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.
top of page
Rechercher
Posts récents
Voir tout“As analysts are primary recipients of these reports, we investigate whether and how analyst forecast properties have changed following...
This study proposes a new method for detecting insider trading. The method combines principal component analysis (PCA) with random forest...
Cyber risk classifications often fail in out-of-sample forecasting despite their in-sample fit. Dynamic, impact-based classifiers...
bottom of page
留言