# Topological Fraud Detection in Latent Transaction Spaces

arXiv: https://arxiv.org/abs/2609.08445 · HTML: https://mlsanity.com/paper/2609.08445/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 8 Sep 2026.

## Builds on (most cited first, 3 of 3)

- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Why do tree-based models still outperform deep learning on tabular data?](https://mlsanity.com/paper/2207.08815/) (2022)
- [Topological Clustering of Agents in Hidden Information Contagions: Application to Financial Markets](https://mlsanity.com/paper/2410.21104/) (2024)

## 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.