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.

洗钱是犯罪分子企图掩盖其违法所得的性质和来源的行为。大规模洗钱对一个国家的经济、政治秩序甚至社会稳定都有很大的危害。因此，科学合理地预测洗钱风险至关重要。洗钱数据具有复杂的时间依赖性。历史交易对当前交易有影响。不同的交易也具有复杂的空间相关性。正是出于这个原因，提出了一种基于图卷积神经网络 (GCN) 和长短期记忆 (LSTM) 的混合时空洗钱预测模型，缩写为 MGC-LSTM，用于学习不同洗钱交易之间的依赖关系。首先，采用LSTM获取不同时间洗钱数据集的时间依赖性；其次，利用 GCN 来学习不同洗钱交易的复杂空间依赖性。对不同交易、时间和交易特征的历史观察被定义为图信号。对于每个时间戳，LSTM 训练的结果作为 GCN 的输入；最后，我们将 MGC-LSTM 与其他最先进的算法进行比较，以评估所提出方法的性能。实验结果表明，MGC-LSTM 在有效性和重要性方面优于其他比较算法。时间和交易特征被定义为图信号。对于每个时间戳，LSTM 训练的结果作为 GCN 的输入；最后，我们将 MGC-LSTM 与其他最先进的算法进行比较，以评估所提出方法的性能。实验结果表明，MGC-LSTM 在有效性和重要性方面优于其他比较算法。时间和交易特征被定义为图信号。对于每个时间戳，LSTM 训练的结果作为 GCN 的输入；最后，我们将 MGC-LSTM 与其他最先进的算法进行比较，以评估所提出方法的性能。实验结果表明，MGC-LSTM 在有效性和重要性方面优于其他比较算法。