Link analysis

Analytics

Money laundering

Computer security

Anomaly detection

Data mining

Computer science

Business

Transaction data

Database

Data science

Artificial intelligence

Database transaction

Finance

Machine learning

Key (lock)

Knowledge and Information Systems

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.

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