{"title":"Applying Big Data Technologies to Detect Cases of Money Laundering and Counter Financing of Terrorism","authors":"Kirill V. Plaksiy, A. Nikiforov, N. Miloslavskaya","doi":"10.1109/W-FICLOUD.2018.00017","DOIUrl":null,"url":null,"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.","PeriodicalId":218683,"journal":{"name":"2018 6th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 6th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/W-FICLOUD.2018.00017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.