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 method for imputing missing values for use in a fraud analysis predictive model, said method comprising:
 executing instructions on a computer system for receiving an input data set from a computer-readable data store wherein one or more values are missing with respect to one or more attributes of the input data set;   executing instructions on the computer system for determining a value for an attribute based upon a closed form equation representing the value to be used when encountering a missing value for the attribute;   wherein the closed form equation is based upon fraud-related tag information;   executing instructions on the computer system for using in the fraud analysis predictive model the determined value;   wherein the receiving an input data set, determining a value for an attribute, and using the determined value are performed on a data processor.   
     
     
         2 . The method of  claim 1 , wherein the fraud-related tag considerations include a tag associated with an input record as to whether the input record is considered fraud or non-fraud. 
     
     
         3 . The method of  claim 1 , wherein the fraud analysis predictive model is a neural network predictive model. 
     
     
         4 . The method of  claim 1 , wherein the closed form equation is generated for an attribute based upon historical data associated with the attribute. 
     
     
         5 . The method of  claim 4 , wherein the closed form equation is generated by using an optimality criterion involving the tag information associated with the attributes. 
     
     
         6 . The method of  claim 1 , wherein the input data set includes fraud-related features;
 wherein the closed form equation is independent of the features.   
     
     
         7 . The method of  claim 1 , wherein the missing data values pertain to missing authorization data values, missing posting data values, missing settlement data values, missing statement data values, missing payment data values, or missing non-monetary data values. 
     
     
         8 . The method of  claim 1 , wherein the determined missing value is used in training, testing, or training and testing the fraud analysis predictive model. 
     
     
         9 . The method of  claim 8 , wherein the training of the fraud analysis predictive model includes:
 receiving training data;   training a first predictive model using the received training data;   said training data including data from a plurality of accounts;   using a partitioning criterion to determine how to partition the training data into partitions;   said partitioning criterion being based upon a fraud ranking violation metric;   training multiple predictive models sequentially using at least one of the partitions of training data;   said training of predictive models being performed to optimize a fraud ranking violation;   wherein the first and second predictive models are combined for use in predicting credit card or debit card fraud;   wherein said receiving training data, training a first predictive model, determining how to partition the training data, and training multiple predictive models are performed on a data processor.   
     
     
         10 . The method of  claim 9 , wherein the first and second predictive models are neural network predictive models. 
     
     
         11 . The method of  claim 10 , wherein the determined missing value is used as part of the input layer to the combined first and second predictive models. 
     
     
         12 . The method of  claim 1 , wherein the determined missing value is used in a lookup table by the fraud analysis predictive model to predict in a production environment whether an account of an entity has been compromised. 
     
     
         13 . A computer-implemented system for imputing missing values for use in a fraud analysis predictive model, comprising:
 a computer-readable data store to store an input data set wherein one or more values are missing with respect to one or more attributes of the input data set;   value determination software instructions on a computer-readable medium to determine a value for an attribute based upon a closed form equation representing the value to be used when encountering a missing value for the attribute;   wherein the closed form equation is based upon fraud-related tag information;   wherein the determined value is for use in the fraud analysis predictive model.