Social Network Analytics for Supervised Fraud Detection in Insurance

How do moral hazard behaviors lead to the waste of medical insurance funds? An empirical study from China

Objective

The huge loss of health insurance funds has been a topic of concern around the world. This study aims to explore the network of moral hazard activities and the attribution mechanisms that lead to the loss of medical insurance funds.

Methods

Data were derived from 314 typical cases of medical insurance moral hazards reported on Chinese government official websites. Social network analysis (SNA) was utilized to visualize the network structure of the moral hazard activities, and crisp-set qualitative comparative analysis (cs/QCA) was conducted to identify conditional configurations leading to funding loss in cases.

Results

In the moral hazard activity network of medical insurance funds, more than 50% of immoral behaviors mainly occur in medical service institutions. Designated private hospitals (degree centrality = 33, closeness centrality = 0.851) and primary medical institutions (degree centrality = 30, closeness centrality = 0.857) are the main offenders that lead to the core problem of medical insurance fraud (degree centrality = 50, eigenvector centrality = 1). Designated public hospitals (degree centrality = 27, closeness centrality = 0.865) are main contributor to another important problem that illegal medical charges (degree centrality = 26, closeness centrality = 0.593). Non-medical insurance items swap medical insurance items (degree centrality = 28), forged medical records (degree centrality = 25), false hospitalization (degree centrality = 24), and overtreatment (degree centrality = 23) are important immoral nodes. According to the results of cs/QCA, low-economic pressure, low informatization, insufficient policy intervention, and organization such as public medical institutions, were the high-risk conditional configuration of opportunism; and high-economic pressure, insufficient policy intervention, and organizations, such as public medical institutions and high violation rates, were the high-risk conditional configuration of risky adventurism (solution coverage = 31.03%, solution consistency = 90%).

Conclusion

There are various types of moral hazard activities in medical insurance, which constitute a complex network of behaviors. Most moral hazard activities happen in medical institutions. Opportunism lack of regulatory technology and risky adventurism with economic pressure are two types causing high loss of funds, and the cases of high loss mainly occur before the government implemented intervention. The government should strengthen the regulatory intervention and improve the level of informatization for monitoring the moral hazard of medical insurance funds, especially in areas with low economic development and high incident rates, and focus on monitoring the behaviors of major medical services providers.