Determing Audit Materiality Using AI

Nikolaos Belesis (), Christos Kampouris (), Andreas Fousteris () and Antonios Vasilatos ()
Additional contact information
Nikolaos Belesis: University of Piraeus, Karaoli and Dimitriou
Christos Kampouris: University of Piraeus, Karaoli and Dimitriou
Andreas Fousteris: University of Piraeus, Karaoli and Dimitriou
Antonios Vasilatos: University of Piraeus, Karaoli and Dimitriou

A chapter in Advanced Data Analytics, Machine Learning and AI in Business, 2026, pp 599-613 from Springer

Abstract: Abstract The paper estimates the numerical levels of audit materiality thresholds with the sensitivity of market value to key accounting variables in publicly listed corporations. As a response to the perpetual problem of establishing the appropriate quantitative levels of materiality, the study scrutinizes the panels of the Russell 3000 index covering the 2001–2023 time span, excluding the special sector firms. Utilizing Ordinary Least Squares (OLS) regression and advanced machine methods—Random Forest and Gradient Boosting—this research elucidates how book value, revenues, earnings, and total assets influence market capitalization. Log-transformation of the variables is used to address skewness and enable elasticity-based interpretation. The results are that multiple accounting variable models reveal high explanatory power, of which Random Forest outperforms, followed by OLS then Gradient Boosting, in the complex, multi-variable scenarios. OLS, on the other hand, continues to dominate in the simple, mono-variable models. The results are that standard OLS-based materiality levels are wide in accordance with the literature, but machine methods favor lower levels, substantially so, if the machine methods reveal non-linearities, besides when adjusting for multicollinearity. The contribution of the study is twofold: first, it offers the literature the first market-based, empirically derived intervals of audit materiality, and second, it evaluates how traditional OLS and machine-learning models differ in their implications for materiality judgments, highlighting the methodological risks of relying solely on ML techniques in the presence of nonlinearities and multicollinearity. The critical limitation is the exhaustive set of all sectors, uniformly, with no segregations for special sectors.

Keywords: Audit Materiality; Materiality Quantitative Thresholds; Machine Learning; Russell 3000; Audit (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-032-23493-3_36

Ordering information: This item can be ordered from
http://www.springer.com/9783032234933

DOI: 10.1007/978-3-032-23493-3_36

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().