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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 11 Documents
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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.

Page 2 of 2 | Total Record : 11

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