Abstract
EFD (Electronic Fraud Detection) assists Investigative Consultants in the Managed Care & Employee Benefits Security Unit of the Travelers Insurance Companies in the detection and preinvestigative analysis of healthcare provider fraud. the task EFD performs, scanning a large population of health insurance claims in search of likely fraud, has never been done manually. Furthermore, the available database has few positive examples. Thus, neither existing knowledge engineering techniques nor statistical methods are sufficient for designing the identification process. to overcome these problems, EFD uses knowledge discovery techniques on two levels. First, EFD integrates expert knowledge with statistical information assessment to identify cases of unusual provider behavior. the heart of EFD is 27 behavioral heuristics, knowledge-based ways of viewing and measuring provider behavior. Rules operate on them to identify providers whose behavior merits a closer look by the Investigative Consultants. Second, machine learning is used to develop new rules and improve the identification process. Pilot operations involved analysis of nearly 22 000 providers in six metropolitan areas. the pilot is implemented in SAS Institute's SAS® System, AICorp's Knowledge Base Management System (KBMS®), and Borland International's Turbo Prolog®. © 1992 John Wiley & Sons, Inc.
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