Abstract
The rapid growth of digital financial systems and the increasing complexity of business transactions have significantly increased the risk of financial fraud in organizations worldwide. Traditional auditing techniques that rely heavily on manual procedures and limited sampling methods often fail to detect sophisticated fraudulent activities hidden within large volumes of financial data. In response to these challenges, the use of data analytics has emerged as a powerful tool for enhancing the effectiveness of auditing processes and improving fraud detection capabilities. Data analytics allows auditors to analyse vast amounts of structured and unstructured financial data using advanced statistical techniques, algorithms, and visualization tools. By examining entire datasets instead of limited samples, auditors can identify unusual patterns, anomalies, and irregularities that may indicate fraudulent activities. Technologies such as machine learning, predictive analytics, artificial intelligence, and data mining are increasingly being integrated into audit practices to improve the accuracy and efficiency of fraud detection. This research paper explores the role of data analytics in modern auditing practices with a specific focus on fraud detection. The study examines different analytical techniques used by auditors, the benefits of adopting data analytics in audit processes, and the challenges associated with its implementation. The research is based on secondary data sources including academic journals, professional audit reports, and industry publications. The findings of the study indicate that data analytics significantly enhances the ability of auditors to detect fraudulent transactions, monitor financial activities in real time, and improve the overall quality of audits. However, successful implementation requires investment in technological infrastructure, skilled professionals, and effective data governance practices. The study concludes that data analytics is transforming the auditing profession by enabling more comprehensive financial analysis and proactive fraud detection strategies. As organizations continue to generate massive amounts of data, the importance of data analytics in auditing will continue to grow in the future.
Keywords
Data Analytics, Auditing, Fraud Detection, Financial Fraud, Machine Learning, Predictive Analytics, Digital Auditing
Citations
IRE Journals:
Rohit Kumar Jaiswal "Use of Data Analytics in Auditing for Fraud Detection" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 491-495 https://doi.org/10.64388/IREV9I9-1714934
IEEE:
Rohit Kumar Jaiswal
"Use of Data Analytics in Auditing for Fraud Detection" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1714934
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