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Dedicated Algorithms for Risk Minimization in Suspicious Financial Transactions

机译:可疑金融交易中风险最小化的专用算法

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Under EU Directive 2015/849 of the European Parliament and of the Council on the prevention of the use of the financial system for the purpose of money laundering or terrorist financing, it is necessary to identify both individuals and transactions of a certain degree of risk. The process of identifying the risk of both customers and transactions considered suspicious lies at the root of systems aimed at preventing money laundering and terrorist financing. Such systems are called AML (Anti-Money Laundering) systems. An important step in calculating a client's risk is to check his / her existence in lists of suspicious or possibly suspicious persons, also called sanction lists. Classic search methods involve large processing capabilities. Taking into account the obligation of all financial institutions to implement these methods, there is a need to implement a fast and secure search flow. Therefore, the attention was drawn to the searching techniques for artificial intelligence. These kinds of methods include advanced machine learning for optimizing the whole searching process: the system is able to detect certain patterns and identify new ones based on certain characteristics of the search query and by identifying similarities between words.
机译:根据欧洲议会的欧盟指令2015/849和理事会预防金融体系的使用,以便洗钱或恐怖主义融资,有必要确定一定程度的风险的个人和交易。确定客户和交易风险的过程被认为是可疑的,旨在防止洗钱和恐怖主义融资的系统根源。这些系统称为AML(反洗钱)系统。计算客户的风险的一个重要步骤是检查他/她的存在于可疑或可能可疑人士的列表中,也称为制裁清单。经典搜索方法涉及大的处理能力。考虑到所有金融机构实施这些方法的义务,需要实现快速安全的搜索流程。因此,对人工智能的搜索技术绘制了注意力。这些方法包括高级机器学习,用于优化整个搜索过程:系统能够根据搜索查询的某些特征来检测某些模式并识别新的模式,并通过识别单词之间的相似性。

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