Using Kohonen's Self-Organizing Feature Map to Uncover Automobile Bodily Injury Claims Fraud

By Brockett, Patrick L.; Xia, Xiaohua et al. | Journal of Risk and Insurance, June 1998 | Go to article overview

Using Kohonen's Self-Organizing Feature Map to Uncover Automobile Bodily Injury Claims Fraud


Brockett, Patrick L., Xia, Xiaohua, Derrig, Richard A., Journal of Risk and Insurance


INTRODUCTION AND BACKGROUND

One vexing problem confronting the property-casualty insurance industry is claims fraud. Individuals and conspiratorial rings of claimants and providers unfortunately can and do manipulate the claim processing system for their own undeserved benefit (Derrig and Ostaszewski, 1994; Cummins and Tennyson, 1992). The National Insurance Crime Bureau (NICB) estimates that the annual cost of the insurance fraud problem is $20 billion, which is equivalent to the cost of a Hurricane Andrew each year (NICB, 1994). According to the National Health Care Association, insurance fraud in health insurance represented an estimated 10 percent surcharge on the U.S. $550 billion annual health care bill in 1988 (Garcia, 1989). A recent Insurance Research Council report on automobile insurance fraud stated that "the excess injury payments as a result of fraud and/or buildup are estimated to be between 17 and 20 percent of total paid losses, or $5.2 to $6.3 billion additional for all injury claims in 1995." (IRC, 1996) Outside of the United States, fraud claims are also increasing. For example, arson was thought to be costing the United Kingdom [pounds]500 million a year in 1991 (Wilmot, 1991).

Private passenger automobile bodily injury (BI) insurance is the largest line of property-casualty insurance in the U.S. It is estimated that about 40-50 percent of BI claims, for Massachusetts at least, contain some degree of suspicion concerning fraud (Derrig, Weisberg and Chen, 1994). The proportion of fraudulent claims also appears to be increasing as evidenced by ever higher rates of bodily injury claims per accident. The Insurance Research Council documents a countrywide change from 22 BI claims per property damage liability claim (the proxy for claims per accident) in 1987 to 29 BI claims per accident in 1992 (IRC, 1994). While the entire increase may not be due to an increase in fraudulent BI claims, the increase is indicative of fraudulent or abusive insurance lotteries (Cummins and Tennyson, 1992).

With the awareness of the increasing frequency of suspected claims fraud, more and more rigorous techniques and empirical databases are being created for the purpose of fraud detection. One such database is the National Insurance Crime Bureau (NICB) Database System, which contains 200 million records of claims and stolen vehicles and which was recently made available to member insurance companies (Dauer, 1993). Once a company enters a claim in the database, either the NICB or that company's special investigation unit (SIU) will commence an investigation if some suspicious information arises for that particular claim.(1) In Massachusetts, a detail claim database (DCD) of all auto BI claims has been assembled commencing January 1, 1994. This database, available to company special investigative units (SIUs) and the Insurance Fraud Bureau, is expected to provide detailed information on approximately two hundred thousand claims annually.

In addition to databases, people began to use other approaches to analyze the automobile bodily injury (BI) claims fraud problem. Using statistical methods, Weisberg and Derrig (1991) determined the mechanisms behind automobile BI claims fraud, such as relationships between injury type and treatment, for example. Their studies of 1985/1986 and 1989 BI claims found that the overall level of suspected or apparent fraud was about 10 percent of the claims, while the apparent build-up(2) level was 35 percent in 1985/86 and 48 percent in 1989.

Nearly all companies rely on the training of personnel as the primary method of recognizing claims suspected of fraud. Specified objective and subjective claim characteristics, such as "no witnesses to the accident," have become known as potential fraud indicators or red flags. Three-quarters of the companies rely on the presence of these red flags to assist the claim adjuster in recognizing suspicious claims, and one-quarter of those companies use automated methods of tracking red flags (IRC, 1992). …

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