AI RESEARCH
SilIF: Silhouette-Augmented Isolation Forest for Unsupervised Transaction Fraud Detection
arXiv CS.LG
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ArXi:2605.26135v1 Announce Type: new Unsupervised anomaly detection is widely used in transaction fraud detection where labels are scarce. Isolation Forest (IF) is among the most popular classical methods due to its scalability and ease of deployment. We propose SilIF, an augmentation of Isolation Forest that adds a silhouette-based scoring layer computed in a representation space induced by the trees of the forest.