Audit data analytics, machine learning, and full population testing

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

doi:10.1016/j.jfds.2022.05.002

Audit data analytics, machine learning, and full population testing

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

Rutgers Business School, Acctg & Info Systems

Rutgers, The State University

Research output: Contribution to journal › Article › peer-review

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 language	American English
Pages (from-to)	138-144
Number of pages	7
Journal	Journal of Finance and Data Science
Volume	8
DOIs	https://doi.org/10.1016/j.jfds.2022.05.002
State	Published - Nov 2022

ASJC Scopus subject areas

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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10.1016/j.jfds.2022.05.002

Cite this

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title = "Audit data analytics, machine learning, and full population testing",

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.",

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AU - Vasarhelyi, Miklos A.

AU - Yan, Zhaokai

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N2 - 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.

AB - 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.

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JF - Journal of Finance and Data Science

ER -

Audit data analytics, machine learning, and full population testing

Abstract

ASJC Scopus subject areas

Keywords

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