dc.contributor.author | Alshantti, Abdallah Anis Sameer | |
dc.contributor.author | Rasheed, Adil | |
dc.date.accessioned | 2022-05-06T11:50:55Z | |
dc.date.available | 2022-05-06T11:50:55Z | |
dc.date.created | 2021-10-28T00:11:45Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Frontiers in Artificial Intelligence. 2021, 4, 761925 . | en_US |
dc.identifier.issn | 2624-8212 | |
dc.identifier.uri | https://hdl.handle.net/11250/2994567 | |
dc.description.abstract | There has been an emerging interest by financial institutions to develop advanced systems that can help enhance their anti-money laundering (AML) programmes. In this study, we present a self-organising map (SOM) based approach to predict which bank accounts are possibly involved in money laundering cases, given their financial transaction histories. Our method takes advantage of the competitive and adaptive properties of SOM to represent the accounts in a lower-dimensional space. Subsequently, categorising the SOM and the accounts into money laundering risk levels and proposing investigative strategies enables us to measure the classification performance. Our results indicate that our framework is well capable of identifying suspicious accounts already investigated by our partner bank, using both proposed investigation strategies. We further validate our model by analysing the performance when modifying different parameters in our dataset. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Frontiers | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.subject | Self-organising map (SOM) | en_US |
dc.subject | Money laundering | en_US |
dc.subject | Suspicious accounts | en_US |
dc.subject | Risk levels | en_US |
dc.subject | Investigation strategies | en_US |
dc.title | Self-organising map based framework for investigating accounts suspected of money laundering | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © 2021 Alshantti and Rasheed. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. | en_US |
dc.source.pagenumber | 15 | en_US |
dc.source.volume | 4 | en_US |
dc.source.journal | Frontiers in Artificial Intelligence | en_US |
dc.identifier.doi | 10.3389/frai.2021.761925 | |
dc.identifier.cristin | 1949119 | |
dc.source.articlenumber | 761925 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |