# Detecting Fraudulent Claims in Automobile Insurance Policies by Data Mining Techniques

## Keywords:

Na¨ıve Bayes, random forest, adaptive boosting, logistic regression, variable selection## Abstract

The insurance industry is a fast-growing industry and handles substantial amounts of data. Fraudulent claims are the main problem in the industry. Auto insurance fraud is one of the most prominent types of insurance fraud. Numerous fraudulent claims affect not only the insurance company but also the sincere policyholders because of the increase in premium amounts. Typically, a fraud report is

unbalanced data. Overlooking this generally leads to weak classifiers for predicting the minority class (fraudulent claim). Therefore, the fraud detection is a challenging problem. Traditional approaches are difficult to handle and inefficient. Data mining has recently offered significant contributions to insurance analysis. To overcome this, data mining techniques are used to predict fraudulent claims. The aims of this research are to develop, firstly, what types of features should be used to build the predictive model; and second, a statistical learning strategy to classify whether a fraud report is fraudulent or not. To discover important sets of features, logistic regression (parametric method) and random forest (non-parametric method) are considered as tools of variable selection algorithms. This process is done by cross-validation to reduce uncertainty until two sets of important features are obtained. Four algorithms including logistic regression, random forest, Na¨ıve Bayes, and adaptive boosting are employed as classifiers. A confusion matrix is used to evaluate the algorithm’s performance. The results suggest that a set of important features obtained from the non-parametric method provides better performance than the parametric method. The random forest is considered as the best algorithms to identify fraudulent claims with the highest sensitivity (99.19%) and the positive predictive value (93.62%). This work would help in a screening process to investigate claims, thus minimizing human resources and monetary losses in the insurance industry.

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