This study proposes an attention-based ensemble model for detecting credit card fraud, integrating classifiers' predictions using two aggregation operators (DOWA and IOWA). The model, which selects key features via a bootstrap forest, achieves 99.95% accuracy and a perfect AUC of 1, demonstrating the effectiveness of AI in fraud detection.
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