AI-Driven Risk Control for Health Insurance Fund Management: A Data-Driven Approach

Abstract

This study presents an AI-driven risk control framework aimed at effectively managing the risks associated with health insurance fund operations. The backdrop reveals that the increase in fraudulent activities has contributed to a significant slowdown in the growth rate of health insurance fund income compared to expenditures in China, leading to a decline in surplus rates. To address this challenge, the proposed framework integrates unsupervised and supervised learning methodologies for risk identification and quantification. Specifically, we employ Gaussian Mixture Models (GMM) for clustering medical behaviors to detect anomalies, followed by the application of the LightGBM model for risk classification and quantification. Experimental results demonstrate the framework’s robust capabilities in identifying potential fraud, underscoring that frequent medical visits and significant expenditures on non-essential medications are key indicators of fraudulent behavior. In conclusion, the proposed framework not only enhances the transparency and efficiency of health insurance fund management but also provides a solid foundation for implementing effective risk control measures.