Szczegóły publikacji
Opis bibliograficzny
Modelling recovery rates in mass debt portfolios using machine learning algorithms / Łukasz JANKOWSKI, Rafał JANKOWSKI // W: Proceedings of the 46th International Business Information Management Association Computer Science Conference (IBIMA) [Dokument elektroniczny] : green and digital transitions, and artificial intelligence to boost competitiveness in global economies : 26-27 November 2025, Ronda, Spain / ed. Khalid S. Soliman. — Wersja do Windows. — Dane tekstowe. — [Spain] : International Business Information Management Association (IBIMA), cop. 2025. — ( Proceedings of the... International Business Information Management Association Conference ; ISSN 2767-9640 ). — e-ISBN: 979-8-9867719-8-4. — S. 1366–1380. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://s.agh.edu.pl/GWvFl [2026-01-13]. — Bibliogr. s. 1379–1380, Abstr. — Dostęp po zalogowaniu ; Abstract na stronie https://s.agh.edu.pl/B8kIJ ; Prezentacja na stronie https://www.youtube.com/watch?v=lvKF_ffBr50
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 165413 |
|---|---|
| Data dodania do BaDAP | 2026-01-14 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | International Business Information Management 2025 |
| Czasopismo/seria | Proceedings of the... International Business Information Management Association Conference |
Abstract
The growing use of data analytics in the debt collection sector has increased the demand for accurate and transparent models capable of predicting recovery levels in large portfolios of mass receivables. Despite the rising importance of machine-learning methods in financial analysis, the literature still lacks empirical studies based on real operational data from large-scale portfolios. This work addresses this gap by conducting a comparative assessment of three tree-based machine-learning algorithms: decision tree, random forest and XGBoost. The models were trained on ex-ante data derived from 389,250 actual receivables serviced by an entity operating on the Polish debt collection market. The applied approach included an extensive hyperparameter tuning procedure and an evaluation of predictive performance using MAE, RMSE and R² metrics. To enhance interpretability and ensure transparency relevant to managerial and regulatory analysis, SHAP values were employed, enabling the identification of the most important variables influencing model outcomes. The obtained results indicate that the random forest model provides the most favourable balance between accuracy and generalisation ability, outperforming the single decision tree and achieving slightly better results than XGBoost. The most significant predictors were the nominal claim value, the purchase price and the debtor’s age, complemented by regional characteristics and legal-form attributes. These findings have important managerial and economic implications, supporting more precise portfolio valuation, more effective risk assessment, better allocation of operational resources and improved planning of both amicable and enforcement strategies.
