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Integrating credit and debit data for enhanced insights into borrowing behavior and predictive modeling of credit card delinquency

Håvard Huse et al.

The Journal of Finance and Data Science2025https://doi.org/10.1016/j.jfds.2025.100166article
ABDC B
Weight
0.50

What the paper says

This research delves into the predictive modeling of credit card delinquency by harnessing both credit and debit data, offering a nuanced perspective on consumer financial behavior. The study introduces a novel hierarchical Bayesian regression model that significantly surpasses traditional machine learning algorithms in predictive accuracy. By integrating behavioral aspects of financial decision-making, the model provides a profound understanding of the factors influencing delinquency, such as payment timing and repayment ability.We found that the combination of credit and debit data allows for a more comprehensive assessment of a cardholder's financial behavior and risk potential. The model effectively captures individual variations in financial behavior, making it possible to predict delinquency with higher precision. This approach not only enhances the predictive power but also aids in understanding the underlying patterns of financial behavior that lead to credit risk.The practical implications of this research are substantial for financial institutions, which can leverage these insights to refine risk assessment processes and develop targeted strategies for managing credit risk. The findings advocate for a more informed approach to credit scoring that considers broader behavioral factors, offering a strategic advantage in the competitive financial services market.

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https://doi.org/https://doi.org/10.1016/j.jfds.2025.100166

Or copy a formatted citation

@article{håvard2025,
  title        = {{Integrating credit and debit data for enhanced insights into borrowing behavior and predictive modeling of credit card delinquency}},
  author       = {Håvard Huse et al.},
  journal      = {The Journal of Finance and Data Science},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jfds.2025.100166},
}

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Integrating credit and debit data for enhanced insights into borrowing behavior and predictive modeling of credit card delinquency

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.