Abstract: Improved systems and methods are provided for identifying financial relationships. In particular, financial relationships may be identified by associating tradelines with one or more people who sign or co-sign on the tradeline. In various embodiments a method is provided comprising, receiving, at a computer-based system for credit data analysis comprising a processor and a tangible, non-transitory memory, credit reporting data relating to a tradeline, parsing, by the computer-based system, the credit reporting data to yield primary debtor data and secondary debtor data, and linking, by the computer-based system, the tradeline with the primary debtor data and the secondary debtor data.

Abstract: Improved systems and methods are provided for identifying financial relationships. In particular, financial relationships may be identified by associating tradelines with one or more people who sign or co-sign on the tradeline. In various embodiments a method is provided comprising, receiving, at a computer-based system for credit data analysis comprising a processor and a tangible, non-transitory memory, credit reporting data relating to a tradeline, parsing, by the computer-based system, the credit reporting data to yield primary debtor data and secondary debtor data, and linking, by the computer-based system, the tradeline with the primary debtor data and the secondary debtor data.

Abstract: The spend capacity of a consumer typically increases as the number of consumers in the household increases, since the consumer can draw on the spending power of other consumers in the household. The size of wallet of the household is thus a better indicator of the consumer's spend capacity than an individual size of wallet. All consumers in a given household can be aggregated based on, for example, their address of record. Duplicate tradelines within each household are removed from consideration in a size of wallet estimate. A spend capacity is then estimated for each tradeline using calculations derived from a consumer behavior model. The spend capacities for all tradelines in the household are combined to determine a household size of wallet. Each consumer in the household is then tagged with the household size of wallet, rather than their individual size of wallet.

Abstract: The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

Abstract: The present disclosure generally relates to financial data processing, and in particular it relates to lender credit scoring, lender profiling, lender behavior analysis and modeling. More specifically, it relates to rating lenders based on data derived from their respective consumers. Also, the present disclosure relates to rating consumer lenders based on the predicted spend capacity of their consumers.

Abstract: The spend capacity of a consumer typically increases as the number of consumers in the household increases, since the consumer can draw on the spending power of other consumers in the household. The size of wallet of the household is thus a better indicator of the consumer's spend capacity than an individual size of wallet. All consumers in a given household can be aggregated based on, for example, their address of record. Duplicate tradelines within each household are removed from consideration in a size of wallet estimate. A spend capacity is then estimated for each tradeline using calculations derived from a consumer behavior model. The spend capacities for all tradelines in the household are combined to determine a household size of wallet. Each consumer in the household is then tagged with the household size of wallet, rather than their individual size of wallet.

Abstract: The spend capacity of a consumer typically increases as the number of consumers in the household increases, since the consumer can draw on the spending power of other consumers in the household. The size of wallet of the household is thus a better indicator of the consumer's spend capacity than an individual size of wallet. All consumers in a given household can be aggregated based on, for example, their address of record. Duplicate tradelines within each household are removed from consideration in a size of wallet estimate. A spend capacity is then estimated for each tradeline using calculations derived from a consumer behavior model. The spend capacities for all tradelines in the household are combined to determine a household size of wallet. Each consumer in the household is then tagged with the household size of wallet, rather than their individual size of wallet.

Abstract: Improved systems and methods are provided for identifying financial relationships. In particular, financial relationships may be identified by associating tradelines with one or more people who sign or co-sign on the tradeline. In various embodiments a method is provided comprising, receiving, at a computer-based system for credit data analysis comprising a processor and a tangible, non-transitory memory, credit reporting data relating to a tradeline, parsing, by the computer-based system, the credit reporting data to yield primary debtor data and secondary debtor data, and linking, by the computer-based system, the tradeline with the primary debtor data and the secondary debtor data.

Abstract: Improved systems and methods are provided for identifying financial relationships. In particular, financial relationships may be identified by associating tradelines with one or more people who sign or co-sign on the tradeline. In various embodiments a method is provided comprising, receiving, at a computer-based system for credit data analysis comprising a processor and a tangible, non-transitory memory, credit reporting data relating to a tradeline, parsing, by the computer-based system, the credit reporting data to yield primary debtor data and secondary debtor data and linking, by the computer-based system, the tradeline with the primary debtor data and the secondary debtor data.

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 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 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: 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. Research analysts can use CSoW/CSoSW to provide a comprehensive and robust indication of the business prospects of a rated company.

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 (“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.

Abstract: Share of Wallet (“SOW”) is a modeling approach that utilizes various data sources to provide scores that describe a consumers spending capability, tradeline history including balance transfers, and balance information. Share of wallet scores can be used as a parameter for determining whether or not to accept and/or guarantee a check. The share of wallet can be used to calculate a risk value of a customer. For example, the scores can weight one or more factors related to the check writer and differentiate between a low-risk customer and a high-risk customer.

Abstract: The present disclosure generally relates to financial data processing, and in particular it relates to lender credit scoring, lender profiling, lender behavior analysis and modeling. More specifically, it relates to rating lenders based on data derived from their respective consumers. Also, the present disclosure relates to rating consumer lenders based on the predicted spend capacity of their consumers.

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. Research analysts can use CSoW/CSoSW to provide a comprehensive and robust indication of the business prospects of a rated company.

Abstract: 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.

Abstract: Improved systems and methods are provided for identifying financial relationships. In particular, financial relationships may be identified by associating tradelines with one or more people who sign or co-sign on the tradeline. In various embodiments a method is provided comprising, receiving, at a computer-based system for credit data analysis comprising a processor and a tangible, non-transitory memory, credit reporting data relating to a tradeline, parsing, by the computer-based system, the credit reporting data to yield primary debtor data and secondary debtor data, and linking, by the computer-based system, the tradeline with the primary debtor data and the secondary debtor data.