mlsanityData to 7 Oct 2026

Topological Fraud Detection in Latent Transaction Spaces

arXiv 2609.08445 ↗First posted 8 Sep 2026cs.LG cs.CRcited by 0

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Abstract (arXiv)

Working entirely on topologically anonymized embeddings, we perform fraud detection using iterative rounds of unsupervised filtering followed by supervised sniping. The result is an ultra-low latency privacy--preserving triage that allows institutions to flag suspicious activity without compromising Personally Identifiable Information.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 23 references that are in the map. First posted on arXiv 8 Sep 2026.

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3 of 23 references.

References · 3

  1. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  2. 2022Why do tree-based models still outperform deep learning on tabular data?cited by 198
  3. 2024Topological Clustering of Agents in Hidden Information Contagions: Application to Financial Marketscited by 1

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