How Data Analytics is Revolutionizing Forensic Accounting Investigations: A Deep Dive into Fraud Detection Techniques

Forensic accounting has long relied on traditional auditing techniques, but the integration of data analytics has revolutionized fraud detection, enhancing accuracy and efficiency. This study explores the role of data analytics in forensic accounting investigations over the past five years, assessing its impact on fraud detection methodologies. Using secondary data analysis, the study examines forensic reports, financial fraud case studies, and regulatory documents from 2020 to 2024. Key findings indicate a significant increase in fraud detection success, rising from 68% in 2020 to 88% in 2024, alongside a reduction in average fraud detection time from 45 to 25 days. Statistical models confirm a strong positive correlation (r = 0.998, p < 0.001) between forensic analytics adoption and fraud detection efficiency, with an R-squared value of 0.995, demonstrating that analytics explains 99.5% of fraud detection variability. The results also reveal that AI-driven fraud detection models significantly improve fraud recovery rates, increasing from 50% to 90% as forensic analytics adoption rose from 45% to 85%. Despite these advancements, challenges such as data privacy concerns, high implementation costs, and the evolving nature of financial fraud persist. The study recommends continuous investment in forensic analytics training, real-time fraud monitoring systems, and regulatory frameworks to enhance ethical AI integration in fraud detection.