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Методы теории нечетких множеств в кредитном скоринге

Журнал «Финансы и кредит»
т. 23, вып. 35, сентябрь 2017

Получена: 04.07.2017

Получена в доработанном виде: 09.08.2017

Одобрена: 24.08.2017

Доступна онлайн: 29.09.2017

Рубрика: Банковская деятельность

Коды JEL: C38, C55, D81

Страницы: 2088–2106

https://doi.org/10.24891/fc.23.35.2088

Волкова Е.С. кандидат физико-математических наук, доцент департамента анализа данных, принятия решений и финансовых технологий, Финансовый университет при Правительстве Российской Федерации, Москва, Российская Федерация EVolkova@fa.ru

Гисин В.Б. кандидат физико-математических наук, профессор департамента анализа данных, принятия решений и финансовых технологий, Финансовый университет при Правительстве Российской Федерации, Москва, Российская Федерация VGisin@fa.ru

Соловьев В.И. доктор экономических наук, профессор, руководитель департамента анализа данных, принятия решений и финансовых технологий, Финансовый университет при Правительстве Российской Федерации, Москва, Российская Федерация VSoloviev@fa.ru

Предмет. В ряде случаев риск принятия решений может быть связан с неопределенностью, в основе которой лежат явления, не подчиняющиеся вероятностным закономерностям. Для моделирования неопределенности такого рода используются методы теории нечетких множеств. Они нашли свое применение и в кредитном скоринге. В сочетании с классическими методами они позволяют строить гибкие и эффективные модели. В статье приводится обзор современного состояния исследований, связанных с применением теории нечетких множеств и нечеткой логики в задачах кредитного скоринга.
Цели. Описание и классификация конструкций теории нечетких множеств и нечеткой логики, применяемых в современных моделях кредитного скоринга.
Методология. Изучение актуальных научных публикаций по теме статьи, представленных в Google Scholar.
Результаты. Представлено описание и анализ основных методов теории нечетких множеств, применяемых в кредитном скоринге.
Выводы. Применение нечетких множеств и нечеткой логики в моделях кредитного скоринга позволяет строить гибкие модели, допускающие естественную и понятную интерпретацию. Перспективным представляется направление, связанное с использованием систем нечеткого логического вывода.

Ключевые слова: кредитный скоринг, машинное обучение, нечеткие множества, нечеткая логика, нечеткий вывод

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