Identification of influential features and fraud detection in the insurance industry using the data mining techniques (Case study: Automobile's body insurance)

By increase of fraud in the insurance company and imposition of immense financial costs on these companies, identification of, and prevention from, fraud has been turned into one of significant distresses of the insurance companies. Data mining techniques assist in discernment of, and prevention from, fraud and abuse in the insurance. In this paper, using two selection methods of feature based on genetic algorithm and correlation, influential variables were found and then implemented on the data set of naive bayesian and decision tree algorithms. Results showed that modeling by use of selection method of feature based on genetic algorithms has a higher accuracy compared to selection method of feature based on correlation.