FlowScope: Spotting Money Laundering Based on Graphs


  • Xiangfeng Li Beijing University of Post and Telecommunication
  • Shenghua Liu Institute of Computing Technology, Chinese Academy of Sciences
  • Zifeng Li University of Surrey
  • Xiaotian Han Texas A&M University
  • Chuan Shi Beijing University of Post and Telecommunication
  • Bryan Hooi School of Computer Science, National University of Singapore
  • He Huang China Citic Bank
  • Xueqi Cheng Chinese Academy of Sciences




Given a graph of the money transfers between accounts of a bank, how can we detect money laundering? Money laundering refers to criminals using the bank's services to move massive amounts of illegal money to untraceable destination accounts, in order to inject their illegal money into the legitimate financial system. Existing graph fraud detection approaches focus on dense subgraph detection, without considering the fact that money laundering involves high-volume flows of funds through chains of bank accounts, thereby decreasing their detection accuracy. Instead, we propose to model the transactions using a multipartite graph, and detect the complete flow of money from source to destination using a scalable algorithm, FlowScope. Theoretical analysis shows that FlowScope provides guarantees in terms of the amount of money that fraudsters can transfer without being detected. FlowScope outperforms state-of-the-art baselines in accurately detecting the accounts involved in money laundering, in both injected and real-world data settings.




How to Cite

Li, X., Liu, S., Li, Z., Han, X., Shi, C., Hooi, B., Huang, H., & Cheng, X. (2020). FlowScope: Spotting Money Laundering Based on Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 4731-4738. https://doi.org/10.1609/aaai.v34i04.5906



AAAI Technical Track: Machine Learning