The methodical application of data analytics to find abnormalities, patterns, and irregularities that can point to fraudulent or incorrect activity within organizational processes is known as data-driven fraud detection (Challa, 2025; Albrecht et al., 2006). While fraud involves intentional acts designed to deceive for one’s own or an organization’s benefit, errors usually result from unintentional mistakes such data entry issues, incorrect calculations, or a lack of understanding of procedures. Because errors and fraud demand different investigative responses, internal controls, and governance tactics, it is critical to distinguish between the two. Data analytics helps investigators and auditors make decisions based on facts rather than their emotions.

Audit sampling is crucial in the auditing process for identifying fraud and errors (Rahman et al., 2023). In order to assess compliance, accuracy, and risk exposure, auditors choose representative samples because it is frequently impractical to look at every transaction. However, sophisticated or uncommon fraud schemes might go undetected by conventional sampling techniques. By employing risk-based models, anomaly detection, and stratification techniques, data-driven approaches improve audit sampling and free up auditors to concentrate on high-risk transactions instead of depending just on random selection.

Modern auditing techniques are more effective when audit sampling and data-driven fraud detection are combined (Abhayawansa et al., 2021). Large databases may be continuously monitored by sophisticated analytical tools, which can also spot odd trends and flag questionable transactions for additional research. This method not only raises the possibility of finding fraud but also boosts the effectiveness and dependability of audits. In the end, data-driven audit sampling helps firms in the public and private sectors have better corporate governance, accountability, and transparency.

References

  • Abhayawansa S, Adams CA and Neesham C. (2021) Accountability and governance in pursuit of Sustainable Development Goals: conceptualising how governments create value. Accounting, Auditing & Accountability Journal 34: 923-945.
  • Albrecht WS, Albrecht CO, Albrecht CC, et al. (2006) Fraud examination: Thomson South-Western New York, NY.
  • Challa SR. (2025) Advancements in Digital Brokerage and Algorithmic Trading: The Evolution of Investment Platforms in a Data Driven Financial Ecosystem. Advances in Consumer Research 2.
  • Rahman MS, Bhowmik PK, Hossain B, et al. (2023) Enhancing Fraud Detection Systems in the USA: A Machine Learning Approach to Identifying Anomalous Transactions. Journal of Economics, Finance and Accounting Studies 5: 145-160.