...Mark Eshwar Lokanan Purpose This study explores the application of machine learning ( ML ) algorithms to enhance the detection and reporting of Suspicious Activity Reports (SARs) in California’s financial sector. This research aims to improve anti-money laundering ( AML ) compliance by evaluating...

...Jose Castelao-López; Teresa Corzo Santamaría; Dolores Lagoa-Varela Purpose The purpose of this paper is to systematically review and evaluate recent anti-money laundering (AML) research, focusing on methodological shifts toward machine learning and network analysis, and identify key challenges...

... at: ns.andriychenko@yur.sumdu.edu.ua © 2025 Emerald Publishing Limited 2025 Emerald Publishing Limited Licensed re-use rights only Artificial intelligence Machine learning Bank Financial monitoring Suspicious transactions Money laundering Money laundering is a critical source of illicit...

... at: celestialrita@gmail.com © 2025 Emerald Publishing Limited 2025 Emerald Publishing Limited Licensed re-use rights only Ponzi schemes Pyramid scheme Machine learning Financial literacy Ethereum Blockchain Over the years, P.S.’s have become widespread and more sophisticated, partly due...

... and “auto-escalating” high-risk alerts. Design/methodology/approach Machine learning is used to create a “bolt on” model that sits on top of existing rule-based transaction monitoring systems to improve their effectiveness and efficiency. This was achieved by developing a model to mimic the analysts...

... of money laundering concepts, issues and techniques of AML. The review articles are on the techniques of AML, such as machine learning, data mining, graph networks and artificial intelligence, which are applied to detect and prevent money laundering issues. Originality/value Money laundering, being...

...Syahril Ramadhan Purpose The purpose of this study is to develop and evaluate the effectiveness of the criminology-centric machine learning (CCTML) framework in detecting money laundering activities by integrating criminological theories with machine learning techniques. Design/methodology...

...Endre Jo Reite; Johan Karlsen; Elias Grefstad Westgaard Purpose This study aims to describe and empirically explore a new method for bank anti-money laundering (AML) systems using machine learning models. Current automated money laundering detection systems are notorious for flagging many false...

.../approach This research is a literature review from various research sources originating from Pro-Quest, Emerald, Science Direct and Google Scholar. Findings The researchers found that the most widely used methods for detecting money laundering were artificial intelligence, machine learning, data...

..., a statistical approach which uses machine learning algorithm to predict outcomes by using historical data. The models are applied to a modified data set designed to mimic transactions of retail banking within the USA. Design/methodology/approach Machine learning classifiers, as a subset of AI, are trained...

... and sell cryptocurrencies for illegal drug trade on DarkNet was discovered using machine learning, i.e. natural language processing and topic modeling. This study compares how current Italian and Russian laws address this technique. Findings Despite differences in cryptocurrency regulation, both...

... of ML as a tool to combat money laundering using cryptocurrency exchanges. Anti-money laundering Machine learning Supervised learning Algorithms Cryptocurrency Several recent studies (Jullum et al., 2020 ; Alarab et al., 2020) exhibit promising results in the field...

...Abhishek Gupta; Dwijendra Nath Dwivedi; Jigar Shah; Ashish Jain Purpose Good quality input data is critical to developing a robust machine learning model for identifying possible money laundering transactions. McKinsey, during one of the conferences of ACAMS, attributed data quality as one...

... after omitting out-of-scope selections was 27 documents, which mainly span from 2015 to 2020. The sample is discussed based on a categorization, which demarcates solutions, machine learning, data sources, evaluation methods, implementation tools, sampling techniques and regions of study...

... in the data without information on which data correspond to money laundering and not. For (2), latter, the methods attempt to learn the patterns that differentiate between money laundering and legitimate operations by using data where the label/outcome (money laundering or not) is known. Machine learning...

... laundering transactions Compliance Money laundering Machine learning Data mining Algorithm Data analysts Figure 3. Diagrammatic illustration of clustering in data mining The evaluation and interpretation stage of the data mining process is where data analysts evaluate the quality...