Data Analytics in Forensic Auditing Leveraging Technology to Detect Financial Irregularities

Abstract

This research explores the role of data analytics in forensic auditing, focusing on how technology can be leveraged to detect financial irregularities. The study aims to demonstrate the integration of advanced data analysis techniques, such as data mining, machine learning, and pattern recognition, in uncovering fraudulent activities within financial systems. The research employs a qualitative approach, utilizing case studies from various sectors to evaluate the effectiveness of data analytics in forensic investigations. Findings indicate that data analytics enhances the ability to identify discrepancies, trends, and anomalies that traditional auditing methods might overlook. Additionally, the use of technology significantly reduces the time and cost associated with audits, providing more efficient and accurate results. The implications of this study suggest that adopting data analytics tools in forensic auditing can improve the detection and prevention of financial fraud, ultimately fostering transparency and trust in financial reporting systems.