Researchers from BI Norwegian Business School and NHH Norwegian School of Economics have developed a new behavioral credit-risk model that integrates credit and debit transactions, significantly outperforming state-of-the-art machine learning methods in predicting credit card delinquency. The study, published in The Journal of Finance and Data Science, demonstrates that combining credit card data with customers' debit transactions substantially improves the ability to predict credit card delinquency.
The research team developed a hierarchical Bayesian behavioral model that consistently outperforms leading machine-learning algorithms including XGBoost, GBM, neural networks, and stacked ensembles. According to first author Håvard Huse, credit data alone provides only a partial picture of a customer's financial situation. By integrating debit transactions, the model gains insight into payday spending, repayment behavior, and income patterns—factors that strongly influence whether someone is at risk of missing payments.
The study draws on detailed credit and debit transaction data from a large Norwegian bank. Traditional credit-risk models rely heavily on monthly aggregates such as balance and credit limit, but these measures do not reveal how customers actually manage their finances day-to-day. The new model captures behavioral dynamics, including how repayment patterns evolve over time and how spending spikes after payday, explaining both why delinquency occurs and who is likely to default.
The model improves prediction accuracy at the individual level and identifies distinct behavioral segments with different "memory lengths"—the extent to which past financial states affect current repayment behavior. Customers in financial distress tend to be more influenced by earlier months' behavior, and the model captures this dynamic far better than standard machine-learning tools. Notably, the team's approach not only performs better than state-of-the-art algorithms but is also more interpretable, allowing banks to understand which behavioral patterns drive risk.
Using a three-month prediction horizon, early detection of at-risk cardholders could generate substantial cost savings by enabling timely intervention and reducing losses. For banks, this represents more than an accuracy improvement—it provides a way to proactively help customers avoid serious financial problems. The findings highlight an emerging shift in credit scoring from traditional static models toward richer behavioral analytics based on a full picture of customer transactions.
