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Journal articles on the topic 'Anti-Money Laundering Recognition'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

H, S. Vijay Kumar, and Roopashree GR. "Intelligent Anti-Money Laundering System for Money Service Business." COMPUSOFT: An International Journal of Advanced Computer Technology 03, no. 06 (2014): 868–72. https://doi.org/10.5281/zenodo.14742654.

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The criminal individuals and organizations in today's world including terrorists have taken advantage of the available financial systems, to launder money from illegal proceeds and for their illegal activities. India is particularly under threat from terrorists and it also has been difficult to track down the finance of criminal elements. Developing effective suspicious activity detection models has drawn more and more interests for supervision agencies and financial institutions in their efforts to combat money laundering.   This paper proposes a suspicious activity recognition method ba

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2

Naresh, Babu Bynagari, and Al Ayub Ahmed Alim. "Anti-Money Laundering Recognition through the Gradient Boosting Classifier." Academy of Accounting and Financial Studies Journal 25, no. 5 (2021): 1–11. https://doi.org/10.5281/zenodo.5523918.

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Perhaps the most disturbing danger to the solidness and progress of the economy of the world is the beast called illegal tax avoidance. To forestall this beast and the frightful harms it causes, certain rules are set up. These rules are called Against Cash rules. For a smooth methodology concerning tax evasion, it has been suggested that monetary establishments move away from moving toward those that are rule-arranged and hazardous, to those that current danger as their principal drivers. This research aims to explore the exhibition of a slope boosting calculation in distinguishing tax evasion

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Rao, GuoLi, Zhuxuanzi Wang, and Jiayu Liang. "Reinforcement Learning for Pattern Recognition in Cross-Border Financial Transaction Anomalies: A Behavioral Economics Approach to AML." Applied and Computational Engineering 142, no. 1 (2025): 116–27. https://doi.org/10.54254/2755-2721/2025.kl22287.

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This paper presents a novel approach to anti-money laundering (AML) in cross-border financial transactions by integrating reinforcement learning (RL) with behavioral economics principles. The research addresses critical limitations in traditional AML systems by conceptualizing money laundering detection as a sequential decision-making problem where detection policies adapt to evolving criminal strategies. We develop a specialized methodology that incorporates multi-level data representations, behavioral feature extraction algorithms, and a composite reward function designed to balance detectio

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Xu, Haosen, Keke Yu, Ming Wei, and Yida Zhu. "Intelligent Anti-Money Laundering Transaction Pattern Recognition System Based on Graph Neural Networks." Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930) 2, no. 1 (2024): 93–108. https://doi.org/10.60087/vol2iisue1.p007.

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This paper presents a new Intelligent Anti-Money Laundering Transaction Pattern Recognition System based on Graph Neural Networks (GNNs). The proposed system addresses the limitations of traditional anti-money laundering (AML) by leveraging the power of image representation and deep learning techniques. We introduce general methods for creating financial networks based on different shapes, including structural and physical. A custom GNN architecture is designed, featuring heterogeneous graph convolution, listening mechanisms, and physical models to capture the exchange patterns. The system use

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5

Song, Jialin, and Yijun Gu. "HBTBD: A Heterogeneous Bitcoin Transaction Behavior Dataset for Anti-Money Laundering." Applied Sciences 13, no. 15 (2023): 8766. http://dx.doi.org/10.3390/app13158766.

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In this paper, we predict money laundering in Bitcoin transactions by leveraging a deep learning framework and incorporating more characteristics of Bitcoin transactions. We produced a dataset containing 46,045 Bitcoin transaction entities and 319,311 Bitcoin wallet addresses associated with them. We aggregated this information to form a heterogeneous graph dataset and propose three metapath representations around transaction entities, which enrich the characteristics of Bitcoin transactions. Then, we designed a metapath encoder and integrated it into a heterogeneous graph node embedding metho

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Frater, T. "Positive recognition for the BVI for its anti-money laundering standards." Trusts & Trustees 15, no. 3 (2009): 127. http://dx.doi.org/10.1093/tandt/ttp013.

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7

Pereverzyeva, Оlga, and Vasif Gadjiev. "Features of legal nature Financial Action Task Force on Money Laundering FATF." Law Review of Kyiv University of Law, no. 3 (November 10, 2020): 370–74. http://dx.doi.org/10.36695/2219-5521.3.2020.21.

