Застосування методів інтелектуального аналізу даних до розв’язання задач актуарного моделювання та оцінювання фінансових ризиків

• Дата: 2017
• Автори: Dubinina, S. V., Bidyuk, Petro I.
• Формат: Стаття
• Мова: Ukrainian
• Опубліковано: The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2017
• Теми: actuarial processes; insurance risks; generalized linear models; Bayesian approach; data mining; group method for data handling; fuzzy GMDH; актуарные процессы; страховые риски; обобщенные линейные модели; байесовский подход; методы интеллектуального анализа данных; метод группового учета аргументов; нечеткий МГУА; актуарні процеси; страхові ризики; узагальнені лінійні моделі; байесовский підхід; методи інтелектуального аналізу даних; метод групового обліку аргументів; нечіткий МГУА
• Онлайн доступ: http://journal.iasa.kpi.ua/article/view/101738
• Назва журналу: System research and information technologies

Results of application of the data mining to solving the problem of actuarial processes modeling and risk estimation for insurance companies are presented. As a mathematical modeling tool the following approaches were used: generalized linear models, Bayesian networks, the group method for data handling, fuzzy GMDH, and Bayesian parameter estimation techniques. Using actual statistical data from the insurance industry, new generalized linear models were constructed that were used for estimation of a possible loss by an insurance company. Also, a model in the form of a Bayesian network was constructed that was applied to estimate the bankruptcy risk in a case of insurance losses. The best model constructed in this case turned out to be the gamma distribution based model and logarithmic link function whose parameters were estimated within four iterations of the estimation algorithm. A substantial computed value of the insurance company bankruptcy risk reflects the fact that the company under consideration does not possess an effective mechanism for managing its own capital and the payments from clients. Thus, an application of data mining is an effective approach to solving the problems of short-term forecasting financial processes and estimation of actuarial risks.