Computer-Implemented Data Storage Systems And Methods For Use With Predictive Model Systems

Abstract

Systems and methods for performing fraud detection. As an example, a system and method can be configured to contain a raw data repository for storing raw data related to financial transactions. A data store contains rules to indicate how many generations or to indicate a time period within which data items are to be stored in the raw data repository. Data items stored in the raw data repository are then accessed by a predictive model in order to perform fraud detection.

1 . A computer-implemented fraud scoring system comprising:
 raw data repository on a computer-readable storage media for storing raw data related to financial transactions;   wherein the stored raw data are unprocessed financial transaction data records resulting from the financial transactions;   wherein the stored raw data are data associated with multiple different types of entities;   a view selector for executing on a computer system and for selecting an entity or type of entity;   wherein the raw data associated with the selected entity of type of entity is used by a fraud scoring system for determining fraud scores for the selected entity or type of entity.   
     
     
         2 . The system of  claim 1 , wherein the view selector is configured for use by a user or computer program to select which entity or type of entity upon which fraud analysis is to be performed. 
     
     
         3 . The system of  claim 1 , wherein the multiple different types of entities include a merchant entity type, card entity type, and geographical entity type. 
     
     
         4 . The system of  claim 1 , wherein the view selector is configured for use by a user or a computer application to shift view from a first type of entity to a different type of entity. 
     
     
         5 . The system of  claim 4 , wherein the first type of entity is a cardholder account;
 wherein the different type of entity is a merchant.   
     
     
         6 . The system of  claim 4 , wherein the shifting of the view from the first type of entity to the different type of entity shifts the fraud analysis from being performed upon the first type of entity to being performed upon the different type of entity. 
     
     
         7 . The system of  claim 6 , wherein the stored raw data that is used to predict whether fraud has occurred with respect to the first type of entity is used to predict whether fraud has occurred with respect to the different type of entity. 
     
     
         8 . The system of  claim 6 , wherein the first type of entity is a cardholder account;
 wherein the different type of entity is a merchant;   wherein the stored raw data that is used to predict whether fraud has occurred with respect to the cardholder account is used to predict whether fraud has occurred with respect to the merchant.   
     
     
         9 . The system of  claim 8 , wherein the stored raw data that is used to predict whether fraud has occurred with respect to the merchant is used to determine a merchant attrition score. 
     
     
         10 . The system of  claim 9 , wherein the merchant attrition score is an indicator of how likely a relationship between a merchant and the merchant's associated institution is to be severed. 
     
     
         11 . The system of  claim 1 , wherein source of the raw data is different financial institutions. 
     
     
         12 . The system of  claim 1 , wherein the view selector facilitates fraud indicative scores to be generated by the fraud scoring system at multiple different levels. 
     
     
         13 . The system of  claim 12 , wherein the fraud indicative scores that are generated at the multiple different levels include generating fraud indicative scores at a card holder scoring level, merchant scoring level, merchant attrition scoring level, or at a bankruptcy prediction scoring level. 
     
     
         14 . The system of  claim 1 , wherein the stored raw data include incremental transaction data, said system further comprising:
 first scoring software instructions, contained within the fraud scoring system on a computer-readable media, for generating a first score based upon data regarding a new incremental transaction with respect to an entity;   whereby, subsequent to the generation of the first score, a trigger is received that was generated independent of whether an incremental transaction has occurred or not;   second scoring software instructions, contained within the fraud scoring system on a computer-readable media, for generating in response to the generated trigger a second score for the entity based upon stored past incremental transaction data and upon non-incremental transaction data;   wherein the generated second score is indicative of whether fraud has occurred or not;   whereby the second score is used to determine whether a fraud handling action is to be performed upon the entity.   
     
     
         15 . A computer-implemented fraud scoring method comprising:
 storing raw data related to financial transactions in a computer-readable raw data repository;   wherein the stored raw data are unprocessed financial transaction data records resulting from the financial transactions;   wherein the stored raw data are data associated with multiple different types of entities;   selecting through a view selector software module an entity or type of entity;   wherein the raw data associated with the selected entity of type of entity is used by a fraud scoring system for determining fraud scores for the selected entity or type of entity;   wherein the selecting is performed using a data processor.