Fraud detection in health insurance using ensemble learning methods

Abstract

Insurance fraud is one of the most expensive economic financial crimes. Most risk management solutions use rules to detect potential abuse, but as the patterns of abuse change, those solutions become ineffective. In this paper we apply machine learning (Decision Trees, Bagging, Random Forests and Boosting) for fraud detection in health insurance. Performance of the model is evaluated using accuracy, error rate, sensitivity and specificity. The best results were achieved using Bagging technique. In further research it would be useful to analyze applicability of deep learning models and anomaly detection methods.