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Business Analytics Applications for Consumer Credits

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  • Claudia Antal-Vaida

    (The Bucharest University of Economic Studies, Romania)

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.

Suggested Citation

  • Claudia Antal-Vaida, 2020. "Business Analytics Applications for Consumer Credits," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 11(1), pages 14-23.
    • Handle: RePEc:aes:dbjour:v:11:y:2020:i:1:p:14-23
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    References listed on IDEAS

    as
    1. Lean Yu & Zebin Yang & Ling Tang, 2016. "A novel multistage deep belief network based extreme learning machine ensemble learning paradigm for credit risk assessment," Flexible Services and Manufacturing Journal, Springer, vol. 28(4), pages 576-592, December.
    2. Hardeep Chahal & Jeevan Jyoti & Jochen Wirtz (ed.), 2019. "Understanding the Role of Business Analytics," Springer Books, Springer, number 978-981-13-1334-9, January.
    3. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
    4. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    5. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
    Full references (including those not matched with items on IDEAS)

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