Risk management of an insurance company based on actuarial models

Abstract

The purpose of the article is to study modern approaches to risk management of an insurance company based on actuarial models, as well as to determine the effectiveness of the application of mathematical and statistical methods to ensure the financial stability of the insurer. The work pays special attention to the relationship between the quality of risk management, the structure of the insurance portfolio, and the accuracy of actuarial calculations, which enable predicting unprofitability, optimising the tariff policy, and increasing the company's solvency level. The study uses a set of theoretical methods, in particular, the analysis and generalisation of the scientific literature and elements of economic modelling, to substantiate the effectiveness of an insurance company's risk management system. The article summarises theoretical approaches to the application of collective risk models and methods for modelling insurance payments in the risk management of an insurance company. Their role in the formation of technical reserves, in assessing capital adequacy, and in maintaining the financial stability of the insurer is revealed. Results. It is shown that integrating actuarial models into the risk management system provides an analytical basis for improving loss forecasting accuracy, optimising capital allocation, and supporting strategic decision-making. The results obtained are of practical importance for insurers, allowing them to systematise risk assessment approaches, increase the effectiveness of tariff policy, and maintain the stability of their activities amid market instability. The conclusions substantiate that the construction of an effective risk management system involves the comprehensive integration of actuarial models at all stages of management processes - from underwriting and pricing to reserve control and long-term financial planning. Prospects for further research include improving methods for assessing catastrophic risks, adapting models to the increased volatility of the insurance market, and using modern digital analytics tools to improve forecast accuracy.