NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation

Jingguang Han, Utsab Barman, Jeremiah Hayes, Jinhua Du, Edward Burgin, Dadong Wan


Abstract
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 time-varying characteristics, resulting in a high percentage of false positives. Therefore, analysts 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 decision-making. 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.
Anthology ID:
P18-4007
Volume:
Proceedings of ACL 2018, System Demonstrations
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
37–42
Language:
URL:
https://aclanthology.org/P18-4007
DOI:
10.18653/v1/P18-4007
Bibkey:
Cite (ACL):
Jingguang Han, Utsab Barman, Jeremiah Hayes, Jinhua Du, Edward Burgin, and Dadong Wan. 2018. NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation. In Proceedings of ACL 2018, System Demonstrations, pages 37–42, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation (Han et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-4007.pdf