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Topological Fraud Detection in Latent Transaction Spaces

Bourla, Avraham
arXiv (All) · Papers
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cryptography-and-securitymachine-learning
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
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