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

Maria Carannante (), Valeria D’Amato (), Paola Fersini (), Salvatore Forte () and Giuseppe Melisi ()
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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

Review of Managerial Science, 2024, vol. 18, issue 7, No 8, 1963-1983

Abstract: 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.

Keywords: NPLs; Machine learning; Due diligence; OVERALS; 91G15; 91G70 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11846-023-00635-y

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