AUTO-INSURANCE FRAUD DETECTION: A BEHAVIORAL FEATURE ENGINEERING APPROACH
Journal of Critical Reviews · 2020
Every year, billions of dollars are lost in the auto insurance industry due to fraud, which forces insurance premium prices to go up annually. Although fraud detection solutions have been developed to fix the fraud detection problem, they all still face the same well-known problems of imbalanced data. There is need for a centralized claims database to gather a holistic view of fraudulent characteristic behavior. This paper proposes a data pre-processing technique, particularly a fraud behavior feature engineering approach, to improve the overall performance of prediction models. The behavior being assessed is be based on the RFM model along with an additional behavior analysis related to policy expiration. Furthermore, an ensemble feature selection and modeling is used to deal with the high dimensionality problems that the feature engineering approach brings along with it, as well as the class imbalance problems. The proposed approach shows a 56.2% increase in the F1-meaure, compared against the previous published stat-of-the-art results.