Intelligent Anti-Money Laundering Fraud Control Using Graph-Based Machine Learning Model for the Financial Domain
| Autor: | Atif Usman, Nasir Naveed, Saima Munawar |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Journal of Cases on Information Technology. 25:1-20 |
| ISSN: | 1548-7725 1548-7717 |
| DOI: | 10.4018/jcit.316665 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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