Application of Data Mining Techniques in Automobile Insurance Fraud Detection
Kannat Na Bangchang, Sangdao Wongsai, Teerawat Simmachan · 2023
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 policy holders because of the increasing in premium amounts. Therefore, detection of insurance fraud is a challenging problem. Traditional approaches are hard 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. This work would help in a screening process to investigate claims, thus minimizing human resources and monetary losses. Three sets of features are obtained by logistic regression models: one with forward selection, one with backward elimination, and one without variable selection. Three algorithms including Naïve Bayes, random forest and adaptive boosting are employed as classifiers. K-fold cross validation is used to evaluate the algorithm performance. The results suggest that the smaller number of features, the better performance. The random forest performs the best with highest accuracy (85.28%), sensitivity (93.85%), and precision (97.91%) whereas the adaptive boosting provides the highest specificity (70.41%) and F-score (89.86%).