Leveraging big data analytics to combat emerging financial fraud schemes in the USA: A literature review and practical implications
1 Department of Mathematics and Statistics, College o science, Youngstown State University, Ohio, United States.
2 Department of Business Administration, Institute of Social Sciences, Istanbul Aydin University, Turkey.
3 Business Administration (Accounting), Fenimore & Fisher College of Business, Oral Roberts University, United States.
Review Article
World Journal of Advanced Research and Reviews, 2024, 24(01), 017–043
Publication history:
Received on 20 August 2024; revised on 28 September 2024; accepted on 30 September 2024
Abstract:
The rise of emerging financial fraud schemes in the USA has presented significant challenges for financial institutions, regulators, and law enforcement agencies. As fraud tactics become increasingly sophisticated, traditional methods of detection and prevention are proving insufficient. This literature review explores the role of big data analytics as a critical tool in combating financial fraud, focusing on its practical applications and effectiveness. By leveraging machine learning, predictive analytics, and artificial intelligence, big data analytics enables real-time monitoring and detection of fraudulent activities, offering a more dynamic and adaptive approach to fraud prevention.
The review synthesizes key studies, case reports, and industry practices, highlighting the implementation of big data in analyzing large volumes of transactional and behavioral data to identify suspicious patterns. It further discusses the challenges associated with integrating big data technologies into existing systems, including issues related to data privacy, regulatory compliance, and the high cost of implementation. Additionally, the paper assesses the impact of big data solutions on fraud prevention, comparing their effectiveness to traditional methods and identifying best practices for institutions looking to adopt these technologies.
Finally, the review proposes future directions, emphasizing the potential of emerging technologies such as artificial intelligence and blockchain to enhance fraud detection capabilities. It offers recommendations for financial institutions, regulators, and policymakers to collaborate in developing more robust strategies to safeguard the financial system from evolving fraud threats. This paper provides a comprehensive analysis of the current state of big data analytics in combating financial fraud and its practical implications for the financial services industry in the USA.
Keywords:
Big Data Analytics; Financial Fraud; Fraud Detection; Machine Learning; Predictive Analytics; Artificial Intelligence; USA; Fraud Prevention; Transaction Data; Behavioral Data; Regulatory Compliance; Blockchain; Financial Institutions; Real-Time Monitoring
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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