DIGITAL TRANSFORMATION AND THE USE OF ARTIFICIAL INTELLIGENCE IN SOCIAL INSURANCE AND INSURANCE
Abstract
The goal of writing this article is to explore the impact of artificial intelligence on enhancing efficiency and ensuring the competitiveness of insurance companies, as well as to uncover the opportunities and advantages, downsides, and challenges of using digital technologies in social insurance and insurance. Digitalization in this field occurs under the influence of globalization, technological innovations, and changes in demographic trends. The study pays special attention to the impact of digital technologies on insurance processes, including the use of big data and artificial intelligence for personalizing insurance products and optimizing risk assessment and claim processing procedures. The research also focuses on analyzing the effects of these changes on social equity and the accessibility of insurance services for different population strata. This article provides a comprehensive view of the trends and prospects for the development of social insurance and insurance, highlighting both theoretical and practical aspects of reforming these systems in the context of modern socio-economic challenges. The research shows that companies that reject innovation will face higher costs in the future compared to those actively implementing new developments. These companies will also face challenges in attracting high-risk clients, whereas innovations help attract more reliable clients who benefit from personalized products. This article demonstrates that through digitalization of insurance, clients become more satisfied and interested in insurance services, which leads to increased insurance premium revenues. Moreover, digital technologies help reduce costs by optimizing decision-making and reducing risks, thereby enhancing the efficiency of insurance operations and strengthening the competitive advantages of insurers. The advantages of digitization and the use of AI include improved accuracy of risk assessment, process automation, product personalization, fraud detection, and enhanced investment decision-making. The downsides may include data confidentiality issues, the exclusion of the human factor, the risk of algorithm bias, implementation complexity and high costs, and the threat of unemployment.
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