Digest Finance
 

Data Mining Techniques: Modern Approaches to Application in Credit Scoring

Vol. 22, Iss. 4, DECEMBER 2017

Received: 4 July 2017

Received in revised form: 9 August 2017

Accepted: 24 August 2017

Available online: 19 December 2017

Subject Heading: Banking

JEL Classification: C38, C55, D81

Pages: 400Ц412

https://doi.org/10.24891/df.22.4.400

Volkova V.S. Financial University under Government of Russian Federation, Moscow, Russian Federation EVolkova@fa.ru

Gisin V.B. Financial University under Government of Russian Federation, Moscow, Russian Federation VGisin@fa.ru

Solov'ev V.I. Financial University under Government of Russian Federation, Moscow, Russian Federation VSoloviev@fa.ru

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.

Keywords: loan scoring, credit score, machine learning, data mining

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