Research demonstrates a risk-return assessment model for insurance, applying entropy and TOPSIS methods to optimize underwriting in high-risk environments.
With the increasing frequency of extreme weather events, the insurance industry faces a major challenge of how to balance risk and return. In this study, a risk-return assessment model is constructed with the aim of providing scientific decision support for insurance companies. The model quantifies risk severity and risk probability through risk matrix, calculates risk index by linear interpolation, and generates comprehensive risk index by combining Borda method and hierarchical analysis method. For the benefit assessment, the direct benefit index was calculated by regional emergency management budget normalization and tanh function mapping, and the indirect benefit index was generated by using expert scoring and sigmoid function mapping, which led to the comprehensive benefit index. Finally, the entropy method-TOPSIS method is used to generate the underwriting priority ranking by calculating the geometric distances of regions from positive and negative ideal solutions. Taking eight U.S. states and the Wenchuan earthquake as examples, the model demonstrates its applicability in macro-regional assessment and specific disaster scenarios. Sensitivity analysis verifies the robustness of the model and shows its strong adaptability to input changes. This study provides theoretical basis and practical guidance for the insurance industry's decision-making in high-risk environments.
No takes yet. Share an insight, caveat, or question.
Tianxing Liang (2025) studied this question.