It can be difficult to design and develop artificial intelligence systems to meet specific quality standards. Often, AI systems are designed to be "as good as possible" rather than meeting particular targets. Using the Design for Six Sigma quality methodology, an automated insurance underwriting expert system was designed, developed, and fielded. Using this methodology resulted in meeting the high quality expectations required for deployment.
Rockville, MD 20850 Colleen McClintock Infinite Intelligence, Inc. 1155 Connecticut Avenue, #500 Washington 20036 Jacqueline Sobieski Fannie Mae 3900 Wisconsin Avenue Washington 20016 Abstract Business policy can be defined as the guidelines and procedures by which an organization conducts its business. Organizations depend on their information systems to implement their business policy. It is important that any implementation of business policy allows faster application development and better quality management and also provides a balance between flexibility and centralized control. This paper views business rules as atomic units of business policy that can be used to define or constrain different aspects of the business. It then argues that business rules provide an excellent representation for business policy. KARMA was developed and deployed at Fannie Mae. 1 Introduction Business policy can be defined as the guidelines and procedures by which an organization conducts its business. Business policy is often documented in manuals and business guidelines and is reflected in an organization's information systems. Organizations depend on their information systems to implement this policy.
Fannie Mae, the nation's largest source of conventional mortgage funds, has made a commitment to use technology to improve the efficiency of processing a loan by reducing the time, paperwork and cost associated with loan origination. The Desktop Underwriter (DU) system which was developed as a result of this commitment, is an automated underwriting expert system that applies both heuristics and statistics to the problem. The system supports both the wholesale and retail mortgage environments and is built to reason and underwrite loans with incomplete, unverified and conflicting data. The system generates a credit recommendation based on the loan's conformity to credit standards and an eligibility recommendation based on the loan's conformity to eligibility
This paper describes the GE Compliance Checker [GECCO], a knowledge-based application for use in the home mortgage industry. GECCO is a tool for automating the information-intensive processes of underwriting and reselling mortgage loans. GECCO was initially designed and deployed for one business component of GE Capital Mortgage Corporation [GECMC], and then successfully integrated into three other GECMC businesses. Its first application was for third-party underwriting. This was followed by the use of GECCO in wholesale pricing and registration, and in direct loan origination. Most recently, GECCO has evolved into a commercial product offered for purchase to mortgage lenders. GECCO has significantly improved the underwriting and resale process: quality control has become much more effective, adding consistency, completeness and robustness to the decision making process; the quantity of loans processed has increased; customer service has been enhanced; and a once "subjective" process has now been standardized. The successful use of AI has also permeated GECMC business application software to the extent that AI has become a requirement rather than a remote technology used in an isolated application.
Fannie Mae is a congressionally chartered, shareholderowned company and the nation's largest source of conventional home mortgage funds. Fannie Mae purchases and securitizes loans and is considered the leader in the secondary mortgage market. Because of its strong leadership role, Fannie Mae's policies for loan eligibility set the standard in the mortgage industry and applying these policies consistently and effectively is critical to Fannie Mae's mission and profitability. Fannie Mae's policies for selling and servicing mortgage loans span the business functions of the secondary mortgage market and therefore are contained in many different software applications. Managing policy across multiple business applications became increasingly complex.