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.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims priority to U.S. Provisional Application, No. 61 / 545,496, entitled “METHODS AND SYSTEMS FOR AUTOMATICALLY GENERATING HIGH QUALITY ADVERSE ACTION NOTIFICATIONS” filed Jan. 31, 2013, which application is hereby incorporated in its entirety by reference. This application also relates to U.S. application Ser. No. 13 / 454,970, entitled “SYSTEM AND METHOD FOR PROVIDING CREDIT TO UNDERSERVED BORROWERS” filed Apr. 24, 2012, which application is also hereby incorporated in its entirety by reference.TECHNICAL FIELD[0002]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.BACKGROUND AND SUMMARY[0003]People use credit daily for purchases large and small. Lenders, such as banks and credit card companies, use “credit scores” to evaluate the potential risk posed by lending money. These measures purport to determine the...

AI Technical Summary

Benefits of technology

The present invention can be used independently or in conjunction with a system and method for providing credit to borrowers. The technical effect of this invention is that it provides a way to generate adverse action letters that can be used to protect against fraud or other negative events in a credit system.

Problems solved by technology

As a result of decreased physical interaction with lenders and increased computational complexity of calculating “creditworthiness,” consumers' visibility into what “drives” their credit scores diminished.

But lenders engage in only minimal compliance, often providing generic reasons for their decisions.

Unfortunately, these adverse action letters do very little to actually help consumers verify their credit history and / or determine which actions could increase their creditworthiness (at least, insofar as these mathematical models are concerned).

This outcome is not beneficial from a lending policy standpoint as customers aren't getting what they need.

However, generating adverse actions letters that provide useful consumer feedback is not a straightforward task.

Describing market factors as a reason for denials, in and of itself, is a complex job.

But when compounded with the complexity of the newer mathematical credit scoring models, the job of effectively communicating the reasons is far more challenging.

Indeed, pinpointing one or more variables—jointly or severally—that correlate to increased credit scores involves a complex analysis that few lenders, if any, would bother to perform, much less communicate to their consumers.

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