Use of Data Mining Techniques for Data Balancing and Fraud Detection in Automobile Insurance Claims

A novel hybrid data balancing method based on both undersampling and oversampling with ensemble technique has been presented in this paper for efficiently detecting the auto insurance frauds. Initially, the skewness from the original imbalance dataset is removed by excluding outliers from the majority class samples using Box and Whisker plot and synthetic samples are generated from the minority class samples by using synthetic minority oversampling (SMOTE) technique. We employed three supervised classifiers, namely, support vector machine, multilayer perceptron, and K-nearest neighbors for classification purpose. The final classification results are obtained by aggregating the results obtained from these classifiers using the majority voting ensemble technique. Our model has been experimentally evaluated with a real-world automobile insurance dataset.