ABSTRACT

With the aging population, the increasing incidence of various diseases, and the change in people’s lifestyles, the health risks encountered by the population have surged, resulting in high growth, high risk, high compensation, and high cost of health insurance. With the development of modern science and technology, such as big data and machine learning, and their application in risk management and control of health insurance, RC Health Insurance Company urgently wants to build an intelligent decision support system for intelligently evaluating the compensation risk of customers. In order to realize the intelligent risk assessment of claims, this study constructs the main factors that affect the risk of claims through the customer business data of insurance companies, especially the data of customer claims. It uses machine learning and data mining technology to analyze and model a large number of customer data by using Random Forest Algorithm. Through these intelligent models, customers’ claims risk can be predicted and evaluated, and corresponding decision support can be provided. The risk assessment model of claims is applied to a decision support system, which can automatically assess risks and provide real-time results and suggestions. In this way, insurance companies can evaluate the claims risk of customers more quickly and accurately and take corresponding measures, thus reducing the claims risk and effectively controlling business performance.

Keywords: Customer risk assessment, decision support system, data mining, random forest algorithm, sprial model
https://doi.org/10.57180/yqqt4879