Conventional audit sampling is based on the idea of adequate confidence, which means that in order to determine whether financial statements are fair, auditors look at a subset of transactions (Aslan, 2021). Although this method is effective and commonly used, it was not created with fraud detection in mind. Since fraud is frequently deliberate, selective, and strategically hidden, it is less likely to occur in sampled transactions than misstatements, which are assumed to be distributed randomly in sampling.

Small-value or carefully planned transactions—such as those with amounts slightly below approval limits or infrequent entries meant to avoid detection—are often the sites of fraud (Albrecht et al., 2006). When auditors only employ statistical or judgmental sample approaches, they may miss these transactions since they are purposefully made to blend in with regular corporate operations. Because of this, even meticulously created audit samples could miss important clues of dishonest activity.By focusing on full-population analysis employing analytical techniques and technology, data-driven fraud detection gets over these restrictions (Ma et al., 2019). Auditors can spot patterns, trends, and anomalies that indicate a higher risk of fraud by looking at every transaction. Crucially, data analytics improves the audit process by identifying high-risk items for targeted inquiry, enabling auditors to use professional scepticism more successfully and efficiently. It does not, however, replace auditor judgment.

References:

  • Albrecht WS, Albrecht CO, Albrecht CC, et al. (2006) Fraud examination: Thomson South-Western New York, NY.
  • Aslan L. (2021) The Evolving Competencies Of The Public Auditor And The Future Of Public Sector Auditing. In: Grima S and Boztepe E (eds) Contemporary Issues in Public Sector Accounting and Auditing. 113-129.
  • Ma JC, Duan ZT and Tang L. (2019) A Methodology to Assess Output Vulnerability Factors for Detecting Silent Data Corruption. Ieee Access 7: 118135-118145.