Applying Big Data Technologies to Detect Cases of Money Laundering and Counter Financing of Terrorism

Kirill V. Plaksiy, A. Nikiforov, N. Miloslavskaya
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引用次数: 15

Abstract

The paper suggests a technique that allows to automate schemes that generates new criminal cases for money laundering and counter financing of terrorism (ML/CFT), which are based on ML/CFT typologies but do not appear as their exact copies. This feature hinders an automated system from making a decision about their exact coincidence or its absence while comparing case objects and links among them and links in ML/CFT typologies. Possibilities and advantages of application of Big Data for financial investigation data analysis and processing are also explored. The visualization of ML/CFT typologies with the use of graphs is considered. The article proposes a technique for generating variants of typologies (for example, "Peso" typology, "commission scheme") based on cases built on typologies. A program for implementation and verification of this technique was written and successfully tested on case graphs built on typologies.
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该论文提出了一种技术,该技术允许自动生成新的洗钱和反恐怖主义融资(ML/CFT)刑事案件的方案,这些方案基于ML/CFT类型,但不显示为其精确副本。在比较case对象和它们之间的链接以及ML/CFT类型中的链接时,该特性阻碍了自动化系统对它们的精确重合或缺失做出决定。探讨了大数据在金融调查数据分析处理中的应用可能性和优势。考虑了使用图来可视化ML/CFT类型。本文提出了一种基于基于类型学的案例生成类型学变体的技术(例如,“Peso”类型学,“commission scheme”)。编写了用于实现和验证该技术的程序,并成功地在基于类型学的案例图上进行了测试。
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