Methods and systems for automatically generating high quality adverse action notifications

Abstract

This invention relates generally to the personal finance and banking field, and more particularly to the field of lending and credit notification methods and systems. Preferred embodiments of the present invention provide systems and methods for automatically generating high quality adverse action notifications based on identifying variations between a declined borrower's profile and that of approved applicants, both with simple and sophisticated credit scoring systems, using specific algorithms.

What is claimed is: 
     
         1 . A central computer server communicatively coupled to a public network, the central computer server having a non-transitory computer-usable medium with a sequence of instructions which, when executed by a processor, causes said processor to execute an electronic process that automatically generates high quality adverse action notifications, said process comprising:
 collecting an electronic dataset for a borrower which contains a credit score and a plurality of variables and meta-variables that describe specific aspects of the borrower to generate a borrower profile;   independently and collectively processing the plurality of variables and meta-variables in the borrower dataset against a lender's criteria for creditworthiness;   identifying sets of variables and meta-variables in the borrower profile that, when changed, result in an improved measure of creditworthiness, wherein the identifying step includes analyzing at least one shortest path between the borrower dataset and the dataset of at least one of: (i) a perfect applicant and (ii) an average approved applicant; and   generating a report that interprets the at least one shortest path, and variables and meta-variables therein, into plain language through which the borrower may understand how to improve the borrower's credit score.   
     
     
         2 . The central computer server of  claim 1 , wherein the lender's criteria for creditworthiness is measured by a credit score. 
     
     
         3 . The central computer server of  claim 1 , wherein the process further includes ranking the identified sets of variables and meta-variables in the borrower profile that, when changed, result in an improved credit score, said ranking using at least one of the following steps:
 voting strategy; and   calculating a weighted contribution of relevant fields in the at least one shortest path that meets or exceeds the lender criteria.   
     
     
         4 . The central computer server of  claim 3 , wherein the process further comprises calculating the weighted contribution of each relevant field in each of the at least one shortest path to a final score difference, wherein the calculating step includes at least one of:
 randomly sampling;   ranking by scoring; and   genetic algorithm.   
     
     
         5 . The central computer server of  claim 4 , wherein the step of generating a report that interprets the at least one shortest path, and variables and meta-variables therein, into plain language through which the borrower may understand how to improve the borrower's credit score, includes at least one of the following steps:
 recording weighted contributions;   translating the values of one or more variables and meta-variables that comprise the weighted contributions into qualitative text strings;   generating a labeled set of training exemplars that connect a weight pattern for a given application to report classes of variables and meta-variables with which the application is associated; and   generating reports that are issued to the borrower from the labeled set of training exemplars.