A Novel Spatiotemporal Prediction Approach Based on Graph Convolution Neural Networks and Long Short-Term Memory for Money Laundering Fraud

Money laundering is an act of criminals attempting to cover up the nature and source of their illegal gains. Large-scale money laundering has a great harm to a country's economy, political order and even social stability. Therefore, it is essential to predict the risk of money laundering scientifically and reasonably. Money laundering data have complex temporal dependency. Historical transactions have an impact on current transactions. Different transactions also have complex spatial correlation. For this very reason, a hybrid spatiotemporal money laundering prediction model based on graph convolution neural networks (GCN) and long short-term memory (LSTM), abbreviated MGC-LSTM, is proposed to learn the dependency between different money laundering transactions. Firstly, LSTM is employed to obtain the temporal dependence of money laundering data set at different times; secondly, GCN is wielded to learn the complex spatial dependency of different money laundering transactions. Historical observations on different transactions, temporal and transactions features are defined as graph signals. For each time stamp, the results trained by LSTM are served as the input of GCN; finally, we compare the MGC-LSTM with other state-of-the-art algorithms to evaluate the performance of the proposed method. The experimental results demonstrate that MGC-LSTM outperforms other comparing algorithms with respect to effectiveness and significance.

Automated summary

Money laundering is a criminal act that seeks to conceal the illegal gains of criminals, leading to significant harm to a country's economy, political order, and social stability. To predict money laundering risks effectively, a hybrid spatiotemporal prediction model MGC-LSTM is proposed, utilizing LSTM and GCN to learn the dependency between different money laundering transactions, achieving better performance compared to other state-of-the-art algorithms.