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A robust machine learning approach for credit risk analysis of large loan level datasets using deep learning and extreme gradient boosting

In: Are post-crisis statistical initiatives completed?

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  • Anastasios Petropoulos
  • Vasilis Siakoulis
  • Evaggelos Stavroulakis
  • Aristotelis Klamargias

Abstract

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Suggested Citation

  • Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Are post-crisis statistical initiatives completed?, volume 49, Bank for International Settlements.
    • Handle: RePEc:bis:bisifc:49-49
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    References listed on IDEAS

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    1. Avery, Robert B. & Calem, Paul S. & Canner, Glenn B., 2004. "Consumer credit scoring: Do situational circumstances matter?," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 835-856, April.
    2. Mizen, Paul & Tsoukas, Serafeim, 2012. "Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model," International Journal of Forecasting, Elsevier, vol. 28(1), pages 273-287.
    3. 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.
    4. Peter Martey Addo & Dominique Guégan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Documents de travail du Centre d'Economie de la Sorbonne 18003, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    5. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01835164, HAL.
    6. Kamstra, Mark & Kennedy, Peter & Suan, Teck-Kin, 2001. "Combining Bond Rating Forecasts Using Logit," The Financial Review, Eastern Finance Association, vol. 36(2), pages 75-96, May.
    7. Dominique Guegan, 2018. "Credit Risk Analysis Using machine and Deep Learning Models," Post-Print halshs-01889154, HAL.
    8. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Working Papers 2018:08, Department of Economics, University of Venice "Ca' Foscari".
    9. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep Learning models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01719983, HAL.
    10. 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.
    11. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549, World Scientific Publishing Co. Pte. Ltd..
    12. Robert B. Avery & Paul S. Calem & Glenn B. Canner, 2004. "Consumer credit scoring: do situational circumstances matter?," BIS Working Papers 146, Bank for International Settlements.
    13. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    14. Petr Gurný & Martin Gurný, 2013. "Comparison of Credit Scoring Models on Probability of Default Estimation for Us Banks," Prague Economic Papers, Prague University of Economics and Business, vol. 2013(2), pages 163-181.
    15. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    16. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Post-Print halshs-01835164, HAL.
    17. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    18. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    19. Dominique Guegan, 2018. "Credit Risk Analysis Using machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01889154, HAL.
    20. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep Learning models," Post-Print halshs-01719983, HAL.
    21. 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.
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    Cited by:

    1. Onder Ozgur & Erdal Tanas Karagol & Fatih Cemil Ozbugday, 2021. "Machine learning approach to drivers of bank lending: evidence from an emerging economy," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-29, December.
    2. Roy Cerqueti & Francesca Pampurini & Annagiulia Pezzola & Anna Grazia Quaranta, 2022. "Dangerous liasons and hot customers for banks," Review of Quantitative Finance and Accounting, Springer, vol. 59(1), pages 65-89, July.
    3. A. R. Provenzano & D. Trifir`o & A. Datteo & L. Giada & N. Jean & A. Riciputi & G. Le Pera & M. Spadaccino & L. Massaron & C. Nordio, 2020. "Machine Learning approach for Credit Scoring," Papers 2008.01687, arXiv.org.
    4. Jagjit S. Chadha & Richard Barwell, 2019. "Renewing our Monetary Vows: Open Letters to the Governor of the Bank of England," National Institute of Economic and Social Research (NIESR) Occasional Papers 58, National Institute of Economic and Social Research.
    5. Andrés Alonso Robisco & José Manuel Carbó Martínez, 2022. "Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.
    6. Andrés Alonso & José Manuel Carbó, 2021. "Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation," Working Papers 2105, Banco de España.
    7. 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.
    8. Cerqueti, Roy & Pampurini, Francesca & Quaranta, Anna Grazia & Storani, Saverio, 2024. "Risk transmission, systemic fragility of banks’ interacting customers and credit worthiness assessment," Finance Research Letters, Elsevier, vol. 62(PA).
    9. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    10. Jaewon Park & Minsoo Shin & Wookjae Heo, 2021. "Estimating the BIS Capital Adequacy Ratio for Korean Banks Using Machine Learning: Predicting by Variable Selection Using Random Forest Algorithms," Risks, MDPI, vol. 9(2), pages 1-19, February.
    11. García-Céspedes, Rubén & Moreno, Manuel, 2022. "The generalized Vasicek credit risk model: A Machine Learning approach," Finance Research Letters, Elsevier, vol. 47(PA).
    12. Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).

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