Design and development of a fintech-based algorithmic framework for detecting and preventing cross-border financial terrorism
1 MBA Finance and Strategy Program, Olin Business School, Washington University in St. Louis, MO, USA.
2 Department of Computation Data Science and Engineering North Carolina A & T State University, Greensboro, NC, USA.
3 School of Computer Science, University of Guelph, Ontario, Canada.
4 MBA program (Finance and Strategy), The Ohio State University, Columbus, OH USA.
5 Financial Analysis Program, Fox School of Business, Temple University, Philadelphia, PA, USA.
6 Finance and Risk Management Program, Hult International Business School, Cambridge, MA, USA.
7 Finance Program, David Eccles School of Business, University of Utah, Salt Lake City, UT, USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 1688–1698
Article DOI: 10.30574/wjarr.2024.23.2.2500
Publication history:
Received on 07 July 2024; revised on 19 August 2024; accepted on 21 August 2024
Abstract:
The increasing complexity of financial transactions and the sophistication of cybercriminals have made the detection and prevention of cross-border financial terrorism a critical challenge. This study presents the design and development of a fintech-based algorithmic framework leveraging big data analytics, machine learning, blockchain technology, and natural language processing to enhance fraud detection and prevention in financial institutions. We applied various supervised and unsupervised learning algorithms to extensive transaction datasets, achieving high accuracy in detecting fraudulent activities. Notably, the XGBoost model demonstrated superior performance with a precision of 0.94, recall of 0.92, and an AUC-ROC of 0.96. The Random Forest algorithm also showed strong results, with a precision of 0.93 and recall of 0.91. Unsupervised learning methods, such as K-means clustering, effectively identified new fraud patterns, achieving a precision of 0.96 in anomaly detection. The integration of blockchain technology ensured transaction security and transparency, with zero tampered transactions recorded. Natural language processing techniques, including sentiment analysis and entity recognition, successfully detected linguistic cues indicative of fraud, with negative sentiments correlating strongly with fraudulent activities. Real-time analytics capabilities were validated with high accuracy and low latency, enabling timely detection and response to fraudulent transactions. Geographic distribution analysis identified high-risk regions, providing insights for targeted fraud prevention strategies. These advancements significantly improve the capabilities of U.S. financial institutions to combat financial terrorism, ensuring greater financial stability and compliance with regulatory requirements.
Keywords:
Financial Terrorism; Machine Learning; Blockchain; Big Data Analytics; Natural Language Processing.
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0