Using local outlier factor to detect fraudulent claims in auto insurance

Abstract

Given the significant increase in fraudulent claims and the resulting financial losses‎, ‎it is important to adopt a scientific approach to detect and prevent such cases‎. ‎In fact‎, ‎not equipping companies with an intelligent system to detect suspicious cases has led to the payment of such losses‎, ‎which may in the short term lead to customer happiness but eventually will have negative financial consequences for both insurers and insured‎. ‎Since data labeled fraud is really limited‎, ‎this paper‎, ‎provides insurance companies with an algorithm for identifying suspicious cases‎. ‎This is obtained with the help of an unsupervised algorithm to detect anomalies in the data set‎. ‎The use of this algorithm enables insurance companies to detect fraudulent patterns that are difficult to detect even for experienced experts‎. ‎According to the outcomes‎, ‎the frequency of financial losses‎, ‎the time of and the type of incident are the most important factors to in detecting suspicious cases‎.

Keywords

• unsupervised algorithm‎ fraud detection‎
• ‎ auto insurance‎
• classification