Health Insurance Fraud Detection

Abstract

Health insurance fraud detection is an important and challenging task. Traditional heuristic-rule based fraud detection techniques can not identify complex fraud schemes. Such a situation demands more sophisticated analytical methods and techniques that are capable of detecting fraud activities from large databases. Traditionally, insurance companies use human inspections and heuristic rules to detect fraud. As the number of electronic insurance claims increases each year, it is difficult to detect insurance fraud in a timely manner by manual methods alone. In addition, new types of fraud emerge constantly and SQL operations based on heuristic rules cannot identify those new emerging fraud schemes. Such a situation demands more sophisticated analytical methods and techniques that are capable of detecting fraud activities from large databases. This chapter describes the application of three predictive models: MCLP, decision tree, and Naive Bayes classifier, to identify suspicious claims to assist manual inspections. The predictive models can label high-risk claims and help investigators to focus on suspicious records and accelerate the claim-handling process.