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Современные подходы к применению методов интеллектуального анализа данных в задаче кредитного скоринга

т. 23, вып. 34, сентябрь 2017

PDF-версия статьи

Получена: 04.07.2017

Получена в доработанном виде: 09.08.2017

Одобрена: 24.08.2017

Доступна онлайн: 19.09.2017

Рубрика: Банковская деятельность

Коды JEL: C38, C55, D81

Страницы: 2044–2060

https://doi.org/10.24891/fc.23.34.2044

Предмет. Рост спроса на потребительские кредиты привел к увеличению конкуренции на рынке кредитования. Банки и другие кредитные институты столкнулись с необходимостью обрабатывать большие объемы данных со все возрастающей скоростью. Современные требования к объему обрабатываемых данных и скорости их обработки таковы, что процессы должны быть практически полностью автоматизированы. Эти требования распространяются не только на непосредственную цифровую обработку, но и на процедуры настройки, адаптации и даже построения соответствующих количественных моделей. Традиционно используемые в кредитовании модели, такие как скоринг, стали комбинироваться с новыми вычислительными методами, которые относят к области так называемого машинного обучения или интеллектуального анализа данных. В статье приводится обзор современного состояния исследований в этой области.
Цели. Классификация современных методов кредитного скоринга. Описание моделей сравнения эффективности его различных методов.
Методология. Изучение актуальных научных публикаций по теме статьи, представленных в Google Scholar.
Результаты. Представлена классификация современных методов интеллектуального анализа данных, применяемых в кредитном скоринге.
Выводы. Требующийся в современных условиях уровень эффективности могут обеспечить модели кредитного скоринга, использующие процедуры машинного обучения и гибридные модели, в которых применяются комбинированные методы.

Ключевые слова: кредитный скоринг, машинное обучение, интеллектуальная обработка данных

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