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Forecasting recovery rates on non-performing loans with machine learning

Author

Listed:
  • Bellotti, Anthony
  • Brigo, Damiano
  • Gambetti, Paolo
  • Vrins, Frédéric

Abstract

We compare the performance of a wide set of regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees, and random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors that refer to the bank recovery process — prior to the portfolio’s sale to a debt collector — are also shown to enhance forecasting performance. These variables, derived from the time series of contacts to defaulted clients and client reimbursements to the bank, help all algorithms better identify debtors with different repayment ability and/or commitment, and in general those with different recovery potential.
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Suggested Citation

  • Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2020. "Forecasting recovery rates on non-performing loans with machine learning," LIDAM Reprints LFIN 2020002, Université catholique de Louvain, Louvain Finance (LFIN).
    • Handle: RePEc:ajf:louvlr:2020002
    Note: In : International Journal of Forecasting, Vol. 37, no. 1, p. 428-444
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    Cited by:

    1. Nazemi, Abdolreza & Rezazadeh, Hani & Fabozzi, Frank J. & Höchstötter, Markus, 2022. "Deep learning for modeling the collection rate for third-party buyers," International Journal of Forecasting, Elsevier, vol. 38(1), pages 240-252.
    2. Nazemi, Abdolreza & Fabozzi, Frank J., 2024. "Interpretable machine learning for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 164(C).
    3. Rakshith Bhandary & Bidyut Kumar Ghosh, 2025. "Credit Card Default Prediction: An Empirical Analysis on Predictive Performance Using Statistical and Machine Learning Methods," JRFM, MDPI, vol. 18(1), pages 1-20, January.
    4. Peng, Qiao & McKillop, Donal & Quinn, Barry & Liu, Kailong, 2025. "Modeling and predicting failure in US credit unions," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1237-1259.
    5. Damiano Brigo & Xiaoshan Huang & Andrea Pallavicini & Haitz Saez de Ocariz Borde, 2021. "Interpretability in deep learning for finance: a case study for the Heston model," Papers 2104.09476, arXiv.org.
    6. Distaso, Walter & Roccazzella, Francesco & Vrins, Frédéric, 2025. "Business cycle and realized losses in the consumer credit industry," European Journal of Operational Research, Elsevier, vol. 323(3), pages 1024-1039.
    7. Hazar Altınbaş & Gülay Hanişoğlu, 2023. "Forecasting and Evaluation of Non-Performing Loans in the Turkish Banking Sector," Istanbul Business Research, Istanbul University Business School, vol. 52(2), pages 381-406, August.
    8. Konstantin Gorgen & Abdolreza Nazemi & Melanie Schienle, 2022. "Robust Knockoffs for Controlling False Discoveries With an Application to Bond Recovery Rates," Papers 2206.06026, arXiv.org.
    9. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
    10. Andrey Koltays & Anton Konev & Alexander Shelupanov, 2021. "Mathematical Model for Choosing Counterparty When Assessing Information Security Risks," Risks, MDPI, vol. 9(7), pages 1-13, July.
    11. Marc Gürtler & Marvin Zöllner, 2023. "Heterogeneities among credit risk parameter distributions: the modality defines the best estimation method," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 251-287, March.
    12. 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.
    13. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    14. Carleo, Alessandra & Rocci, Roberto, 2024. "Functional clustering of NPLs recovery curves," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    15. Marjan Alirezaie & William Hoffman & Paria Zabihi & Hossein Rahnama & Alex Pentland, 2024. "Decentralized Data and Artificial Intelligence Orchestration for Transparent and Efficient Small and Medium-Sized Enterprises Trade Financing," JRFM, MDPI, vol. 17(1), pages 1-16, January.
    16. Maria Carannante & Valeria D’Amato & Paola Fersini & Salvatore Forte & Giuseppe Melisi, 2024. "Machine learning due diligence evaluation to increase NPLs profitability transactions on secondary market," Review of Managerial Science, Springer, vol. 18(7), pages 1963-1983, July.
    17. Ozili, Peterson K, 2025. "Bank non-performing loans research around the world," MPRA Paper 125217, University Library of Munich, Germany.
    18. Pan Tang & Yuwei Zhang, 2024. "China's business cycle forecasting: a machine learning approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(5), pages 2783-2811, November.
    19. Tanasuica Zotic Coralia, 2024. "A Quantitative Analysis of Default Risk Using Machine Learning and SHAP Value Interpretation," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 233-245.
    20. Jiajia, Liu & Kun, Guo & Fangcheng, Tang & Yahan, Wang & Shouyang, Wang, 2023. "The effect of the disposal of non-performing loans on interbank liquidity risk in China: A cash flow network-based analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 105-119.
    21. González, Marta Ramos & Ureña, Antonio Partal & Fernández-Aguado, Pilar Gómez, 2023. "Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    22. Kellner, Ralf & Nagl, Maximilian & Rösch, Daniel, 2022. "Opening the black box – Quantile neural networks for loss given default prediction," Journal of Banking & Finance, Elsevier, vol. 134(C).

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