Assessment model for the sustainability of an insurance organization’s development

The issues of sustainability of the insurance organization are given priority attention by participants in insurance economic relations - insurers, policyholders, the insurance market regulator, investors. The article presents the results of the component analysis of the sustainability of the insurance organization as the main characteristic of the company’s ability to adapt to external threats, internal challenges associated with the imperfection of business processes taking place in the company. The authors present a model for assessing the sustainability of the insurer’s development, developed using the hierarchy analysis method (HAM). The presented model, according to the authors, is a tool that allows you to systematize and summarize data on the functioning of the insurance company, forming a comprehensive assessment of their sustainability. The results obtained in modeling the sustainability of the insurance organization’s development allow the company to effectively respond to external and internal threats. Ensuring long-term stability and economic growth. The proposed system of indicators for assessing the level of sustainability of the insurance organization’s development involves the definition of five groups of sustainability indicators. Along with the assessment of financial stability as the main component describing the ability of the insurance company to fulfill its obligations, the system also includes digital sustainability, market sustainability, environmental sustainability and external sustainability. To test the significance of individual components and confirm or refute the hypotheses, a binary choice logit model was constructed to analyze the factors influencing the probability of revocation of an insurance company’s license. The results showed that financial and market stability have a significant impact on the sustainability of an insurance company’s development. The impact of external stability was not significant, which may be due to the complex nature of external factors or their long-term impact. Testing the model on real data also showed the presence of an explanatory effect. In future studies, the model may be modified using additional indicators to increase explanatory power.