引力和智能数据分析,以评估金融机构的洗钱风险

S. Lyeonov, Joanna Żurakowska-Sawa, O. Kuzmenko, V. Koibichuk
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引用次数: 15

摘要

利用公司进行洗钱的各种各样的计划,如石油走私、非法天然气销售、挪用中央银行再融资、挪用银行资金和国有企业,构成了研究问题。所研究的样本包括全球102个国家,这些国家受到金融行动特别工作组(FATF)的密切监测,它们的社会政治和经济发展水平各不相同。利用金融机构洗钱来评估金融监控风险的科学方法是基于多维静态分析、描述性、聚类和分散数据分析、重力理论、非线性计量经济建模、动态非线性系统的微分和分岔分析等方法。该研究的结果是一个发达的国家金融机构洗钱综合风险评估模型,该模型考虑了按国家分组的情况。收稿日期:2020年2月1日修订日期:2020年10月接受日期:2020年12月DOI:10.14254/20718330.2020/13-4/18《国际研究》S ci entifi P a pe rs©国际研究基金会,2020©CSR, 2020《国际研究》第13卷,2020年第4期260洗钱风险等级,归属于国家的集群识别;形成综合指标作为洗钱风险评级评估,并基于引力模型进行风险评估;基于非线性计量经济模型构建了各国金融机构风险动态系统的阶段画像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gravitational and intellectual data analysis to assess the money laundering risk of financial institutions
The wide variety of schemes to use companies for money laundering, such as oil smuggling, illegal gas sales, misappropriation of the Central Bank refinancing, misappropriation of bank funds and state-owned enterprises, form the research issues. The sample under study includes 102 countries around the world, which are closely monitored by the Financial Action Task Force (FATF) and have different levels of sociopolitical and economic development. The scientific and methodological approach to assess the financial monitoring risk in terms of the use of financial institutions for money laundering is based on the methods of multidimensional static analysis, descriptive, cluster and dispersive data analysis, gravity theory, nonlinear econometric modeling, differential and bifurcation analysis of dynamic nonlinear systems. The result of the study is a developed model of comprehensive risk assessment for the countries’ financial institutions for money laundering, which considers grouping of countries by the Received: February, 2020 1st Revision: October, 2020 Accepted: December, 2020 DOI: 10.14254/20718330.2020/13-4/18 Journal of International Studies S ci en ti fi c P a pe rs © Foundation of International Studies, 2020 © CSR, 2020 Journal of International Studies Vol.13, No.4, 2020 260 level of money laundering risk, identification of the cluster belonging to the state; formation of an integrated index as a money laundering risk rating assessment, and risk assessment based on the gravitational model; construction of a phase portrait for a dynamic system of the risk to use the countries’ financial institutions based on a nonlinear econometric model.
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