Delator: Automatic Detection of Money Laundering Evidence on Transaction Graphs via Neural Networks

• URL: http://arxiv.org/abs/2205.10293v1
• Date: Fri, 20 May 2022 16:44:58 GMT
• Title: Delator: Automatic Detection of Money Laundering Evidence on Transaction Graphs via Neural Networks
• Authors: Henrique S. Assump\c{c}\~ao, Fabr\'icio Souza, Leandro Lacerda Campos, Vin\'icius T. de Castro Pires, Paulo M. Laurentys de Almeida, Fabricio Murai
• Abstract summary: We propose DELATOR, a new CAAT (computer-assisted audit technology) to detect money laundering activities. In collaboration with a Brazilian bank, we design and apply an evaluation strategy to quantify DELATOR's performance on historic data comprising millions of clients.
• Score: 1.904940310103857
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Money laundering is one of the most relevant criminal activities today, due to its potential to cause massive financial losses to governments, banks, etc. We propose DELATOR, a new CAAT (computer-assisted audit technology) to detect money laundering activities based on neural network models that encode bank transfers as a large-scale temporal graph. In collaboration with a Brazilian bank, we design and apply an evaluation strategy to quantify DELATOR's performance on historic data comprising millions of clients. DELATOR outperforms an off-the-shelf solution from Amazon AWS by 18.9% with respect to AUC. We conducted real experiments that led to discovery of 8 new suspicious among 100 analyzed cases, which would have been reported to the authorities under the current criteria.

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