Automating the Underwriting of Insurance Applications

AI Magazine

An end-to-end system was created at Genworth Financial to automate the underwriting of long-term care (LTC) and life insurance applications. Relying heavily on artificial intelligence techniques, the system has been in production since December 2002 and in 2004 completely automates the underwriting of 19 percent of the LTC applications. Finally, a natural language parser is used to improve the coverage of the underwriting system. GNW is committed to providing financial protection to its customers, their families, and their businesses. This is accomplished through a diverse set of products, including long-term care, term life, dental, disability, and mortgage insurance.


Automating the Underwriting of Insurance Applications

AI Magazine

An end-to-end system was created at Genworth Financial to automate the underwriting of long-term care (LTC) and life insurance applications. Relying heavily on artificial intelligence techniques, the system has been in production since December 2002 and in 2004 completely automates the underwriting of 19 percent of the LTC applications. A fuzzy logic rules engine encodes the underwriter guidelines and an evolutionary algorithm optimizes the engine's performance. Finally, a natural language parser is used to improve the coverage of the underwriting system.


Automating the Underwriting of Insurance Applications

AAAI Conferences

An end-to-end system was created at Genworth Financial to automate the underwriting of Long Term Care (LTC) and Life Insurance applications. Relying heavily on Artificial Intelligence techniques, the system has been in production since December 2002 and today completely automates the underwriting of 19.2% of the LTC applications.


IAAI95-005.pdf

AAAI Conferences

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.


IAAI97-199.pdf

AAAI Conferences

MetLife processes over 300,000 life insurance applications a year. Underwriting of these applications is labor intensive. Automation is difficult since they include many freeform text fields. MITA, MetLife's Intelligent Text Analyzer, uses the Information Extraction --IE-- technique of Natural Language Processing to structure the extensive text fields on a life insurance application. Knowledge engineering, with the help of underwriters as domain experts, was performed to elicit significant concepts for both medical and occupational text fields. A corpus of 20,000 life insurance applications provided the syntactical and semantic patterns in which these underwriting concepts occur. The extracted information can then be analyzed by conventional knowledge based systems. We project that MlTA and knowledge based analyzers will increase underwriting productivity by 20 to 30%.