Source: http://www.google.com.tw/patents/US8086509
Timestamp: 2013-05-25 17:33:14
Document Index: 67586801

Matched Legal Cases: ['Application No. 2006285253', 'Application No. 2006285253', 'Application No. 2006285253', 'Application No. 2006285253', 'Application No. 2006285253', 'Application No. 2008']

�M�Q US8086509 - Determining commercial share of wallet - Google �M�Q�j�M �Ϥ� �a�� Play YouTube �s�D Gmail ���ݵw�� ��h »�i���M�Q�j�M | �������� | �n�J�i���M�Q�j�M�M�QCommercial size of spending wallet (��SoSW��) is the total business spend of a business including cash but excluding bartered items. Commercial share of wallet (��SoW��) is the portion of the spending wallet that is captured by a particular financial company. Commercial SoW is a modeling approach...http://www.google.com.tw/patents/US8086509?utm_source=gb-gplus-share�M�Q US8086509 - Determining commercial share of wallet���}��US8086509 B2�X���������v�ӽЮѽs��11/497,563�o�G���2011�~12��27���ӽФ��2006�~8��2�� �u���v���2004�~10��29����L���}�M�Q��US8195550US20070067209US20120066108�o��HArnab BiswasKathleen HaggertyCharles Christopher LyonRobert E. PhelanJon Kevin RutermanGeraldine A. TurnerChao M. Yuan��M�Q�v�HAmerican Express Travel Related Services Company, Inc. ���M�Q������705/35��ڱM�Q������G06Q40/00 �X�@����G06Q40/00G06Q40/08 �ڬw������G06Q 40/08G06Q 40/00�ѦҤ��m�M�Q�ޥ� (102)�D�M�Q�ޥ� (169)�~���s�����M�Q�ӼЧ� ���M�Q�ӼЧ��M�Q����T�� �ڬw�M�Q��Determining commercial share of walletUS 8086509 B2�K�n Commercial size of spending wallet (��SoSW��) is the total business spend of a business including cash but excluding bartered items. Commercial share of wallet (��SoW��) is the portion of the spending wallet that is captured by a particular financial company. Commercial SoW is a modeling approach that utilizes various data sources to provide outputs that describe a company's spend capacity. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new account evaluation, and customer management across the lifecycle. Company financial statements are utilized to identify and calculate total business spend of a company that could be transacted using a commercial credit card. A spend-like regression model may then be developed to estimate annual commercial SoSW value for customers and prospects within a credit network.
a. U.S. patent application Ser. No. 11/394,165, filed Mar. 31, 2006, entitled ��Using Commercial Share of Wallet to Determnine Insurance Risk��; b. U.S. patent application Ser. No. 11/394,166, filed Mar. 31, 2006, entitled ��Using Commercial Share of Wallet to Manage Vendors��; c. U.S. patent application Ser. No. 11/394,169, filed Mar. 31, 2006, entitled ��Using Commercial Share of Wallet to Rate Business Prospects��; d. U.S. patent application Ser. No. 11/394,206, filed Mar. 31, 2006, entitled ��Using Commercial Share of Wallet to Manage Investments��; e. U.S. patent application Ser. No. 11/394,217, filed Mar. 31, 2006, entitled ��Using Commercial Share of Wallet to Make Lending Decisions��; f. U.S. patent application Ser. No. 11,497,521, filed Aug. 2, 2006, entitled ��Using Commercial Share of Wallet in Private Equity Investments,��; g. U.S. patent application Ser. No. 11/497,529, filed Aug. 2, 2006, entitled ��Using Commercial Share to Analyze Vendors in Online Marketplaces,��; h. U.S. patent application Ser. No. 11/497,530, filed Aug. 2, 2006, entitled ��Using Commercial Share of Wallet in Financial Databases,��; i. U.S. patent application Ser. No. 11/497,562, filed Aug. 2, 2006, entitled ��Using Commercial Share of Wallet to Rate Investments,��; and j. U.S. patent application Ser. No. 11/497,527, filed Aug. 2, 2006, entitled ��Using Commercial Share of Wallet to Compile Marketing Company Lists,��. BACKGROUND OF THE INVENTION 1. Field of the Invention
For the purposes of this disclosure, 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. 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.
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.
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. 30 is a chart illustrating the active number of ��OPEN: The Small Business Network�� (OSBN) accounts by quintile according to an embodiment of the present invention.
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.
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.
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 and development.
In further embodiments, the population of current consumers is then subdivided into a plurality of further categories based on the amount of balance information available and the balance activity of such available data. In the example shown in the diagram 300, the amount of balance information available is represented by string of ��+�� ��0�� and ��?�� characters. Each character represents one month of available data, with the rightmost character representing the most current months and the leftmost character representing the earliest month for which data is available. In the example provided in FIG. 3, a string of six characters is provided, representing the six most recent months of data for each category. The ��+�� character represents a month in which a credit account balance of the consumer has increased. The ��0�� character may represent months where the account balance is zero. The ��?�� character represents months for which balance data is unavailable. Also provided in the diagram is the number of consumers who fall in to each category and the percentage of the consumer population represent by that sample.
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.
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 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 consumer 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 businesses.
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:
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.
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 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 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.
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.
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.
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.
The SoW model may also be useful to merchants accepting checks at a point of sale (��POS��). Before accepting a check from a consumer at a POS as a form of payment, merchants typically��verify�� the check or request a��check guarantee��. The verification and/or guarantee are usually provided by outside service providers.
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.
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.
Banks, lenders, and credit providers (referred to collectively herein as ��lenders��) lend money based on a borrower's credit rating and collateral. Even when loans are secured by collateral, though, there is no guarantee that the value of the collateral will not depreciate over time to a value that is below the outstanding loan balance. While a credit rating of the borrower may be a good indicator of a borrower's willingness to repay, it is not a good indicator of borrower's future ability to repay. By predicting future spend, the commercial SoW and commercial SoSW models provide a score that is, effectively, a proxy for predicting a borrower's ability to repay.
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.
A research analyst provides a rating that summarizes the analyst's opinion about the quality and/or prospects of the rated company's securities. Such a rating might be��BUY,�� ��HOLD,�� or��SELL�� for equity, or��A,�� ��B,�� ��C,�� or��JUNK�� for debt. Whether conducting analyses that would result in a rating for debt or equity, analysts review a company's performance, management and prospects, among other things.
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.
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.
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