Intelligent Anti-Money Laundering Solution Based upon Novel Community Detection in Massive Transaction Networks on Spark

Authors:
  • XLXurui Li
    Xurui Li
  • XCXiang Cao
    Xiang Cao
  • XQXuetao Qiu
    Xuetao Qiu
  • JZJintao Zhao
    Jintao Zhao
  • JZJianbin Zheng
    Jianbin Zheng
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Abstract
Criminals are using every means available to launder the profits from their illegal activities into ostensibly "legitimate" assets. Meanwhile, most commercial anti-money laundering systems are still rule-based, which cannot adapt to the ever-changing tricks. Although some machine learning methods have been proposed, they are mainly focused on the perspective of abnormal behavior for single accounts. Considering money laundering activities are often involved in gang criminals, these methods are still not intelligent enough to crack down on criminal gangs all-sidedly. In this paper, a systematic solution is presented to find suspicious money laundering gangs. A temporal-directed Louvain algorithm has been proposed to detect communities according to relevant anti-money laundering patterns. All processes are implemented and optimized on Spark platform. This solution can greatly improve the efficiency of anti-money laundering work for financial regulation agencies.

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