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.

Metonymy is a creative process that establishes relationships based on contiguity or semantic relatedness between concepts. We outline a mechanism for deriving new concepts from WordNet using metonymy. We argue that by exploiting polysemy in WordNet we can take advantage of the metonymic relations between concepts. The focus of our metonymy generation work has been the creation of noun­ noun compounds that do not already exist in WordNet and which can be profitably added to WordNet. The mechanism of metonymy generation we outline takes a source compound and creates new compounds by exploiting the polysemy associated with hyponyms of the head of the source compound. We argue that metonymy generation is a sound basis for concept creation as the newly created compounds are semantically related to the source concept. We demonstrate that metonymy generation based on polysemy is superior to a method of metonymy generation that ignores polysemy. These new concepts can be used to augment WordNet.