NextGen AML: distributed deep learning based language technologies to augment anti money laundering Investigation
| Autor: | Edward Burgin, Dadong Wan, Jingguang Han, Jeremiah Hayes, Utsab Barman, Jinhua Du |
|---|---|
| Přispěvatelé: | Liu, Fei, Solorio, Thamar |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Deep learning 05 social sciences Sentiment analysis 02 engineering and technology Money laundering Machine learning computer.software_genre Relationship extraction Entity linking 0502 economics and business 0202 electrical engineering electronic engineering information engineering False positive paradox 020201 artificial intelligence & image processing Artificial intelligence 050207 economics business computer Machine translating Link analysis |
| Zdroj: | Han, Jingguang, Barman, Utsab, Hayes, Jer, Du, Jinhua ORCID: 0000-0002-3267-4881 ACL (4) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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