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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