Abstract: Commercial size of spending wallet (“CSoSW”) is the total business spend of a business including cash but excluding bartered items. Commercial share of wallet (“CSoW”) is the portion of the spending wallet that is captured by a particular financial company. A modeling approach utilizes various data sources to provide outputs that describe a company's spend capacity. Marketing companies that sell lists compile those lists by searching one or more databases for names and/or businesses that match certain criteria. Those marketing companies can use the CSoW/CSoSW modeling approach to show predicted spend and/or revenues for each company on a list. This makes the list more valuable to list buyers.

Abstract: Commercial size of spending wallet (“CSoSW”) is the total business spend of a business including cash but excluding bartered items. Commercial share of wallet (“CSoW”) is the portion of the spending wallet that is captured by a particular financial company. A modeling approach utilizes various data sources to provide outputs that describe a company's spend capacity. Private equity firms and other investors of small businesses can use the CSoW/CSoSW modeling approach to more accurately evaluate small and privately held companies, both during investment and for evaluating prospective investments. Over-the-counter securities trading systems can also use this modeling approach to provide more accurate information and/or rankings of listed companies to their customers.

Abstract: Commercial size of spending wallet (“CSoSW”) is the total business spend of a business including cash but excluding bartered items. Commercial share of wallet (“CSoW”) is the portion of the spending wallet that is captured by a particular financial company. A modeling approach utilizes various data sources to provide outputs that describe a company's spend capacity. A mutual fund rating company can use this CSoW/CSoSW modeling approach to predict the performance of funds that invest in a particular industry or sector. In addition, since mutual funds often provide guidelines for selecting stocks, rating companies can use this modeling approach to predict the performance of companies in a fund's portfolio.

Abstract: Time series consumer spending data, point-in-time balance information, internal customer financial data and consumer panel information provides input to a model for consumer spend behavior on plastic instruments or other financial accounts, from which approximations of spending ability may be reliably identified and utilized to promote additional consumer spending.

Abstract: Commercial size of spending wallet (“CSoSW”) is the total business spend of a business including cash but excluding bartered items. Commercial share of wallet (“CSoW”) is the portion of the spending wallet that is captured by a particular financial company. A modeling approach utilizes various data sources to provide outputs that describe a company's spend capacity. Private equity firms and other investors of small businesses can use the CSoW/CSoSW modeling approach to more accurately evaluate small and privately held companies, both during investment and for evaluating prospective investments. Over-the-counter securities trading systems can also use this modeling approach to provide more accurate information and/or rankings of listed companies to their customers.

Abstract: Share of Wallet (“SOW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumers spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. The outputs can be used as attributes to consider in developing a credit bureau scorecard.

Abstract: Commercial size of spending wallet (“CSoSW”) is the total business spend of a business including cash but excluding bartered items. Commercial share of wallet (“CSoW”) is the portion of the spending wallet that is captured by a particular financial company. A modeling approach utilizes various data sources to provide outputs that describe a company's spend capacity. A mutual fund rating company can use this CSoW/CSoSW modeling approach to predict the performance of funds that invest in a particular industry or sector. In addition, since mutual funds often provide guidelines for selecting stocks, rating companies can use this modeling approach to predict the performance of companies in a fund's portfolio.

Abstract: Commercial size of spending wallet (“CSoSW”) is the total business spend of a business including cash but excluding bartered items. Commercial share of wallet (“CSoW”) is the portion of the spending wallet that is captured by a particular financial company. A modeling approach utilizes various data sources to provide outputs that describe a company's spend capacity. A mutual fund rating company can use this CSoW/CSoSW modeling approach to predict the performance of funds that invest in a particular industry or sector. In addition, since mutual funds often provide guidelines for selecting stocks, rating companies can use this modeling approach to predict the performance of companies in a fund's portfolio.

Abstract: Share of Wallet (“SOW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumers spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. A SOW score focusing on a consumer's spending capability can be used in the same manner as a credit bureau score.

Abstract: Share of Wallet (“SoW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumer's spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. In addition to credit card companies, SoW outputs may be useful to companies issuing, for example: private label cards, life insurance, on-line brokerages, mutual funds, car sales/leases, hospitals, and home equity lines of credit or loans. “Best customer” models can correlate SoW outputs with various customer groups. A SoW score focusing on a consumer's spending capacity can be used in the same manner as a credit bureau score.

