The Impact of Big Data Analytics on The Detection of Errors And Fraud in Accounting Processes

Abstract

Purpose: This study aims to discuss and investigate the role of big data analytics (BDA) in promoting error detection and preventing fraud in accounting operations.   Methodology: It uses a secondary method of data collection (desk study) to explore the potential impact of BDA in enhancing error and fraud prevention on six key considerations including data quality and integrity; data privacy and security; real-time monitoring and alerts; integration with internal controls; ethical implications; and human experience.   Finding: The analysis shows that the BDA enhances fraud detection by integrating data from multiple sources, using sophisticated algorithms to identify anomalies. Reduces false positives and improves accuracy. However, human expertise is essential for ethical standards and transparency.   Implications: It has significant implications for the accounting profession, as it provides an addition in both theoretical knowledge and practical applications, theoretical implications include developing accounting knowledge, developing data-driven models, establishing ethical frameworks, and promoting interdisciplinary insights. On a practical level, it provides guidance for improving financial accuracy, fraud prevention, regulatory compliance, data-driven decision-making, and professional development for accountants.   Contribution: It contributes to bridging the research gap in the aspect related to the analysis of big data and its impact on the quality of accountants' work, as this topic is of high importance to researchers, governments, policymakers, industries, companies, investors, and regulators, bridging the gap between accounting and data analytics. This interdisciplinary approach is critical in understanding the evolving landscape of the impact of big data analytics on financial transparency and accuracy of financial reporting.   Article Type: Research Paper.