GENIUS(TM) Automated Underwriting System: Combining Knowledge Engineering and Machine Learning to Achieve Balanced Risk Assessment

David C. Golibersuch, GE Corporate Research and Development; Rebecca Towne and Cheryl A. Wiebe, GE Capital Mortgage Corporation

The GENIUS Automated Underwriting System is an expert advisor that has been in successful nationwide production by GE Mortgage Insurance Corporation for two years to underwrite mortgage insurance. The knowledge base was developed using a unique hybrid approach combining the best of traditional knowledge engineering and a novel machine learning method called Example Based Evidential Reasoning (EBER). As one indicator of the effkacy of this approach, a complex system was completed in 11 months that achieved a 98% agreement rate with practicing underwriters for approve recommendations in the fist month of operation. This performance and numerous additional business benefits have now been confirmed by two full years of nationwide production during which time some 800,000 applications have been underwritten. As a result of this outstanding success, the GENIUS system is serving as the basis for a major re-engineering of the underwriting process within the business. Also, a new version has recently been announced as an external product to bring the benefits of this technology to the mortgage industry at large. In addition, the concepts and methodology are being applied to other financial services applications such as commercial credit analysis and municipal bond credit enhancement. This paper documents the development process and operational results and concludes with a summary of critical success factors.


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