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Aida Nahar
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Journal of Business Crime
Published by Generate Digital Publishing
ISSN : -     EISSN : 30904412     DOI : 10.70764/gdpu-jbc
Core Subject : Economy, Science, Social,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Social Sciences
JBC: Journal of Business Crime provides a venue for high-quality manuscripts dealing with economics, accounting, and compliance in its broadest sense. The editorial board encourages manuscripts that are international in scope, articles that are perceptive, evidence-based, and have a policy impact. however, readers can also find papers investigating domestic issues with global relevance. JBC is published by the Publishing Company "Generate Digital Publishing". JBC is an open access journal which means that all contents is freely available without charge to the user or his/her institution. The scope of this journal includes empirical and theoretical articles related to economics, accounting, criminology, criminal justice, control, prevention of financial crime and related abuse.
Arjuna Subject : Umum - Umum
Articles 12 Documents
 1   2 
A Comprehensive Framework to Identify and Prevent Money Laundering in Decentralized Finance Using Big Data Analytics Eva Harmelia Valentina; Kinza Aish
Journal of Business Crime Vol. 1 No. 2 (2025)
Publisher : Journal of Business Crime

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70764/gdpu-jbc.2025.1(2)-11

Abstract

Objective: This research aims to develop a comprehensive framework to identify and prevent money laundering in Decentralized Finance (DeFi) by leveraging big data analytics, integrating advanced machine learning algorithms, and network analysis techniques to address the challenges of pseudonymity and decentralization inherent to this ecosystem.Research Design & Methods: This research utilizes a mixed method approach with machine learning analysis based on Elliptic Dataset and qualitative policy study, applying graph models and classification algorithms to detect illegal transactions with precision in the context of imbalanced data. Findings: The results show that the MLP and GCN models achieve high accuracy (98% and 97.3%) and excellent recall (99.5% and 99.4%) on the Elliptic Dataset, significantly outperforming traditional methods. Exploratory data analysis and graph visualization confirmed that illegal transactions form denser clusters and more complex paths, indicating a layering pattern. Implications and Recommendations: Theoretically, this research extends the application of big data and graph theory to new financial systems, providing a blueprint for future RegTech and FinTech research. Practically, the framework offers tangible tools for regulators, law enforcement, and DeFi platforms to enhance AML capabilities, supporting the development of real-time monitoring tools and risk assessment models. Contribution and Value Added: The main contribution of this research is the development of a robust and adaptive big data analytics-based AML framework, which effectively addresses the unique challenges of DeFi.
Artificial Intelligence and Money Laundering in The Application of International Criminal Law Abel , Miguel
Journal of Business Crime Vol. 2 No. 1 (2026)
Publisher : Generate Digital Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70764/gdpu-jbc.2026.2(1)-01

Abstract

Objective: This study examines the legal and theoretical issues related to using Artificial Intelligence (AI) for preventing and enforcing money laundering under international and European law. It highlights the conflict between technological advancements and the safeguarding of fundamental rights, especially regarding the EU's 2024 anti-money laundering regulatory package and the AI Act. This work was financially supported by the PID2024-160160OB-I00 project of the Spanish State Research Agency (Ministry of Science, Innovation and Universities), Operational Program FEDER "A way of making Europe"Research Design & Methods: This research examines how different EU regulations and directives on Artificial Intelligence align with each other. It analyzes key concepts such as the Europeanization of criminal law, the principles of legality and proportionality, and the balance between security and liberty. The study uses secondary data from EU legal documents and academic literature.Findings: This research shows that AI can help detect and combat money laundering, but it also poses risks such as opaque algorithms, bias, and privacy violations. The EU 2024 regulatory framework seeks to make the use of AI more humane and trustworthy, but differences in criminal law across countries and the lack of a unified European Criminal Code make enforcement difficult. Concerns remain about the proportionality and legality of sanctions among Member States.Implications: The study highlights the need for continuous legal changes, close human supervision, and a distinct separation of administrative and criminal law for using AI in anti-money laundering systems. It also emphasizes strengthening the alignment of criminal law across Europe for better legal clarity and fairness.Contribution & Value Added: This paper looks at how international and European criminal law is developing in the digital world. It links AI governance with anti-money laundering policies. The paper critically assesses the EU 2024 regulatory framework and offers suggestions for balancing technological innovation with the protection of fundamental rights in the criminal legal system.

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