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Modern Approaches in Credit Risk Modeling: A Literature Review

Author

Listed:
  • Rogojan Luana Cristina

    (Bucharest University of Economic Studies, Bucharest, Romania Institute for Economic Forecasting, Bucharest, Romania)

  • Croicu Andreea Elena

    (Bucharest University of Economic Studies, Bucharest, Romania Institute for Economic Forecasting, Bucharest, Romania)

  • Iancu Laura Andreea

    (Bucharest University of Economic Studies, Bucharest, Romania Institute for Economic Forecasting, Bucharest, Romania)

Abstract

In the financial industry, models are pervasive, and their quantity and complexity continue to increase. Constant advancements are made in econometric and statistical theory, but a fast-developing body of rules and regulations governing their use needs modeling specialists to remain vigilant and adaptable. The tendency of these regulations to be ambiguous necessitates that industry professionals and institutions interpret them independently and jointly. This leads in what is referred to as a “industry standard,” or a set of procedures that are recognized among modeling professionals but not necessarily to those outside of the industry. Non-practitioners in the industry may view the modeling department as a “black box” for these reasons. The accurate evaluation of financial credit risk and the forecasting of bankruptcy are crucial to both the economy and society. In recent years, more and more approaches and algorithms have been advanced for this purpose. At this point, it is of the highest concern to investigate the current credit risk assessment methods. In this paper, we review the traditional statistical models and cutting-edge intelligent methods for forecasting financial distress, with a focus on the greatest advances in the academic literature, as the promising trend in this field. Lastly, the paper will conclude with an overview of the evolution of methodologies and conceptual frameworks in credit risk management research, as well as possible future research directions.

Suggested Citation

  • Rogojan Luana Cristina & Croicu Andreea Elena & Iancu Laura Andreea, 2023. "Modern Approaches in Credit Risk Modeling: A Literature Review," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 1617-1627, July.
    • Handle: RePEc:vrs:poicbe:v:17:y:2023:i:1:p:1617-1627:n:38
    DOI: 10.2478/picbe-2023-0145
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    References listed on IDEAS

    as
    1. Mohammad S. Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib, 2022. "Leveraging random forest in micro‐enterprises credit risk modelling for accuracy and interpretability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3713-3729, July.
    2. Xin Wang & Kai Zong & Cuicui Luo, 2022. "Credit risk detection based on machine learning algorithms," International Journal of Financial Services Management, Inderscience Enterprises Ltd, vol. 11(3), pages 183-189.
    3. Stephen Zamore & Kwame Ohene Djan & Ilan Alon & Bersant Hobdari, 2018. "Credit Risk Research: Review and Agenda," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(4), pages 811-835, March.
    4. Capotorti, Andrea & Barbanera, Eva, 2012. "Credit scoring analysis using a fuzzy probabilistic rough set model," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 981-994.
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