Reducing risks related to check verification

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.

What is claimed is: 
     
         1 . A computer-based method comprising:
 determining, by a computer-based system configured for reducing merchant risks, a risk value of the customer based on an estimated credit-related characteristic, wherein the estimated credit-related characteristic of the customer is based on a model of consumer spending patterns, and wherein previous balance transfers are offset from the estimated credit-related characteristic; and   analyzing a check based on the determined risk value.   
     
     
         2 . The method of  claim 1 , wherein the model uses individual consumer data and aggregate consumer data. 
     
     
         3 . The method of  claim 1 , wherein the model uses individual consumer data and aggregate consumer data, including tradeline data, internal customer data, and consumer panel data. 
     
     
         4 . The method of  claim 1 , wherein the estimated credit-related characteristic is further based on tradeline data of the customer and identified balance transfers of the customer. 
     
     
         5 . The method of  claim 1 , wherein the determining comprises determining whether the customer is a high-risk customer or a low-risk customer. 
     
     
         6 . The method of  claim 1 , further comprising:
 accepting the check in response to the customer being a low-risk customer; and   declining the check in response to the customer being a high-risk customer.   
     
     
         7 . The method of  claim 1 , wherein the determining comprises:
 weighting at least one factor relating to the customer's check based on the estimated credit characteristic; and   calculating a risk value based on the weighted factor.   
     
     
         8 . The method of  claim 7 , wherein factors relating to the customer's check include at least one of velocity, prior activity, the customer's presence in check databases, size of the check, prior bad check activity by geographic location, or prior bad check activity by merchant location. 
     
     
         9 . The method of  claim 1 , wherein the estimated credit-related characteristic is at least one of: a size of the customer's spending wallet, a size of the customer's revolving spending, a size of the customer's transacting spending, a share of wallet size for a particular spend category, a spend capacity of the customer, or an amount of balance transfers transacted by the customer. 
     
     
         10 . The method of  claim 1 , wherein the analyzing the check comprises making a check acceptance decision. 
     
     
         11 . The method of  claim 1 , wherein the analyzing the check comprises assessing a collection probability of a dishonored check. 
     
     
         12 . An article of manufacture including a non-transitory, tangible computer readable storage medium having instructions stored thereon that, in response to execution by a computer-based system configured for reducing merchant risks, cause the computer-based system to perform operations comprising:
 determining, by the computer-based system, a risk value of the customer based on an estimated credit-related characteristic, wherein the estimated credit-related characteristic of the customer is based on a model of consumer spending patterns, and wherein previous balance transfers are offset from the estimated credit-related characteristic; and   analyzing a check based on the determined risk value.   
     
     
         13 . The article of  claim 12 , wherein the model uses individual consumer data and aggregate consumer data. 
     
     
         14 . The article of  claim 12 , wherein the model uses individual consumer data and aggregate consumer data, including tradeline data, internal customer data, and consumer panel data. 
     
     
         15 . The article of  claim 12 , wherein the estimated credit-related characteristic is further based on tradeline data of the customer and identified balance transfers of the customer. 
     
     
         16 . The article of  claim 12 , further comprising:
 accepting the check in response to the customer being a low-risk customer; and   declining the check in response to the customer being a high-risk customer.   
     
     
         17 . The article of  claim 12 , wherein the determining comprises:
 weighting at least one factor relating to the customer's check based on the estimated credit characteristic; and   calculating a risk value based on the weighted factor.   
     
     
         18 . The article of  claim 17 , wherein factors relating to the customer's check include at least one of velocity, prior activity, the customer's presence in check databases, size of the check, prior bad check activity by geographic location, or prior bad check activity by merchant location. 
     
     
         19 . The article of  claim 12 , wherein the estimated credit-related characteristic is at least one of: a size of the customer's spending wallet, a size of the customer's revolving spending, a size of the customer's transacting spending, a share of wallet size for a particular spend category, a spend capacity of the customer, or an amount of balance transfers transacted by the customer. 
     
     
         20 . A system comprising:
 a processor configured for reducing merchant risks;   a tangible, non-transitory memory configured to communicate with the processor;   a determining module in communication with the processor and configured to determine a risk value of the customer based on an estimated credit-related characteristic, wherein the estimated credit-related characteristic of the customer is based on a model of consumer spending patterns, and wherein previous balance transfers are offset from the estimated credit-related characteristic; and   an analyzing module in communication with the processor and configured to analyze a check based on the determined risk value.