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Data Mining Techniques: Modern Approaches to Application in Credit Scoring

т. 22, вып. 4, декабрь 2017

PDF  PDF-версия статьи

Получена: 04.07.2017

Получена в доработанном виде: 09.08.2017

Одобрена: 24.08.2017

Доступна онлайн: 19.12.2017

Рубрика: BANKING

Коды JEL: C38, C55, D81

Страницы: 400–412


Volkova V.S. Financial University under Government of Russian Federation, Moscow, Russian Federation 

Gisin V.B. Financial University under Government of Russian Federation, Moscow, Russian Federation 

Solov'ev V.I. Financial University under Government of Russian Federation, Moscow, Russian Federation 

Importance This article examines the current state of research in machine learning and data mining, which computational methods get combined with conventional lending models such as scoring, for instance.
Objectives The article aims to classify the modern methods of credit scoring and describe models for comparing the effectiveness of the various methods of credit scoring.
Methods To perform the tasks, we have studied relevant scientific publications on the article subject presented in Google Scholar.
Results The article presents a classification of modern data mining techniques used in credit scoring.
Conclusions and Relevance Credit scoring models using machine learning procedures and hybrid models using combined methods can provide the required level of efficiency in the modern environment.

Ключевые слова: loan scoring, credit score, machine learning, data mining

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