Abstract: A portfolio benchmarking system comprises a repository of trade data, a repository of consumer data, a build computer, and a benchmarking query application. The repository of trade data comprises a plurality of data items regarding trade lines. The repository of consumer data comprises a plurality of data items regarding consumers, wherein at least some information in the consumer data is not in the trade data and at least some information in the trade data is not in the consumer data. The build computer periodically generates at least one data file comprising a plurality of data items, each data item combining information from the trade data and the consumer data, such that searches can be performed on the combined data without joining trade data and consumer data at query run time. The benchmarking query application executes queries on the data file generated by the build computer.

Abstract: Share of Wallet (“SoW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumer's spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. The likelihood of default determined by the SoW model, when applied to a loan portfolio, can reduce the amount of credit enhancement required for an asset-backed securities rating.

Abstract: A portfolio benchmarking system comprises a repository of trade data, a repository of consumer data, a build computer, and a benchmarking query application. The repository of trade data comprises a plurality of data items regarding trade lines. The repository of consumer data comprises a plurality of data items regarding consumers, wherein at least some information in the consumer data is not in the trade data and at least some information in the trade data is not in the consumer data. The build computer periodically generates at least one data file comprising a plurality of data items, each data item combining information from the trade data and the consumer data, such that searches can be performed on the combined data without joining trade data and consumer data at query run time. The benchmarking query application executes queries on the data file generated by the build computer.

Abstract: A portfolio benchmarking system comprises a repository of trade data, a repository of consumer data, a build computer, and a benchmarking query application. The repository of trade data comprises a plurality of data items regarding trade lines. The repository of consumer data comprises a plurality of data items regarding consumers, wherein at least some information in the consumer data is not in the trade data and at least some information in the trade data is not in the consumer data. The build computer periodically generates at least one data file comprising a plurality of data items, each data item combining information from the trade data and the consumer data, such that searches can be performed on the combined data without joining trade data and consumer data at query run time. The benchmarking query application executes queries on the data file generated by the build computer.

Abstract: A portfolio benchmarking system comprises a repository of trade data, a repository of consumer data, a build computer, and a benchmarking query application. The repository of trade data comprises a plurality of data items regarding trade lines. The repository of consumer data comprises a plurality of data items regarding consumers, wherein at least some information in the consumer data is not in the trade data and at least some information in the trade data is not in the consumer data. The build computer periodically generates at least one data file comprising a plurality of data items, each data item combining information from the trade data and the consumer data, such that searches can be performed on the combined data without joining trade data and consumer data at query run time. The benchmarking query application executes queries on the data file generated by the build computer.

Abstract: Time series consumer spending data, point-in-time balance information and consumer panel information provide input to a model for consumer spend behavior on plastic instruments or other financial accounts, from which approximations of spending ability and share of wallet may be reliably identified and utilized to promote additional consumer spending.

Abstract: A portfolio benchmarking system comprises a repository of trade data, a repository of consumer data, a build computer, and a benchmarking query application. The repository of trade data comprises a plurality of data items regarding trade lines. The repository of consumer data comprises a plurality of data items regarding consumers, wherein at least some information in the consumer data is not in the trade data and at least some information in the trade data is not in the consumer data. The build computer periodically generates at least one data file comprising a plurality of data items, each data item combining information from the trade data and the consumer data, such that searches can be performed on the combined data without joining trade data and consumer data at query run time. The benchmarking query application executes queries on the data file generated by the build computer.

Abstract: Commercial size of spending wallet (“CSoSW”) is the total business spend of a business including cash but excluding bartered items. Commercial share of wallet (“CSoW”) is the portion of the spending wallet that is captured by a particular financial company. A modeling approach utilizes various data sources to provide outputs that describe a company's spend capacity. Companies which provide databases of financial information about other companies can use scores provided by this CSoW/CSoSW modeling approach to give an indication of how much the company is likely to spend in the future.

Abstract: Share of Wallet (“SOW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumer's spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. The likelihood of default determined by the SOW model, when applied to a loan portfolio, can reduce the amount of credit enhancement required for an asset-backed securities rating.

Abstract: Commercial size of spending wallet (“CSoSW”) is the total business spend of a business including cash but excluding bartered items. Commercial share of wallet (“CSoW”) is the portion of the spending wallet that is captured by a particular financial company. A modeling approach utilizes various data sources to provide outputs that describe a company's spend capacity. Online marketplaces that allow small businesses to advertise their services can use this CSoW/CSoSW modeling approach to provide a rating that gives an indication of the business prospects of the vendors listed on their sites. Further, such marketplaces can combine this information with their own internal analytics to provide a single holistic rating.