Transformation of the Insurance Industry Based on Data: How Predictive Analytics Rethinks Risk Assessment

Анотація

The purpose of the article is to examine the impact of predictive analytics on the transformation of the insurance risk assessment system. The study focuses on quantitative changes in loss ratios, scoring accuracy, underwriting speed, and the effectiveness of pricing policy following the implementation of algorithmic models. The Ukrainian insurance market was selected as the primary research base. The analytical framework was formed through a comparison of insurance portfolio indicators before and after the integration of machine learning technologies. The study incorporates telematics monitoring, behavioral segmentation, and automated fraud detection tools. A structural and functional approach was applied to assess changes in underwriting, pricing, and reserve management. Aggregated financial indicators of insurance companies that implemented algorithmic instruments were analyzed. It was substantiated that predictive models reduce the combined ratio by 3–7%. The frequency of large losses decreases by up to 15% in high-risk segments of the insurance market. Risk classification accuracy exceeds 80%. Insurers develop personalized tariffs and reduce cross-subsidization among clients. Automated scoring shortens decision-making time from several days to minutes. Algorithmic fraud detection more than doubles the identification rate of improper claims. Financial advantages strengthen the requirements for model transparency, data protection, and cyber risk control. Analytical criteria for the application of predictive tools in the development of Ukrainian insurers were defined. The findings are used to design digital transformation strategies for insurance companies. They support tariff optimization, enhance compliance systems, and establish multi-level protection of information infrastructure. The article systematizes the quantitative effects of predictive analytics implementation in insurance and identifies managerial guidelines to ensure a balance between algorithmic accuracy, financial performance, and regulatory responsibility.

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