American Journal of Mobile Systems, Applications and Services
Articles Information
American Journal of Mobile Systems, Applications and Services, Vol.1, No.3, Dec. 2015, Pub. Date: Jan. 21, 2016
Analytics for Insurance Fraud Detection: An Empirical Study
Pages: 227-232 Views: 1008 Downloads: 1155
[01] Carol Anne Hargreaves, Business Analytics, Institute of Systems Science, National University of Singapore, Singapore, Singapore.
[02] Vidyut Singhania, Business Analytics, Institute of Systems Science, National University of Singapore, Singapore, Singapore.
Automobile insurance fraud is a global problem. Handling fraud manually has always been costly for insurance companies. Data analytics can play a crucial role in fraud detection and can aid insurance companies to identify fraud. Typically, there are easily more than thirty variables that are used for the fraud analysis. This paper proposes to determine which variables are significant for fraud detection and to provide a framework for the insurance fraud detection. Further, this paper illustrates the business value of data analytics for insurance fraud detection using an empirical study and demonstrates that through a few business rules, the insurance company can accurately identify fraudulent claims which can most likely reduce costs and increase profitability for the company.
Fraud Detection, Analytics, Insurance, Significant Variables, Business Rules, Framework
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