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Cross-Domain Behavioral Credit Modeling: transferability from private to central data

Author

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  • O. Didkovskyi
  • N. Jean
  • G. Le Pera
  • C. Nordio

Abstract

This paper introduces a credit risk rating model for credit risk assessment in quantitative finance, aiming to categorize borrowers based on their behavioral data. The model is trained on data from Experian, a widely recognized credit bureau, to effectively identify instances of loan defaults among bank customers. Employing state-of-the-art statistical and machine learning techniques ensures the model's predictive accuracy. Furthermore, we assess the model's transferability by testing it on behavioral data from the Bank of Italy, demonstrating its potential applicability across diverse datasets during prediction. This study highlights the benefits of incorporating external behavioral data to improve credit risk assessment in financial institutions.

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  • O. Didkovskyi & N. Jean & G. Le Pera & C. Nordio, 2024. "Cross-Domain Behavioral Credit Modeling: transferability from private to central data," Papers 2401.09778, arXiv.org.
    • Handle: RePEc:arx:papers:2401.09778
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    References listed on IDEAS

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    6. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
    7. Quinn McNemar, 1947. "Note on the sampling error of the difference between correlated proportions or percentages," Psychometrika, Springer;The Psychometric Society, vol. 12(2), pages 153-157, June.
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    Cited by:

    1. O. Didkovskyi & A. Vidali & N. Jean & G. Le Pera, 2026. "Temporal-Aligned Meta-Learning for Risk Management: A Stacking Approach for Multi-Source Credit Scoring," Papers 2601.07588, arXiv.org.

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