Detection of Health Insurance Fraud with Discrete Choice Model: Evidence from Medical Expense Insurance in China

Abstract

Health insurance fraud increases the inefficiency and inequality in our society. To address the widespread problem, cost effect techniques are in need to detect fraudulent claims. With a dataset from medical expense insurance in China, we propose a discrete choice model to identify predicting factors of fraudulent claims, and we address the major limitations of discrete choice model by considering over sampling of fraudulent cases, as well as mislabeling of legitimate claims (omission error). Our results show that a few factors, such as hospital’s qualification and policyholder’s renewal status, could be used to predict fraudulent claims for further investigation.