GAMLNet: a graph based framework for the detection of money laundering
Informazioni aggiuntive
Autori
Schmidt J. .,
Pasadakis D.,
Sathe M.,
Schenk O.
Tipo
Contributo in atti di conferenza
Anno
2024
Lingua
Inglese
Abstract
The accuracy of classification algorithms in detecting fraudulent financial activity is critical in assisting human analysts in the task of preventing financial crime. We consider financial transactions in the form of a directed graph, and propose a Graph Neural Network (GNN) model for the identification of money laundering activity. Our method generates a set of structurally aware and statistically significant features for each graph node, and utilizes them as input to the GNN classifier, that comprises of the combination of the layers of two recently proposed message passing architectures. The effectiveness of our approach is demonstrated in experiments with synthetic data that simulate real-world behaviour, and are infused with seven anomalous money laundering topologies. The accuracy of our method is consistently higher than that of other GNNs and tree-based classification methods over datasets of increasing size and increasing
imbalance between the fraudulent and benign classes.
Atti di conferenza
IEEE SDS24
Mese
giugno
Editore
IEEE
Pagina inizio
1
Pagina fine
10
Nome conferenza
IEEE Swiss Conference on Data Science (SDS)
Luogo conferenza
The Circle Convention Center, Zurich Airport
Data conferenza
May 30 – 31, 2024
Curatore
IEEE
Parole chiave
Anomaly detection, anti-money laundering, Graph Neural Networks
