Credit Card Default Prediction Based on Machine Learning

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.