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This is a Quantitative Study study.

Decision Tree-Based Credit Card Fraud Detection System: Design and Optimization

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This method improves detection accuracy and reduces false positive rates in credit card fraud, suggesting robust financial risk control.

The proposed method enhances detection performance in credit card fraud, improving accuracy significantly.
Utilizing a cost-sensitive decision tree algorithm addresses class imbalance, resulting in a refined detection framework.
Integration of dynamic feature engineering and ensemble learning strategies improves real-time interpretation and effectiveness.
Empirical results highlight the method's superiority over traditional fraud detection techniques.

Jian Sun (2025) studied this question.

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