Utilizing data analytics for fraud detection in accounting: A review and case studies

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.