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NextGen AML: distributed deep learning based language technologies to augment anti money laundering Investigation

Han, Jingguang, Barman, Utsab, Hayes, Jer, Du, Jinhua orcid logoORCID: 0000-0002-3267-4881, Burgin, Edward and Wan, Dadong (2018) NextGen AML: distributed deep learning based language technologies to augment anti money laundering Investigation. In: 56th Annual Meeting of the Association for Computational Linguistics-System Demonstrations, 15-20 July 201, Melbourne, Australia.

Most of the current anti money laundering (AML) systems, using handcrafted rules, are heavily reliant on existing structured databases, which are not capable of effectively and efficiently identifying hidden and complex ML activities, especially those with dynamic and timevarying characteristics, resulting in a high percentage of false positives. Therefore, analysts1 are engaged for further investigation which significantly increases human capital cost and processing time. To alleviate these issues, this paper presents a novel framework for the next generation AML by applying and visualizing deep learning-driven natural language processing (NLP) technologies in a distributed and scalable manner to augment AML monitoring and investigation. The proposed distributed framework performs news and tweet sentiment analysis, entity recognition, relation extraction, entity linking and link analysis on different data sources (e.g. news articles and tweets) to provide additional evidence to human investigators for final decisionmaking. Each NLP module is evaluated on a task-specific data set, and the overall experiments are performed on synthetic and real-world datasets. Feedback from AML practitioners suggests that our system can reduce approximately 30% time and cost compared to their previous manual approaches of AML investigation.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:UNSPECIFIED
Published in: Liu, Fei and Solorio, Thamar, (eds.) Proceedings of ACL 2018, System Demonstrations. . Association for Computational Linguistics.
Publisher:Association for Computational Linguistics
Official URL:https://www.aclweb.org/anthology/P18-4007
Copyright Information:© 2018 Association for Computational Linguistics
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Accenture Labs Dublin, Enterprise Ireland under the Research Programme (Grant EI.IP20170626), Science Foundation Ireland (SFI) Industry Fellowship Programme 2016 (Grant 16/IFB/4490).
ID Code:23358
Deposited On:24 May 2019 15:12 by Thomas Murtagh . Last Modified 24 May 2019 15:12

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