Machine Learning for Italian Non-Performing Loan Valuation: A Methodology Framework with Purged Cross-Validation
Year: 2026
Type: article
Abstract: Develops an integrated machine-learning framework for Italian NPL valuation, addressing three methodological gaps: (1) cross-validation leakage via purged CV with embargo periods; (2) high-cardinality categorical features (26 judicial districts) via LightGBM native categorical handling; (3) regulatory interpretability via SHAP global+local explanations. Six-model framework: AVM (R^2 = 0.50 OOT), recovery rate (R^2 = 0.73 OOT), Weibull recovery timing, share-of-wallet, unsecured-credit, and Nash-bargaining ZOPA. Synthetic dataset of 100,000 loans calibrated to Banca d'Italia, OMI, and Ministero della Giustizia aggregates. Median recovery time North: 25 months vs South: 70 months. Synthetic-data disclosure: results illustrative of the methodology; not empirical findings about actual portfolios. Path to real-data validation through Italian NPL servicer partnership documented.Clarification (2026-06): The synthetic-data disclosure and all headline metrics in this deposit are unchanged and accurate. The manuscript's reference to an "accompanying replication package" denotes the six valuation models (AVM, recovery-rate, share-of-wallet, unsecured, ZOPA, pricing), whose implementations reside in the author's CreditAI codebase; a standalone packaged release will be linked in a future version. Calibration targets are reconstructed from published Banca d'Italia, OMI, and Ministero della Giustizia aggregates.
Author: Stanisljevic, Luka
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7160713608
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