标题： 使用社会网络分析来防止洗钱 显示英文标题

标题： Using social network analysis to prevent money laundering

摘要： 这项研究探讨了将网络分析技术应用于防止洗钱的机会。 我们通过对一家主要在意大利运营的保理公司的中央数据库进行分析，使用了现实世界的数据，时间跨度为19个月。 该数据库包含与保理业务相关的财务操作，以及关于公司客户的其他有用信息。 我们提出了一种新的方法来排序和映射关系数据，并展示了基于网络度量的预测模型，以评估参与保理业务的客户的风险档案。 我们发现，可以利用社会网络度量来预测风险档案。 在我们的数据集中，最危险的社会行为者涉及更大或更频繁的金融操作；他们在交易网络中更加边缘化；他们跨不同经济部门中介交易，并在风险更高的国家或意大利地区运作。 最后，为了识别潜在的犯罪集团，我们提出了对具有相同所有者或代表的不同公司之间存在的隐性联系进行可视化分析。 我们的研究结果表明，在寻找可疑的金融操作和潜在罪犯时，使用基于网络的方法非常重要。 显示英文摘要

摘要： This research explores the opportunities for the application of network analytic techniques to prevent money laundering. We worked on real world data by analyzing the central database of a factoring company, mainly operating in Italy, over a period of 19 months. This database contained the financial operations linked to the factoring business, together with other useful information about the company clients. We propose a new approach to sort and map relational data and present predictive models, based on network metrics, to assess risk profiles of clients involved in the factoring business. We find that risk profiles can be predicted by using social network metrics. In our dataset, the most dangerous social actors deal with bigger or more frequent financial operations; they are more peripheral in the transactions network; they mediate transactions across different economic sectors and operate in riskier countries or Italian regions. Finally, to spot potential clusters of criminals, we propose a visual analysis of the tacit links existing among different companies who share the same owner or representative. Our findings show the importance of using a network-based approach when looking for suspicious financial operations and potential criminals.