Prediction of Insurance Fraud Detection using Machine Learning Algorithms

Abstract

In current era, people are influenced with various types of insurance such as health insurance, automobile insurance, property insurance and travel insurance, due to the availability of extensive knowledge related to insurance. People are trending to invest in such kinds of insurance, which helps the scam artist to cheat them. Insurance fraud is a prohibited act either by the client or vendor of the insurance contract. Insurance fraud from the client side is encountered in the form of overestimated claims and post-dated policies etc. Although, insurance fraud from the vendor side is experienced in the form of policies from non-existent companies and failuew to submit premiums and so on. In this paper, we perform a comparative analysis on various classification algorithms, namely Support Vector Machine (SVM), Random-Forest (RF), Decision-Tree (DT), Adaboost, K-Nearest Neighbor (KNN), Linear Regression (LR), Naïve Bayes (NB), and Multi-Layer Perceptron (MLP) to detect the insurance fraud. The effectiveness of the algorithms are observed on the basis of performance metrics: Precision, Recall and F1-Score. The comparative results of classification algorithms conclude that DT gives the highest accuracy of 79% as compared to the other techniques. In addition to this, Adaboost shows the accuracy of 78% which is closer to the DT