Audit data analytics, machine learning, and full population testing

Feiqi Huang, Won Gyun No, Miklos A. Vasarhelyi, Zhaokai Yan

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Emerging technologies like data analytics and machine learning are impacting the accounting profession. In particular, significant changes are anticipated in audit and assurance procedures because of those impacts. One such potential change is audit sampling. As audit sampling only provides a small snapshot of the entire population, it starts to lose some of its meaning in this big data era. One feasible solution is the usage of audit data analytics and machine learning to enable an analysis of the entire population rather than a sample of the transactions. This paper presents an approach for applying audit data analytics and machine learning to full population testing and discusses related challenges.

Original languageEnglish (US)
Pages (from-to)138-144
Number of pages7
JournalJournal of Finance and Data Science
Volume8
DOIs
StatePublished - Nov 2022

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Business, Management and Accounting (miscellaneous)
  • Finance
  • Economics and Econometrics
  • Computer Science Applications
  • Applied Mathematics

Keywords

  • Audit data analytics
  • Full population testing
  • Machine learning

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