基于时序有向社区检测，高效识别洗钱犯罪团伙。
Intelligent Anti-Money Laundering Solution Based upon Novel Community Detection in Massive Transaction Networks on Spark
- 提出时序有向Louvain算法，挖掘交易网络中的犯罪团伙模式。
- 在Spark上实现全流程优化，显著提升大规模交易分析效率。
- 适合金融监管机构用于打击组织化洗钱行为。
犯罪分子正利用各种手段将非法所得转化为看似合法的资产。目前大多数商业反洗钱系统仍依赖规则，难以应对不断变化的欺诈手法。尽管已有部分机器学习方法被提出，但主要关注单个账户的异常行为，无法全面识别涉及犯罪集团的洗钱活动。本文提出一种系统性解决方案，用于发现可疑的洗钱犯罪团伙。通过引入时序有向Louvain算法，依据反洗钱模式检测社区结构，并在Spark平台上实现与优化全部流程。该方案可显著提升金融监管机构开展反洗钱工作的效率。
原文摘要 · Abstract (English)
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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