| Title: |
LAS-GNN: A Graph Neural Network for Temporal Money Laundering Motif Detection |
| Authors: |
Verlaan, Stan; HulpuÈ™, Ioana; Van Leeuwen, Erik Jan; Sub Data Intensive Systems; Sub Algorithms and Complexity |
| Publication Year: |
2025 |
| Subject Terms: |
anti-money laundering; financial networks; graph neural networks; temporal motif detection; Artificial Intelligence; Finance |
| Description: |
We enhance Graph Neural Networks (GNNs) for identifying suspicious accounts involved in money laundering patterns. Extending the work of Egressy et al. (AAAI 2024), we propose a novel GNN architecture to detect suspicious subgraph motifs in the weighted temporal networks underlying financial data. Our architecture allows for the indication of edge directionality within a single Aggregator function, element-wise edge weight multiplication, and an LSTM aggregator that can learn from the sequential order of edges imposed by timestamps. The resulting model, LAS-GNN, is based on an inductive learning framework and can generalize across different networks. Experimental results on synthetic networks show that LAS-GNN is robust and can identify basic money laundering motifs to near perfection, outperforming a graph isomorphism network benchmark with edge features. |
| Document Type: |
book part |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://dspace.library.uu.nl/handle/1874/483299 |
| Availability: |
https://dspace.library.uu.nl/handle/1874/483299 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.B0165FCE |
| Database: |
BASE |