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

Chen, Zhiyuan; Le, Dan; Teoh, Ee Na; Nazir, Amril; Karuppiah, Ettikan Kandasamy; Lam, Kim Sim

doi:10.1007/s10115-017-1144-z

Cited by 156 publications

(135 citation statements)

References 59 publications

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“…Fraudulent Activities is one of the major sources of operational risk for companies, particularly those in the finance sector. It can take on many forms: for example, bankactivities related fraud (fraudulent credit card transactions [19], [169], money laundering activities [170]), insurancerelated fraud (fraudulent insurance claims [29]), securities and commodities fraud or other frauds such as massmarketing fraud or corporate fraud [171]. Financial fraud can be economically devastating for a business [172].…”

Section: Managing Operational Riskmentioning

confidence: 99%

Machine Learning for Financial Risk Management: A Survey

et al. 2020

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Financial risk management avoids losses and maximizes profits, and hence is vital to most businesses. As the task relies heavily on information-driven decision making, machine learning is a promising source for new methods and technologies. In recent years, we have seen increasing adoption of machine learning methods for various risk management tasks. Machine-learning researchers, however, often struggle to navigate the vast and complex domain knowledge and the fast-evolving literature. This paper fills this gap, by providing a systematic survey of the rapidly growing literature of machine learning research for financial risk management. The contributions of the paper are four-folds: First, we present a taxonomy of financial-risk-management tasks and connect them with relevant machine learning methods. Secondly, we highlight significant publications in the past decade. Thirdly, we identify major challenges being faced by researchers in this area. And finally, we point out emerging trends and promising research directions.

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Section: Managing Operational Riskmentioning

confidence: 99%

Machine Learning for Financial Risk Management: A Survey

et al. 2020

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“…A survey of the machine learning algorithms and methods applied to detect suspicious transactions focusing on machine learning was conducted by Reference [10], where solutions like anti-money laundering typologies, link analysis, behavior modeling, risk score, anomaly detection and geographical capability were identified and analyzed.…”

Section: Background and Related Workmentioning

confidence: 99%

Application of Technological Solutions in the Fight Against Money Laundering—A Systematic Literature Review

2019

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With the growing interest in technological solutions aimed at combating money laundering, several studies involving the application of technology have been carried out. However, there were no records of studies aimed at identifying, selecting, rigorously analyzing and synthesizing the literature on solutions that adopt technology to combat money laundering. This paper presents a systematic review of the literature on the application of technological solutions in the fight against money laundering. Seventy-one papers were selected from the 795 studies initially retrieved for data extraction, analysis and synthesis based on predefined inclusion and exclusion criteria. The results obtained with the data analysis made it possible to identify a general categorization of the domains of application of the approaches, as well as a mapping and classification of the support mechanisms adopted. The findings of this review showed that, among the application domain categories identified, the detection of suspicious transactions attracted greater attention from researchers. Regarding the support mechanisms adopted, the application of data mining techniques was used more extensively to detect money laundering. Topics for further research and refinement were also identified, such as the need for a better description of data analysis to provide more convincing evidence to support the benefits presented.

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“…Так, авторським колективом: Чен З., Ван Хоа Л.Д., Тео Е.Н. , Назір А., Каруппія Е.К., Лам К.С.,-досліджено техніки машинного навчання, як засіб протидії відмивання коштів [5]. Авторами Гао С., Сю Д., Ванг Х., Грін П. розроблено мультиагентну систему з використанням технології інтелектуальних агентів, яка може бути інтегрована в бізнес-процеси банку для виявлення операцій, пов'язаних з відмиванням грошей [6].…”

Section: The Article Is Devoted To the Relevant Issue Of The Fight Agunclassified

Development of Business Models of Financial Monitoring Processes of Economic Agents

Kuzmenko¹,

Yarovenko²,

Бойко³

et al. 2019

Efektyvna Ekonomika

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Стаття присвячена актуальній темі боротьби із легалізацією коштів, отриманих злочинним шляхом, що є однією з причин тінізації економіки України. Авторами визначено, що головним інструментом протидії даному процесу виступає фінансовий моніторинг, ефективність якого може бути підвищена за рахунок його автоматизації. У статті запропоновано удосконалити існуючу систему фінансового моніторингу за рахунок організації чотирьохрівневої автоматизованої інформаційної системи. Як результат, у Ключові слова: автоматизована інформаційна система моніторингу; бізнес-модель; фінансовий моніторинг; первинний моніторинг; економічний агент; легалізація коштів.

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

Cited by 156 publications

References 59 publications

Machine Learning for Financial Risk Management: A Survey

Machine Learning for Financial Risk Management: A Survey

Application of Technological Solutions in the Fight Against Money Laundering—A Systematic Literature Review

Development of Business Models of Financial Monitoring Processes of Economic Agents

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