Оцінювання кредитних ризиків методами інтелектуального анализу даних

• Дата: 2017
• Автори: Danylov, Valery Ya., Jirov, Alex L., Bidyuk, Petro I.
• Формат: Стаття
• Мова: Ukrainian
• Опубліковано: The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2017
• Теми: credit risk; statistical data; logit model; Bayesian networks; model quality parameters; кредитный риск; статистические данные; логит-модель; байесовские сети; параметры качества моделей; кредитний ризик; статистичні дані; логіт-модель; байєсівські мережі; параметри якості моделей
• Онлайн доступ: http://journal.iasa.kpi.ua/article/view/101737
• Назва журналу: System research and information technologies

In this research, credit risks are analyzed for financial organizations using data mining techniques applied to actual data. The two sets of actual statistical data characterizing the borrowers are employed for constructing mathematical models in the form of the nonlinear logit regression, decision trees, and Bayesian networks. The constructed models are analyzed with a set of appropriate statistical criteria, providing a basis for selecting the best alternative model. A series of computational experiments have been carried out using the two sets of actual statistical data from a Ukrainian bank. As a result of the performed computations, it was established that the best models in this application turned out to be nonlinear logit equations and Bayesian networks. In the future studies, we suppose to expand the number of model constructing techniques and to apply the idea of combining the estimates generated by the alternative models. Also, a specialized decision support system is to be constructed for the purpose of carrying research in the area of financial risks estimation and prediction.