Business Analytics Applications for Consumer Credits

Claudia Antal-Vaida ()
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Claudia Antal-Vaida: The Bucharest University of Economic Studies, Romania

Database Systems Journal, 2020, vol. 11, issue 1, 14-23

Abstract: The fast-paced and dynamic economical background determines all the industries to quickly adapt to change and adopt emerging technologies to remain competitive on the market. This tendency led to high volumes of data generated each second and to a decreasing ability of the manpower to analyze it and use if for beneficial purposes. This paper reviews the impact of Digital Transformation on the Banking area and how financial institutions leverage the advantage created by this trend, especially in the credit risk management field. Multiple papers on consumer credit scoring models written after the financial crisis from 2007 were reviewed and their results were summarized in this article, to increase the accuracy of future analysis by leveraging the already known results.

Keywords: Business Analytics; Machine Learning; Banking; Credit Risk Assessment; Scoring Models; Consumer Credits (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:aes:dbjour:v:11:y:2020:i:1:p:14-23

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