Integrating Blockchain with Machine Learning for Enhanced Money Laundering Detection

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Zahra Kazemi

Abstract

The integration of blockchain technology with machine learning presents a transformative approach to enhancing the detection of money laundering activities. This paper explores the synergistic potential of combining these two innovative technologies to address the complexities and challenges inherent in identifying illicit financial transactions. Blockchain's decentralized ledger offers a robust platform for ensuring data integrity, transparency, and traceability, which are critical in the financial sector's ongoing battle against money laundering. In parallel, machine learning algorithms provide powerful tools for pattern recognition and anomaly detection, thereby enabling the identification of suspicious behaviors that could signify fraudulent activities.


 


In this study, we propose a novel framework that leverages the immutable nature of blockchain to store transaction data securely, while employing machine learning models to analyze these datasets for anomalies. This dual approach not only enhances the reliability of the system but also improves the accuracy of detection. By utilizing smart contracts, we automate the process of flagging potentially illicit transactions, thereby reducing the time and resources traditionally required for manual investigations.


 


Our experimental results demonstrate that the proposed system significantly outperforms existing methods in terms of both precision and recall, offering a higher rate of true positive identifications of suspicious activities. Furthermore, the integration of blockchain ensures that the data remains tamper-proof and auditable, providing an added layer of security and trust, essential in regulatory compliance processes.


 


The findings of this research indicate that the convergence of blockchain and machine learning can substantially elevate the effectiveness and efficiency of anti-money laundering systems. This paper contributes to the growing body of knowledge by offering a robust technological solution that could redefine how financial institutions combat money laundering, ultimately fostering a more secure and transparent financial ecosystem.

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How to Cite

Integrating Blockchain with Machine Learning for Enhanced Money Laundering Detection. (2023). International Journal of Computational Health & Machine Learning, 1(1). https://ijchml.com/index.php/ijchml/article/view/218

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