This paper surveys the existing academic literature on artificial intelligence (AI) technologies for anti-money laundering (AML). We review the state-of-the-art AI methods for AML and extend the discussion by proposing a framework that utilizes advanced natural language processing and deep-learning techniques to facilitate next-generation AML technologies. Our framework utilizes unstructured external information to assist domain experts, aiming to decrease the workload for the human

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... . We bridge the gap between the current AML methods and state-of-the art AI, highlighting new trends and directions in AI that can be used to develop the AML pipeline into a robust, scalable solution with a reduced false positive rate and high adaptability.