Actuarial pricing is a critical area underlying risk assessment and premium calculation processes in the insurance and finance sectors. It involves the application of mathematical and statistical models to determine the cost of insurance policies and predict potential future losses. Insurance companies place significant importance on actuarial pricing to provide their customers with affordable and sustainable policies. In this context, actuarial pricing studies are crucial for risk management and financial sustainability. Therefore, understanding and evaluating this vast and dynamic literature on actuarial pricing is essential for professionals and researchers in the sector.
This study aims to examine the actuarial pricing literature using bibliometric analysis. Bibliometric analysis is a method that reveals the structure of the literature, development trends, and key research areas through the numerical and statistical analysis of publications in a specific field. This analysis is a powerful tool for identifying the historical development of actuarial pricing studies, the most influential researchers and their work, commonly used methodologies, and future research trends.
This study demonstrates that the actuarial pricing literature has developed rapidly over the years, with changes in the global risk environment, particularly in the last two decades, stimulating research in this area. Analyses by author, country, publication type, and subject matter reveal the interdisciplinary nature of the field, the decisive role of central researchers and journals, and the importance of international collaborations in the development of the literature. The findings demonstrate that actuarial pricing is not only critical for the insurance sector but also vital for ensuring financial stability, developing risk management strategies, and designing sustainable policies. Therefore, deepening actuarial pricing studies and strengthening international collaborations through the integration of modern analytical methods, artificial intelligence, and machine learning in both academia and industry stands out as a strategic imperative for future success.
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