Research Handbook on Electronic Commerce Law

Rothchild, John

doi:10.4337/9781783479924

Cited by 6 publications

(1 citation statement)

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“…Standard Chartered (Kittrie, 2016) was fined $340 million once found guilty of money laundering and facilitating illegal transactions (Kossovsky, Greenberg, &Brandegee, 2012). Liberty reserve bank was closed by US Federal authorities once it proved involved in $6 billion (Rothchild, 2016) laundering.…”

Section: Anti-money Launderingmentioning

confidence: 99%

Intelligent Anti-Money Laundering Fraud Control Using Graph-Based Machine Learning Model for the Financial Domain

Usman

Naveed

Munawar

2023

Journal of Cases on Information Technology

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Financial domains are suffering from organized fraudulent activities that are inflicting the world on a larger scale. Basel Anti-Money Laundering (AML) index enlists 146 countries, which are impacted by criminal acts like money laundering, and represents the country's risk level with a notable deteriorating trend over the last five years. Despite AML being a substantially focused area, only a fraction of such activities has been prevented. Because financial data related to this field is concealed, access is limited and protected by regulatory authorities. This paper aims to study a graph-based machine-learning model to identify fraudulent transactions using the financial domain's synthetic dataset (100K nodes, 5.3M edges). Graph-based machine learning with financial datasets resulted in promising 77-79% accuracy with a limited feature set. Even better results can be achieved by enriching the feature vector. This exploration further leads to pattern detection in the graph, which is a step toward AML detection.

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Section: Anti-money Launderingmentioning

confidence: 99%

Intelligent Anti-Money Laundering Fraud Control Using Graph-Based Machine Learning Model for the Financial Domain

Usman

Naveed

Munawar

2023

Journal of Cases on Information Technology

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ODR Procedural Standards

Zheng

2020

Online Resolution of E-Commerce Disputes

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Griffiths

2017

Legal Information Management

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Steve Mishkin had a twenty year teaching career working as Head of History and Head of Year before deciding to move into a new occupation and becoming an information professional. He studied MSc Information Science at City University and gained his first professional role at Shearman and Sterling as part of their internship programme. Following graduation, he worked as an Information Officer at Norton Rose Fulbright. Steve currently works at Allen & Overy, in the International Capital Markets Library, embedded within the ICM Know How and Training Team.

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Research Handbook on Electronic Commerce Law

Abstract: ISBN 978 1 78347 991 7 (cased) ISBN 978 1 78347 992 4 (eBook)

Cited by 6 publications

References 0 publications

Intelligent Anti-Money Laundering Fraud Control Using Graph-Based Machine Learning Model for the Financial Domain

Intelligent Anti-Money Laundering Fraud Control Using Graph-Based Machine Learning Model for the Financial Domain

ODR Procedural Standards

Current Awareness

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