1 KPMG, USA.
2 Financial analyst, Texas, USA.
3 Independent Researcher, Lagos, Nigeria.
4 Independent Researcher, London, ON, Canada.
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(01), 1972-1980
Received on 08 June 2024; revised on 18 July 2024; accepted on 20 July 2024
Financial fraud poses a significant threat to the global economy, necessitating advanced measures for detection and prevention. This paper explores the application of machine learning techniques to enhance transaction security and combat financial fraud. It provides a comprehensive overview of machine learning algorithms, including supervised and unsupervised learning, neural networks, and anomaly detection. Each technique's application in identifying and preventing fraudulent activities is discussed, along with their advantages and limitations. Challenges in implementing machine learning for fraud detection, such as data quality, scalability, real-time processing, and model interpretability, are examined. Ethical and privacy concerns associated with using machine learning in financial transactions are also addressed. By highlighting these aspects, the paper aims to contribute to developing more effective and ethical machine learning-based fraud detection systems, ensuring robust transaction security and fostering trust in financial institutions.
Financial fraud; Machine learning; Transaction security; Fraud detection; Ethical concerns
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Shadrack Obeng, Toluwalase Vanessa Iyelolu, Adetola Adewale Akinsulire and Courage Idemudia. Utilizing machine learning algorithms to prevent financial fraud and ensure transaction security. World Journal of Advanced Research and Reviews, 2024, 23(1), 1972-1980. Article DOI: https://doi.org/10.30574/wjarr.2024.23.1.2185