arXiv:2605.26135v1 Announce Type: new Abstract: 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. For each point, we extract a vector of per-tree path lengths, cluster these "fingerprints" into structural groups, and compute a silhouette score that measures how well
The continuous growth of digital transactions necessitates more robust and scalable fraud detection methods, driving innovation in unsupervised learning techniques like Isolation Forest.
Improved fraud detection algorithms directly impact financial security and operational efficiency for institutions handling large volumes of transactions, reducing losses and enhancing trust.
This research introduces an enhanced Isolation Forest method (SilIF) that promises more accurate and scalable unsupervised fraud detection, potentially leading to its wider adoption in financial systems.
- · Financial institutions
- · E-commerce platforms
- · AI/ML developers
- · Fraud prevention solution providers
- · Fraudsters
- · Legacy rule-based fraud detection systems
Financial institutions reduce fraud losses and improve detection rates.
The cost of processing transactions may decrease due to fewer manual reviews and chargebacks.
Enhanced fraud detection could enable new business models sensitive to transaction risk, fostering innovation in digital commerce.
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Read at arXiv cs.LG