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Machine learning due diligence evaluation to increase NPLs profitability transactions on secondary market

Author

Listed:
  • Maria Carannante

    (University of Salerno)

  • Valeria D’Amato

    (University of Salerno)

  • Paola Fersini

    (Luiss ‘Guido Carli’ University)

  • Salvatore Forte

    (Università Telematica Giustino Fortunato)

  • Giuseppe Melisi

    (University of Sannio)

Abstract

In this paper, we contribute to the topic of the non-performing loans (NPLs) business profitability on the secondary market by developing machine learning-based due diligence. In particular, a loan became non-performing when the borrower is unlikely to pay, and we use the ability of the ML algorithms to model complex relationships between predictors and outcome variables, we set up an ad hoc dependent random forest regressor algorithm for projecting the recovery rate of a portfolio of the secured NPLs. Indeed the profitability of the transactions under consideration depends on forecast models of the amount of net repayments expected from receivables and related collection times. Finally, the evaluation approach we provide helps to reduce the ”lemon discount” by pricing the risky component of informational asymmetry between better-informed banks and potential investors in particular for higher quality, collateralised NPLs.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:rvmgts:v:18:y:2024:i:7:d:10.1007_s11846-023-00635-y
    DOI: 10.1007/s11846-023-00635-y
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    References listed on IDEAS

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    1. Nazemi, Abdolreza & Heidenreich, Konstantin & Fabozzi, Frank J., 2018. "Improving corporate bond recovery rate prediction using multi-factor support vector regressions," European Journal of Operational Research, Elsevier, vol. 271(2), pages 664-675.
    2. Nazemi, Abdolreza & Fabozzi, Frank J., 2018. "Macroeconomic variable selection for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 89(C), pages 14-25.
    3. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021. "Forecasting recovery rates on non-performing loans with machine learning," International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
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