SilIF: Silhouette-Augmented Isolation Forest for Unsupervised Transaction Fraud Detection
SilIF enhances Isolation Forest with a silhouette-based scoring layer by clustering path-length fingerprints. On IEEE-CIS fraud benchmark, it improves AUC-PR by 0.0080 on average; no improvement on synthetic Sparkov dataset.
Article intelligence
Key points
- SilIF adds silhouette scoring to Isolation Forest via per-tree path length clustering.
- Achieves +0.0080 AUC-PR on IEEE-CIS benchmark with statistical significance.
- No improvement on Sparkov synthetic dataset, highlighting condition-dependent performance.
- Tunable and easy-to-deploy enhancement with open-source code.
Why it matters
This matters because silIF adds silhouette scoring to Isolation Forest via per-tree path length clustering.
Technical impact
May affect research directions, evaluation methods, open-source reproduction, and productization paths.
[2605.26135] SilIF: Silhouette-Augmented Isolation Forest for Unsupervised Transaction Fraud Detection
[Submitted on 21 May 2026]
Title:SilIF: Silhouette-Augmented Isolation Forest for Unsupervised Transaction Fraud Detection
View a PDF of the paper titled SilIF: Silhouette-Augmented Isolation Forest for Unsupervised Transaction Fraud Detection, by Venkatakrishnan Gopalakrishnan
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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 point fits its assigned group versus the nearest alternative. The silhouette signal is combined with the base IF score via a single hyperparameter alpha. On the IEEE-CIS Fraud Detection benchmark (~590K transactions, 3.5% fraud), SilIF with alpha=1.0 improves over plain Isolation Forest by +0.0080 AUC-PR on average across five seeds, with SilIF winning on all five seeds (paired t-test p=0.046). We also report results on a synthetic credit-card dataset (Sparkov) where the silhouette augmentation does not improve over plain IF, and we characterize the conditions that distinguish the two outcomes. The paper presents SilIF as a tunable, easy-to-deploy enhancement to Isolation Forest with honest reporting of when it helps and when it does not. Code at this https URL.
Comments: 5 pages, 1 figure, 5 tables. Code: this https URL
Subjects:
Machine Learning (cs.LG)
MSC classes: I.2.6, I.5.3
Cite as: arXiv:2605.26135 [cs.LG]
(or arXiv:2605.26135v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.26135
arXiv-issued DOI via DataCite
Submission history
From: Venkatakrishnan Gopalakrishnan [view email] [v1] Thu, 21 May 2026 15:01:30 UTC (26 KB)
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