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The article is devoted to the peculiarities of the legal nature of FATF, the process of its creation, the legal principles of activity,as well as the speciality of its legal entity. Attention is also devoted to the main Conventions, which were signed on the eve of The FirstWorld War and after The Second World War and shows the main preconditions and reasons for the creation of the intergovernmentalorganization FATF.The ongoing growth of the drug business, the expansion of its geographical boundaries, increasingly sophisticated and dangerousforms of this criminal business, the struggle of state

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Pereverzyeva, Оlga, and Vasif Gadjiev. "Features of legal nature Financial Action Task Force on Money Laundering FATF." Law Review of Kyiv University of Law, no. 3 (November 10, 2020): 370–74. http://dx.doi.org/10.36695/2219-5521.3.2020.68.

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The article is devoted to the peculiarities of the legal nature of FATF, the process of its creation, the legal principles of activity,as well as the speciality of its legal entity. Attention is also devoted to the main Conventions, which were signed on the eve of The FirstWorld War and after The Second World War and shows the main preconditions and reasons for the creation of the intergovernmentalorganization FATF.The ongoing growth of the drug business, the expansion of its geographical boundaries, increasingly sophisticated and dangerousforms of this criminal business, the struggle of state

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9

Subbagari, Saikiran. "Leveraging Optical Character Recognition Technology for Enhanced Anti-Money Laundering (AML) Compliance." International Journal of Computer Science and Engineering 10, no. 5 (2023): 1–7. http://dx.doi.org/10.14445/23488387/ijcse-v10i5p102.

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10

Bi, Wenyu, Toan Khang Trinh, and Shukai Fan. "Machine Learning-Based Pattern Recognition for Anti-Money Laundering in Banking Systems." Journal of Advanced Computing Systems 4, no. 11 (2024): 30–41. https://doi.org/10.69987/jacs.2024.41103.

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11

Elizabeth Kuukua Woode Amoako, Victor Boateng, Ola Ajay, Tobias Kwame Adukpo, and Nicholas Mensah. "Exploring the role of Machine Learning and Deep Learning in Anti-Money Laundering (AML) strategies within U.S. Financial Industry: A systematic review of implementation, effectiveness, and challenges." Finance & Accounting Research Journal 7, no. 1 (2025): 22–36. https://doi.org/10.51594/farj.v7i1.1808.

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As the U.S. financial sector confronts evolving threats from financial crimes, the integration of Machine Learning (ML) and Deep Learning (DL) into Anti-Money Laundering (AML) strategies has become imperative. This paper explores the role of ML and DL technologies in enhancing AML frameworks to identify, mitigate, and prevent money laundering activities. The paper begins by analyzing prevalent money laundering schemes and the methods used by criminals to bypass traditional AML controls. The study underscores the importance of educating and training financial institution personnel to ensure the

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12

Neimane, Liene. "The Impact of the Legalization of Criminally Obtained Funds on the Economy of Latvia." SOCRATES. Rīgas Stradiņa universitātes Juridiskās fakultātes elektroniskais juridisko zinātnisko rakstu žurnāls / SOCRATES. Rīga Stradiņš University Faculty of Law Electronic Scientific Journal of Law 3, no. 24 (2022): 41–61. http://dx.doi.org/10.25143/socr.24.2022.3.041-061.

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The legitimate aim of confiscation of proceeds of crime can be defined as the removal of proceeds of crime from lawful civil circulation in order to prevent its further circulation and the further commission of criminal offences and to reduce the financial incentive to commit criminal offences. The author’s research of case law and latest trends has led her to the conclusion that the recognition of property as proceeds of crime requires time and understanding of the application of the respective rules in practice. The paper is second part of previously published article. The paper studies curr

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13

El Siwi, Yara. "Mafia, money-laundering and the battle against criminal capital: the Italian case." Journal of Money Laundering Control 21, no. 2 (2018): 124–33. http://dx.doi.org/10.1108/jmlc-02-2017-0009.

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Purpose This paper aims to look at the case of Italy, which clearly stands out in its relationship with organised crime. The recognition that money is the “lifeblood” of OC has resulted in the implementation of what we can refer to as the anti-money laundering (AML) regime, which backs the systematic targeting of mafia assets and the application of severe obstacles to the concealment of dirty money through increased financial surveillance. This paper discusses the financialisation of counter-mafia strategies, with the purpose of questioning the extent to which this system has been delivering w

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14

Oyewale Oyedokun, Somto Emmanuel Ewim, and Oluwaseun Peter Oyeyemi. "Developing a conceptual framework for the integration of natural language processing(NLP) to automate and optimize AML compliance processes, highlighting potential efficiency gains and challenges." Computer Science & IT Research Journal 5, no. 10 (2024): 2458–84. http://dx.doi.org/10.51594/csitrj.v5i10.1675.

