IDENTIFYING HEALTH INSURANCE CLAIM FRAUDS USING MACHINE LEARNING CONCEPTS

Abstract

Patients depend on health insurance provided by the government systems, private systems, or both to utilize
the high-priced healthcare expenses. This dependency on health insurance draws some healthcare service
providers to commit insurance frauds. Healthcare is one of the largest financial sectors. It has the massive
amount of data containing health records, clinical data, insurance claim, provider and patient information.
Although the number of such service providers is small, it is reported that the insurance providers lose
billions of dollars every year due to frauds. In this paper, we formulate the fraud detection problem over a
minimal, definitive claim data consisting of medical diagnosis and procedure codes. In this paper, we
perform a comparative analysis on various classification algorithms, namely Support Vector Machine
(SVM), Decision-Tree (DT), K- Nearest Neighbor (KNN), and Logistic Regression (LR), to detect the health
insurance fraud. The effectiveness of the algorithms is observed on the basis of performance metrics:
Precision, Recall and F1-Score. Our experimental results demonstrate promising outcomes in identifying
fraudulent records.