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EFD: A Hybrid Knowledge/Statistical‐Based System for the Detection of Fraud
Author(s) -
Major John A.,
Riedinger Dan R.
Publication year - 2002
Publication title -
journal of risk and insurance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.055
H-Index - 63
eISSN - 1539-6975
pISSN - 0022-4367
DOI - 10.1111/1539-6975.00025
Subject(s) - identification (biology) , heuristics , computer science , process (computing) , prolog , knowledge base , expert system , population , task (project management) , automatic summarization , data science , artificial intelligence , engineering , medicine , botany , environmental health , systems engineering , biology , operating system
Electronic Fraud Detection (EFD) assists Investigative Consultants in the Managed Care & Employee Benefits Security Unit of The Travelers Insurance Companies in the detection and preinvestigative analysis of health care 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, and Borland International's Turbo Prolog.