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

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

Risultato della ricerca: Contributo in rivistaArticolo in rivista

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.
Lingua originaleEnglish
pagine (da-a)N/A-N/A
Numero di pagine21
RivistaReview of Managerial Science
DOI
Stato di pubblicazionePubblicato - 2023

Keywords

  • Machine Learning
  • NPLs

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