{"title":"Ehi’s Predictions for the Anti-Money Laundering Sector in 2021","authors":"E. Esoimeme","doi":"10.2139/ssrn.3752074","DOIUrl":null,"url":null,"abstract":"The year 2020 witnessed the introduction of new rules and regulations by the United Kingdom for virtual asset service providers, and for the first time, a Bitcoin “Mixer” was Penalized by the United States Department of the Treasury’s Financial Crimes Enforcement Network (FinCEN) for Violating Anti-Money Laundering Laws. The anti-money laundering landscape also witnessed a shift from documentation based procedures to electronic evidence. This was as a result of the COVID-19 pandemic. The year 2021 will likely continue from where the year 2020 stopped with more regulations and enforcement actions. \n \nThis paper provides an in-depth review and analysis of the topics that are likely to trend on the anti-money laundering space in 2021. This paper predicts that countries around the world will introduce more rules and regulations for virtual asset service providers along with the adoption of an appropriate mix of on-site and off-site supervision of dealers in precious metals, precious stones or jewels, including the introduction of corporate transparency and register reforms. This paper also predicts that financial institutions will enhance their anti-money laundering systems and controls to accommodate application programming interfaces during the customer onboarding process, and upgrade their anti-money laundering surveillance monitoring systems to a new system that can detect highly suspicious transaction patterns including possible layering schemes, transactions not commensurate with the business’s purpose, and commingling of funds between two independent check cashing entities. \n \nThis paper concludes that the combined power of robotic process automation, machine learning technology and human intelligence will help to deter and detect money laundering.","PeriodicalId":301526,"journal":{"name":"Sociology of Innovation eJournal","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sociology of Innovation eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3752074","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The year 2020 witnessed the introduction of new rules and regulations by the United Kingdom for virtual asset service providers, and for the first time, a Bitcoin “Mixer” was Penalized by the United States Department of the Treasury’s Financial Crimes Enforcement Network (FinCEN) for Violating Anti-Money Laundering Laws. The anti-money laundering landscape also witnessed a shift from documentation based procedures to electronic evidence. This was as a result of the COVID-19 pandemic. The year 2021 will likely continue from where the year 2020 stopped with more regulations and enforcement actions.
This paper provides an in-depth review and analysis of the topics that are likely to trend on the anti-money laundering space in 2021. This paper predicts that countries around the world will introduce more rules and regulations for virtual asset service providers along with the adoption of an appropriate mix of on-site and off-site supervision of dealers in precious metals, precious stones or jewels, including the introduction of corporate transparency and register reforms. This paper also predicts that financial institutions will enhance their anti-money laundering systems and controls to accommodate application programming interfaces during the customer onboarding process, and upgrade their anti-money laundering surveillance monitoring systems to a new system that can detect highly suspicious transaction patterns including possible layering schemes, transactions not commensurate with the business’s purpose, and commingling of funds between two independent check cashing entities.
This paper concludes that the combined power of robotic process automation, machine learning technology and human intelligence will help to deter and detect money laundering.