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arXiv AI By Lidia Losavio, Dario Fenoglio, Martin Gjoreski, Marc Langheinrich

Pricing Cross-Bank Context in Federated Anti-Money Laundering

Read the original on arXiv AI →

The paper investigates how to detect money‑laundering patterns across separate bank transaction graphs using federated learning. By leveraging transactions that both banks already record, the authors approximate remote account states locally and run multi‑layer Graph Neural Networks without exchanging sensitive account data. Experiments on AMLworld HI‑Small show that a simple baseline using locally available information yields the greatest performance, while adding limited context such as transaction counts improves results; however, sharing detailed remote states requires excessive noise that diminishes utility.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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