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Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review


Money laundering has been affecting the global economy for many years. Large sums of money are laundered every year, posing a threat to the global economy and its security. Money laundering encompasses illegal activities that are used to make illegally acquired funds appear legal and legitimate. This paper aims to provide a comprehensive survey of machine learning algorithms and methods applied to detect suspicious transactions. In particular, solutions of anti-money laundering typologies, link analysis, behavioural modelling, risk scoring, anomaly detection, and geographic capability have been identified and analysed. Key steps of data preparation, data transformation, and data analytics techniques have been discussed; existing machine learning algorithms and methods described in the literature have been categorised, summarised, and compared. Finally, what techniques were lacking or under-addressed in the existing research has been elaborated with the purpose of pinpointing future research directions.

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This work was supported by a 3rd Called Collaboration with Public Universities and Agencies grant from the University of Nottingham, Malaysia Campus with Project No. UNHT0001.

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Correspondence to Zhiyuan Chen.

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Chen, Z., Van Khoa, L.D., Teoh, E.N. et al. Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review. Knowl Inf Syst 57, 245–285 (2018).

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  • Anti-money laundering
  • Data mining methods and algorithms
  • Supervised learning
  • Unsupervised learning
  • Anti-money laundering typologies
  • Link analysis
  • Behavioural modelling
  • Risk scoring
  • Anomaly detection
  • Geographic capability