Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review
| Autor: | Ettikan Kandasamy Karuppiah, Zhiyuan Chen, Le Dinh Van Khoa, Ee Na Teoh, Kim Sim Lam, Amril Nazir |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Data transformation Supervised learning 02 engineering and technology Machine learning computer.software_genre Money laundering Human-Computer Interaction Artificial Intelligence Hardware and Architecture 020204 information systems 0202 electrical engineering electronic engineering information engineering Key (cryptography) Unsupervised learning 020201 artificial intelligence & image processing Anomaly detection Artificial intelligence business computer Database transaction Software Information Systems Link analysis |
| Zdroj: | Knowledge and Information Systems. 57:245-285 |
| ISSN: | 0219-3116 0219-1377 |
| DOI: | 10.1007/s10115-017-1144-z |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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