GAMLNet: a graph based framework for the detection of money laundering
Informazioni aggiuntive
Autori
Schmidt J.,
Pasadakis D.,
Sathe M.,
Schenk O.,
IEEE
Tipo
Contributo in atti di convegno
Anno
2024
Lingua
Inglese
Sommario
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.
Parole chiave
Accuracy, Anomaly detection, anti-money laundering, Classification algorithms, Data science, Directed graphs, Graph Neural Networks, Message passing, Topology
Titolo atti di convegno
IEEE SDS24
Number ( Month )
June
Editore
IEEE
Nome convegno
IEEE Swiss Conference on Data Science (SDS)
Luogo convegno
The Circle Convention Center, Zurich Airport
Data convegno
May 30 – 31, 2024
Pagine (o numero dell’articolo)
241- 245
