Credit card default prediction is a mature application of machine learning in consumer finance, yet published studies typically stop at discrimination metrics without connecting a model’s output to the economics of a specific intervention a bank could actually deploy. This paper builds an interpretable default-risk model using the public UCI Default of Credit Card Clients dataset (Yeh and Lien, 2009) and couples it to a cost-benefit framework built from cited, real-world figures. A logistic regression baseline achieves test ROC-AUC of 0.741, and a random forest comparison model achieves 0.780, on a cohort of 30,000 Taiwanese credit card clients with a 22.12% next-month default rate. Rather than a single flat dollar loss, this paper estimates avoided loss per client from the client’s own most recent bill amount multiplied by a published average loss-given-default rate for credit cards (61.54%; Bellotti & Crook, 2012). Against this, two realistic intervention channels are costed: an SMS reminder ($0.03) and a live-agent call ($2.70, reused from a companion study), with effectiveness drawn from a randomized field experiment on credit card debt repayment (a 4.4-percentage-point reduction in delinquency; Bursztyn, Fiorin, Gottlieb, & Kanz, 2019). Under these figures, the break-even cost per intervention for untargeted, blanket outreach is $457.45, far above both realistic channel costs, so model-based targeting adds negligible net value over contacting every client at either cost. This paper argues the finding is not a property of the models but of the ratio between typical credit card exposure sizes and low-cost communication channels, and discusses what would need to change for precision targeting to matter in this setting.

Aryan Patre,"From Default Risk to Priced Intervention: A Cost-Benefit Approach to Credit Card Risk Management" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(5): Page(1026-1030) September - October 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.