Topological Fraud Detection in Latent Transaction Spaces
Researchers present a privacy-preserving fraud detection method combining unsupervised filtering and supervised classification on anonymized transaction embeddings for low-latency triage.
The paper describes fraud detection performed entirely on topologically anonymized transaction embeddings. It iterates unsupervised filtering followed by supervised classification ('sniping') to flag suspicious activity. The goal is ultra-low-latency, privacy-preserving triage for institutions without exposing personally identifiable information.
- Unsupervised filtering plus supervised classification on anonymized embeddings
- Targets low-latency, privacy-preserving fraud triage
- Avoids exposing personally identifiable information during detection
From the original article
1 min read at arxiv.orgWorking 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.
By Avraham Bourla · arxiv.org
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