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Credit Risk Analysis Using Machine and Deep Learning Models

Author

Listed:
  • Peter Martey Addo

    (Direction du Numérique, AFD—Agence Française de Développement, Paris 75012, France
    Laboratory of Excellence for Financial Regulation (LabEx ReFi), Paris 75011, France)

  • Dominique Guegan

    (Laboratory of Excellence for Financial Regulation (LabEx ReFi), Paris 75011, France
    IPAG Business School, University Paris 1 Pantheon Sorbonne, Ca’Foscari Unversity of Venezia, Venezia 30123, Italy
    Université Paris 1 Panthéon-Sorbonne, CES, 106 bd de l’Hôpital, Paris 75013, France)

  • Bertrand Hassani

    (Laboratory of Excellence for Financial Regulation (LabEx ReFi), Paris 75011, France
    Université Paris 1 Panthéon-Sorbonne, CES, 106 bd de l’Hôpital, Paris 75013, France
    Capgemini Consulting, Courbevoie 92400, France
    University College London Computer Science, 66-72 Gower Street, London WC1E 6EA, UK)

Abstract

Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency. In this work, we build binary classifiers based on machine and deep learning models on real data in predicting loan default probability. The top 10 important features from these models are selected and then used in the modeling process to test the stability of binary classifiers by comparing their performance on separate data. We observe that the tree-based models are more stable than the models based on multilayer artificial neural networks. This opens several questions relative to the intensive use of deep learning systems in enterprises.

Suggested Citation

  • Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
  • Handle: RePEc:gam:jrisks:v:6:y:2018:i:2:p:38-:d:141267
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    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. Butaru, Florentin & Chen, Qingqing & Clark, Brian & Das, Sanmay & Lo, Andrew W. & Siddique, Akhtar, 2016. "Risk and risk management in the credit card industry," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 218-239.
    3. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(5), pages 687-698, October.
    4. A. Seetharaman & Vikas Kumar Sahu & A. S. Saravanan & John Rudolph Raj & Indu Niranjan, 2017. "The Impact of Risk Management in Credit Rating Agencies," Risks, MDPI, vol. 5(4), pages 1-16, September.
    5. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    6. 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.
    7. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(3), pages 381-386, June.
    8. Robert Tibshirani, 2011. "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 273-282, June.
    9. Justin Sirignano & Apaar Sadhwani & Kay Giesecke, 2016. "Deep Learning for Mortgage Risk," Papers 1607.02470, arXiv.org, revised Mar 2018.
    10. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(4), pages 525-537, August.
    11. Ron S. Kenett & Silvia Salini, 2011. "Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 27(5), pages 465-475, September.
    12. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(2), pages 285-292, April.
    13. Gastwirth, Joseph L, 1972. "The Estimation of the Lorenz Curve and Gini Index," The Review of Economics and Statistics, MIT Press, vol. 54(3), pages 306-316, August.
    14. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    15. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(1), pages 151-159, February.
    16. Angelini, Eliana & di Tollo, Giacomo & Roli, Andrea, 2008. "A neural network approach for credit risk evaluation," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(4), pages 733-755, November.
    17. Yitzhaki, Shlomo, 1983. "On an Extension of the Gini Inequality Index," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 24(3), pages 617-628, October.
    18. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    19. Lerman, Robert I. & Yitzhaki, Shlomo, 1984. "A note on the calculation and interpretation of the Gini index," Economics Letters, Elsevier, vol. 15(3-4), pages 363-368.
    20. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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