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The Changing Landscape of Financial Credit Risk Models

Author

Listed:
  • Tanja Verster

    (Centre for Business Mathematics and Informatics, North-West University, Potchefstroom 2531, South Africa
    National Institute for Theoretical and Computational Sciences (NITheCS), Stellenbosch 7600, South Africa)

  • Erika Fourie

    (Pure and Applied Analytics, School of Mathematical and Statistical Sciences, North-West University, Potchefstroom 2531, South Africa)

Abstract
The landscape of financial credit risk models is changing rapidly. This study takes a brief look into the future of predictive modelling by considering some factors that influence financial credit risk modelling. The first factor is machine learning. As machine learning expands, it becomes necessary to understand how these techniques work and how they can be applied. The second factor is financial crises. Where predictive models view the future as a reflection of the past, financial crises can violate this assumption. This creates a new field of research on how to adjust predictive models to incorporate forward-looking conditions, which include future expected financial crises. The third factor considers the impact of financial technology (Fintech) on the future of predictive modelling. Fintech creates new applications for predictive modelling and therefore broadens the possibilities in the financial predictive modelling field. This changing landscape causes some challenges but also creates a wealth of opportunities. One way of exploiting these opportunities and managing the associated risks is via industry collaboration. Academics should join hands with industry to create industry-focused training and industry-focused research. In summary, this study made three novel contributions to the field of financial credit risk models. Firstly, it conducts an investigation and provides a comprehensive discussion on three factors that contribute to rapid changes in the credit risk predictive models’ landscape. Secondly, it presents a unique discussion of the challenges and opportunities arising from these factors. Lastly, it proposes an innovative solution, specifically collaboration between academic and industry partners, to effectively manage the challenges and take advantage of the opportunities for mutual benefits.

Suggested Citation

  • Tanja Verster & Erika Fourie, 2023. "The Changing Landscape of Financial Credit Risk Models," IJFS, MDPI, vol. 11(3), pages 1-15, August.
    • Handle: RePEc:gam:jijfss:v:11:y:2023:i:3:p:98-:d:1210595
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    References listed on IDEAS

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    1. 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.
    2. Rizwan, Muhammad Suhail & Ahmad, Ghufran & Ashraf, Dawood, 2020. "Systemic risk: The impact of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    3. Francesca Bell & Gary van Vuuren, 2022. "The impact of climate risk on corporate credit risk," Cogent Economics & Finance, Taylor & Francis Journals, vol. 10(1), pages 2148362-214, December.
    4. Majid Bazarbash, 2019. "FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk," IMF Working Papers 2019/109, International Monetary Fund.
    5. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    6. Hodula, Martin, 2023. "Interest rates as a finance battleground? The rise of Fintech and big tech credit providers and bank interest margin," Finance Research Letters, Elsevier, vol. 53(C).
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    Cited by:

    1. Partha Sengupta & Christopher H. Wheeler, 2024. "Credit card loss forecasting: Some lessons from COVID," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2448-2477, November.

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