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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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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. 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).
    9. 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.
    10. 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).
    11. 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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