Identification of Fraud in Accidental and Healthcare Insurance Using Local Outlier Factor: A Machine Learning Approach

Abstract

An unsupervised machine learning model that uses the mechanism of the Local Outlier Factor to flag and detect ambiguous as well as potentially fraudulent claims in Accidental and Healthcare insurance is proposed in this paper. It entirely automates the manual investigation of claims by claim appraisers in any organization. The ethos of this model is to comprehensively automate and expedite the claim investigation process using certain parameters to aid the claim appraiser’s workload of going through straightforward claims and saving their time to investigate more critical and complex claims. The model flags anomalous claims by comparing them to the model’s threshold, and input parameters and alerts are generated. These alerts generated are then investigated for fraud based on the parameters stated. The model can classify these claims and the cost of billable associated with these claims by reporting an accuracy of 99.5% for the Local Outlier Factor model in comparison with other implemented techniques of Isolation Forest which had an accuracy of only 78.37%. Our model has been tested and validated on real-world data and is showing promising results. Being able to identify and flag potentially fraudulent claims before they are paid out can save insurance companies a lot of money and resources.