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Utilizing data analytics for fraud detection in accounting: A review and case studies
1 University of Law Business School, Manchester, United Kingdom.
2 Dangote Sugar Refinery Plc, Lagos, Nigeria.
3 Spacepointe Limited, Rivers State, Nigeria.
4 Independent Researcher, Anambra, Nigeria.
5 Independent Researcher, San Francisco, USA.
6 Information Technology & Management, University of Texas, Dallas, USA.
Review
International Journal of Science and Research Archive, 2024, 11(01), 1348–1363.
Article DOI: 10.30574/ijsra.2024.11.1.0221
Publication history:
Received on 27 December 2023; revised on 03 February 2024; accepted on 05 February 2024
Abstract:
This research paper offers a comprehensive exploration of the evolving landscape of fraud detection strategies within the accounting sector, driven by the integration of data analytics, machine learning, and big data technologies. The study aims to investigate, analyze, and provide insights into the practical application, challenges, and implications of these advanced technologies in fraud detection. Through an extensive literature review, a range of case studies, and a comparative analysis of methodologies, this paper delves into the key aspects of data-driven fraud detection. The literature review establishes the significance of data analytics in the context of fraud detection, highlighting its pivotal role in identifying and preventing fraudulent activities. Various case studies from diverse sectors, including finance, healthcare, and e-commerce, exemplify successful implementations of data analytics and the challenges faced in real-world scenarios. A comparative analysis of fraud detection approaches showcases the strengths and limitations of different methodologies, guiding organizations in optimizing their fraud detection strategies. The findings underscore the transformative impact of data analytics, machine learning, and big data in revolutionizing fraud detection. Implications drawn from this research suggest a future where these technologies will continue to be instrumental in proactively combating evolving fraudulent activities, ensuring regulatory compliance, and upholding ethical standards.
Keywords:
Fraud Detection; Data Analytics; Machine Learning; Big Data; Financial Fraud; Case Studies; Comparative Analysis; Technology Integration.
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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
