Paper 2024/065

Privacy-preserving Anti-Money Laundering using Secure Multi-Party Computation

Marie Beth van Egmond, Netherlands Organisation for Applied Scientific Research (TNO)
Vincent Dunning, Netherlands Organisation for Applied Scientific Research (TNO)
Stefan van den Berg, Netherlands Organisation for Applied Scientific Research (TNO)
Thomas Rooijakkers, Netherlands Organisation for Applied Scientific Research (TNO)
Alex Sangers, Netherlands Organisation for Applied Scientific Research (TNO)
Ton Poppe, ABN AMRO
Jan Veldsink, Rabobank

Money laundering is a serious financial crime where criminals aim to conceal the illegal source of their money via a series of transactions. Although banks have an obligation to monitor transactions, it is difficult to track these illicit money flows since they typically span over multiple banks, which cannot share this information due to privacy concerns. We present secure risk propagation, a novel efficient algorithm for money laundering detection across banks without violating privacy concerns. In this algorithm, each account is assigned a risk score, which is then propagated through the transaction network. In this article we present two results. Firstly, using data from a large Dutch bank, we show that it is possible to detect unusual activity using this model, with cash ratio as the risk score. With a recall of 20%, the precision improves from 15% to 40% by propagating the risk scores, reducing the number of false positives significantly. Secondly, we present a privacy-preserving solution for securely performing risk propagation over a joint, inter-bank transaction network. To achieve this, we use Secure Multi-Party Computation (MPC) techniques, which are particularly well-suited for the risk propagation algorithm due to its structural simplicity. We also show that the running time of this secure variant scales linearly in the amount of accounts and transactions. For 200, 000 transactions, two iterations of the secure algorithm between three virtual parties, run within three hours on a consumer-grade server.

Note: Added acknowledements.

Available format(s)
Publication info
Published elsewhere. Minor revision. Financial Cryptography and Data Security 2024
financial crimemoney launderingmulti-party computationprivacyrisk propagation
Contact author(s)
marie_beth vanegmond @ tno nl
vincent dunning @ tno nl
2024-05-30: revised
2024-01-16: received
See all versions
Short URL
Creative Commons Attribution


      author = {Marie Beth van Egmond and Vincent Dunning and Stefan van den Berg and Thomas Rooijakkers and Alex Sangers and Ton Poppe and Jan Veldsink},
      title = {Privacy-preserving Anti-Money Laundering using Secure Multi-Party Computation},
      howpublished = {Cryptology ePrint Archive, Paper 2024/065},
      year = {2024},
      note = {\url{}},
      url = {}
Note: In order to protect the privacy of readers, does not use cookies or embedded third party content.