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This paper develops a conceptual framework for integrating Natural Language Processing (NLP) into Anti-Money Laundering (AML) regulatory compliance processes. The objective is to automate and optimize AML procedures, enhancing detection capabilities and reducing manual intervention. The research utilizes a qualitative methodology, analyzing existing literature on NLP applications in financial regulations and AML. Key findings indicate that NLP can significantly improve the efficiency of transaction monitoring, entity recognition, and anomaly detection by processing large volumes of unstructure

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15

Chang, Remco, Alvin Lee, Mohammad Ghoniem, et al. "Scalable and Interactive Visual Analysis of Financial Wire Transactions for Fraud Detection." Information Visualization 7, no. 1 (2008): 63–76. http://dx.doi.org/10.1057/palgrave.ivs.9500172.

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Large financial institutions such as Bank of America handle hundreds of thousands of wire transactions per day. Although most transactions are legitimate, these institutions have legal and financial obligations to discover those that are suspicious. With the methods of fraudulent activities ever changing, searching on predefined patterns is often insufficient in detecting previously undiscovered methods. In this paper, we present a set of coordinated visualizations based on identifying specific keywords within the wire transactions. The different views used in our system depict relationships a

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16

Nafisat Temilade Popoola and Felix Adebayo Bakare. "Advanced computational forecasting techniques to strengthen risk prediction, pattern recognition, and compliance strategies." International Journal of Science and Research Archive 12, no. 2 (2024): 3033–54. https://doi.org/10.30574/ijsra.2024.12.2.1412.

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In an era defined by data-driven decision-making, advanced computational forecasting techniques have emerged as powerful tools for strengthening risk prediction, pattern recognition, and compliance strategies. These techniques leverage artificial intelligence (AI), machine learning (ML), and big data analytics to enhance accuracy, efficiency, and reliability in risk assessment across diverse industries. Traditional risk prediction models often rely on historical data and statistical methods, which, while effective, struggle to capture complex, non-linear patterns in evolving datasets. Advanced

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17

DIAK, Oleh. "Operating activities of cryptocurrency exchanges and specifics of their accounting." Economics. Finances. Law 11/2024, no. - (2024): 30–34. https://doi.org/10.37634/efp.2024.11.5.

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This paper examines the operational activities of cryptocurrency exchanges and the distinctive challenges associated with their accounting practices. Unlike traditional exchanges, cryptocurrency exchanges operate within a decentralized and highly volatile environment, which presents unique accounting and regulatory challenges. The paper explores various operational aspects of these exchanges, such as spot and margin trading, deposits and withdrawals, commission structures, liquidity management, and wallet services. Additionally, it analyzes the practices related to staking and lending services

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18

Hunko, Leonid. "Regulatory and Legal Provision of the Fight Against Illegal Armed Groups in Ukraine." Democratic governance 29, no. 1 (2022): 96–111. http://dx.doi.org/10.23939/dg2022.01.096.

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Problem setting. The definition of illegal armed groups is analyzed, the content of problems that need a legal provision to combat illegal armed groups is determined. The system of Ukrainian normative legal acts and key international legal acts related to terrorism and the fight against terrorist financing is outlined. Problem statement. Armed aggression of the Russian Federation against Ukraine, in particular, armed clashes and hostilities with the participation of regular or irregular forces of the Russian Federation, illegal armed groups and terrorist organizations at the Ukrainian state bo

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19

Astrova, Irina. "Anti-money laundering powered by graph machine learning: “Show me your friends and I will tell you who you are”." Intelligent Decision Technologies, February 21, 2023, 1–19. http://dx.doi.org/10.3233/idt-220193.

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Today financial institutions have been investing billions of US dollars to detect money laundering. When financial institutions are found to have their customers conduct money laundering through them, they are subjected to large penalties. Moreover, their reputation suffers greatly through public exposure. In response, financial institutions have been exploring opportunities to use graph machine learning algorithms. This paper describes one of those algorithms called Anti-TrustRank and demonstrates how it can be used to identify money launderers. In contrast to many other algorithms, Anti-Trus

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20

Rowley, Clare. "Leveraging the Legal Entity Identifier to mitigate the risk of financial crime and enhance fraud prevention in cross-border payments." Journal of Payments Strategy & Systems, March 1, 2024. http://dx.doi.org/10.69554/fumw3631.

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While today’s global digital economy promises a new era of commerce, its sheer complexity also presents opportunities for malicious actors to perpetrate money laundering, terrorism financing, and other forms of financial crime. Despite the escalating compliance costs faced by financial institutions needing to meet increasingly rigorous anti-money laundering (AML), counter-terrorist financing (CTF) and sanction screening obligations, the lack of harmonisation within cross-border data flows inhibits the identification of suspicious actors and exposure of criminal networks. This contributes to a

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