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Explainable Machine Learning in Credit Risk Management

Author

Listed:
  • Niklas Bussmann

    (University of Pavia)

  • Paolo Giudici

    (University of Pavia)

  • Dimitri Marinelli

    (FinNet-Project)

  • Jochen Papenbrock

    (FIRAMIS)

Abstract

The paper proposes an explainable Artificial Intelligence model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing peer to peer lending platforms. The model applies correlation networks to Shapley values so that Artificial Intelligence predictions are grouped according to the similarity in the underlying explanations. The empirical analysis of 15,000 small and medium companies asking for credit reveals that both risky and not risky borrowers can be grouped according to a set of similar financial characteristics, which can be employed to explain their credit score and, therefore, to predict their future behaviour.

Suggested Citation

  • Niklas Bussmann & Paolo Giudici & Dimitri Marinelli & Jochen Papenbrock, 2021. "Explainable Machine Learning in Credit Risk Management," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 203-216, January.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:1:d:10.1007_s10614-020-10042-0
    DOI: 10.1007/s10614-020-10042-0
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    References listed on IDEAS

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    1. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871.
    2. Bracke, Philippe & Datta, Anupam & Jung, Carsten & Sen, Shayak, 2019. "Machine learning explainability in finance: an application to default risk analysis," Bank of England working papers 816, Bank of England.
    3. Giudici, Paolo, 2018. "Financial data science," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 160-164.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Dangxing Chen, 2023. "Can I Trust the Explanations? Investigating Explainable Machine Learning Methods for Monotonic Models," Papers 2309.13246, arXiv.org.
    2. Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2023. "Bankruptcy prediction using machine learning and Shapley additive explanations," Post-Print hal-04223161, HAL.
    3. Zhang, Tianjiao & Zhu, Weidong & Wu, Yong & Wu, Zihao & Zhang, Chao & Hu, Xue, 2023. "An explainable financial risk early warning model based on the DS-XGBoost model," Finance Research Letters, Elsevier, vol. 56(C).
    4. Yanhui Shen, 2023. "American Option Pricing using Self-Attention GRU and Shapley Value Interpretation," Papers 2310.12500, arXiv.org.
    5. Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Forecasting, MDPI, vol. 3(2), pages 1-44, May.
    6. Alhanouf Abdulrahman Saleh Alsuwailem & Emad Salem & Abdul Khader Jilani Saudagar, 2023. "Performance of Different Machine Learning Algorithms in Detecting Financial Fraud," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1631-1667, December.
    7. Wei Jie Yeo & Wihan van der Heever & Rui Mao & Erik Cambria & Ranjan Satapathy & Gianmarco Mengaldo, 2023. "A Comprehensive Review on Financial Explainable AI," Papers 2309.11960, arXiv.org.
    8. Kim Long Tran & Hoang Anh Le & Thanh Hien Nguyen & Duc Trung Nguyen, 2022. "Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam," Data, MDPI, vol. 7(11), pages 1-12, November.
    9. Berger, Theo, 2023. "Explainable artificial intelligence and economic panel data: A study on volatility spillover along the supply chains," Finance Research Letters, Elsevier, vol. 54(C).
    10. Md Shajalal & Alexander Boden & Gunnar Stevens, 2022. "Explainable product backorder prediction exploiting CNN: Introducing explainable models in businesses," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2107-2122, December.
    11. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    12. Chen, Yujia & Calabrese, Raffaella & Martin-Barragan, Belen, 2024. "Interpretable machine learning for imbalanced credit scoring datasets," European Journal of Operational Research, Elsevier, vol. 312(1), pages 357-372.
    13. Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2023. "Explainable FinTech lending," Journal of Economics and Business, Elsevier, vol. 125.
    14. Sunghyon Kyeong & Daehee Kim & Jinho Shin, 2021. "Can System Log Data Enhance the Performance of Credit Scoring?—Evidence from an Internet Bank in Korea," Sustainability, MDPI, vol. 14(1), pages 1-12, December.
    15. Mohsin Ali & Abdul Razaque & Joon Yoo & Uskenbayeva Raissa Kabievna & Aiman Moldagulova & Satybaldiyeva Ryskhan & Kalpeyeva Zhuldyz & Aizhan Kassymova, 2024. "Designing an Intelligent Scoring System for Crediting Manufacturers and Importers of Goods in Industry 4.0," Logistics, MDPI, vol. 8(1), pages 1-30, March.
    16. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    17. Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).
    18. Ahelegbey, Daniel & Giudici, Paolo & Pediroda, Valentino, 2023. "A network based fintech inclusion platform," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    19. Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2022. "Explainable artificial intelligence for crypto asset allocation," Finance Research Letters, Elsevier, vol. 47(PB).
    20. Marc Wildi & Branka Hadji Misheva, 2022. "A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection," Papers 2212.02906, arXiv.org.
    21. Ajitha Kumari Vijayappan Nair Biju & Ann Susan Thomas & J Thasneem, 2024. "Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 849-878, February.
    22. Bastos, João A. & Matos, Sara M., 2022. "Explainable models of credit losses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
    23. Alex Gramegna & Paolo Giudici, 2020. "Why to Buy Insurance? An Explainable Artificial Intelligence Approach," Risks, MDPI, vol. 8(4), pages 1-9, December.
    24. David Mhlanga, 2021. "Financial Inclusion in Emerging Economies: The Application of Machine Learning and Artificial Intelligence in Credit Risk Assessment," IJFS, MDPI, vol. 9(3), pages 1-16, July.

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