled during the process of FIG. 2;
 FIG. 5 is a diagram of financial data used for model generation
and validation according to the process of FIG. 2;
 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;
 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
 FIG. 11-19 are tables showing exemplary results and outputs
of the process of FIG. 6 against a sample consumer population.
 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.
 FIG. 21 is a flowchart of a method for estimating commercial
size of spending wallet ("SoSW") according to an embodiment
of the present invention.
 FIG. 22 is a sample financial statement that may be analyzed
using the method of FIG. 21.
 FIG. 23 is a chart displaying the distribution of commercial
SoSW among OSBN HBRU businesses.
 FIG. 24 is a chart displaying the median and mean commercial
SoSW by industry.
 FIG. 25 is a chart displaying a sample share of wallet distribution
among HBRU accounts.
 FIG. 26 is a table describing the relationship between a
commercial SoSW model according to an embodiment of the invention
and business variables.
 FIG. 27 is a graph comparing actual commercial SoSW results
to predicted commercial SoSW estimates according to an embodiment
of the present invention.
 FIG. 28 is a graph comparing a commercial SoSW model according
to an embodiment of the present invention to a perfectly random
 FIG. 29 is a chart illustrating customer-level relationship
classifications according to an embodiment of the present invention.
 FIG. 30 is a chart illustrating the active number of OSBN
accounts by quintile according to an embodiment of the present invention.
 FIG. 31 is a table displaying customer counts in a scored
output file according to an embodiment of the present invention.
 FIG. 32 is a block diagram of an exemplary computer system
useful for implementing the present invention.
DETAILED DESCRIPTION OF THE INVENTION
 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.
 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.
 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.
 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.
 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
 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.
 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
 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.
 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.
 Referring now to FIGS. 1-19, 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.
 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.
 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.
 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.
 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
 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.
 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.
 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.
 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.
 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.
 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
the diagram is number of consumers fallen to each category and the
percentage of the consumer population they represent in that sample.
 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
of selected consumers for modeling in bold. 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.
 Turning now to FIG. 4, therein is depicted an exemplary
diagram 400 showing sub-categorization of the two categories of
FIG. 3 in bold 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.
 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
 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
 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.
 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.
 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
 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.
 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.
 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
 The maximum balance increase is greater than twenty times
the second maximum balance increase for the remaining months of
 The estimated pay-down percent calculated at step 306 above
is less than 40%; and
 The largest balance increase is greater than $1000 based
on the available data.
 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%.
 A third rule identifies a balance transfer for a given consumer's
credit account in any month where:
 the current balance is greater than 1.5 times the previous
 the current balance minus the previous month's balance is
greater than $4500; and
 the estimated pay-down percent from step 306 above is less
 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.
 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.
 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:
 (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.
 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
 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.
 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.
 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.
 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.
 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-B21.
 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
 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.
 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.
 Other like promotions and enhancements to consumers' experiences
are well known and may be used within the processes disclosed herein.
 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).
 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.
 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
 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.
 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.
 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.
 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.
 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.
 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.
 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
 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
 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.
 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.
 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
 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.
 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.
 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.
 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.
 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.
 Alternatively or additionally, the score may include a range
of numbers or a numeric indicator, such as an exponent, 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.
 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.
 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.
 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
 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.
 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.
 In step 2006, it is determined whether there is any correlation
between particular model outputs and the customer categories.
 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.
 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
 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.
 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
 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.
 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.
 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.
 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.
 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+(TotalOpEx*Cardable
OpExRatio) 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.
 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
 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.
 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 emloyees 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.
 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.
 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.
 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
 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.
 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.
 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
 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.
 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.
 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.
 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.
 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
 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.
 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.
 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.
 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
 IV. Applicable Market Segments/Industries for SoW
 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.
 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
 A. Financial Services Companies
 Financial services companies include, for example and without
limitation: banks and lenders, mutual find companies, financiers
of leases and sales, life insurance companies, online brokerages,
and loan buyers.
 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.
 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.
 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
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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
 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
 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.
 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.
 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
 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
 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.
 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
 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.
 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
 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.
 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.
 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.
 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
 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.
 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.
 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.
 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.
 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.
 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
and other lenders reduces the contingency fees that it pays to collection
agencies, and maximizes the amount collected by the in-house collection
 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.
 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.
 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.
 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
 V. Applicable Market Segments/Industries for Commercial
SoW and Commercial SoSW
 A. Banks, Lenders, and Credit Providers
 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
 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
 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.
 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
 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.
 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.
 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, finds 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.
 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.
 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.
 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
 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
 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.
 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.
 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.
 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
 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.
 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.
 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 yaers,
and small-business type certifications do not provide an indication
of the likelihood that a particular business would continue as a
 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.
 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.
 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
 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.
 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
 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.
 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.
 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.
 VI. System Implementations
 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.
 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.
 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
 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.
 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.
 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
 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
 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.
 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.
 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
 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).
 In yet another embodiment, the invention is implemented
using a combination of both hardware and software.
 VII. Conclusion
 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.
 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
 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.