Credit Card Default Prediction Based on Machine Learning

DOI

10.2991/978-2-38476-585-0_37How to use a DOI?

Keywords

AdaBoost; GBDT; Random Forest; Credit Card Default Prediction

Abstract

In recent years, credit cards have become deeply integrated into personal financial activities. While they provide ease and flexibility, they also introduce new challenges for managing financial risk. As the volume of credit card usage grows, concerns over potential defaults have drawn growing interest from the banking sector and related financial entities. Conventional approaches to evaluating credit risk often depend on rigid assumptions, making it difficult to account for the nuanced and dynamic nature of consumer behavior. This study investigates how machine learning techniques can improve default prediction by utilizing a real-world dataset. Three ensemble models—AdaBoost, Gradient Boosted Decision Tree (GBDT), and Random Forest—are implemented and assessed for their effectiveness in recognizing high-risk defaulters. Model performance is evaluated based on commonly used indicators such as accuracy, precision, recall, F1 score, and Area Under the Curve (AUC). Among the models, Random Forest demonstrates the strongest overall performance, especially in terms of balanced classification results and high AUC values. To further assess practical utility, the models are tested on two synthetic customer scenarios. All three models produce consistent outcomes, reinforcing their applicability to real-world cases. This research underscores the value of machine learning in refining credit risk analytics and contributes actionable insights for enhancing early warning frameworks in the finance sector.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shujun Yao
PY  - 2026
DA  - 2026/06/18
TI  - Credit Card Default Prediction Based on Machine Learning
BT  - Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025)
PB  - Atlantis Press
SP  - 312
EP  - 321
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-2-38476-585-0_37
DO  - 10.2991/978-2-38476-585-0_37
ID  - Yao2026
ER  -