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Tuesday, August 3 • 2:30pm - 4:00pm
5D2 Physician Fraud Detection: The Machine Learning Approach

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Gene Lai, University of North Carolina at Charlotte; Licheng Jin, Southwestern University of Finance and Economics; Chia-Ling Ho, Tamkang University

Using the supervised machine learning method, we try to detect fraudulent physicians. We apply the supervised neural network to predict the likelihood of a physician to be fraudulent. We choose the Oversampling, the Synthetic Minority Oversampling Technique (SMOTE), and the hybrid methods to deal with the imbalanced data issue. Our classifier provides AUROC (Area Under the Receiver Operating Characteristic Curve) scores approximately 0.80 for these three methods, indicating that the methods can largely separate the fraudulent physicians from the legitimate ones. Using the threshold 0.5, our methods generate the GMean 0.7015, 0.7071, and 0.7304 for the Oversampling, SMOTE, and Hybrid methods, respectively. The Recall equals 0.6829, 0.6585, and 0.6098 for these three methods accordingly. Compared with the traditional logistic regression, our method is more appropriate for physician fraud detection since the logistic regression underfits the data. This paper has important implications for insurers: speeding up the claim review process, narrowing the fraud investigation range, excluding suspicious fraudulent physicians as external reviewers, and accurately identifying fraudulent physicians. Moreover, our results also have policy implications for policyholders, hospitals, and the health care industry because fraudulent claims cost all the parties involved.


Gene Lai

University of North Carolina-Charlotte

Tuesday August 3, 2021 2:30pm - 4:00pm EDT