Predictive power of ensemble learning in audit qualifications: insights from Indian firms

T. Shahana, Vilvanathan Lavanya and Aamir Rashid Bhat

International Journal of Process Management and Benchmarking, 2025, vol. 21, issue 4, 499-537

Abstract: This study evaluates the effectiveness of ensemble learning methods – bagging, boosting, and stacking – in predicting audit qualifications among Indian firms. Traditional models often fall short because of the complexity of financial reporting data. To address these limitations, our research introduces a novel stacked model incorporating a meta-classifier and utilises a comprehensive dataset enriched with essential financial and non-financial metrics. We systematically compared the performance of seven different ensemble models with conventional classifiers. Our findings demonstrate that ensemble methods, especially our proposed stacking approach, significantly outperform standard models in key metrics such as recall, precision, and area under the curve (AUC). This study highlights the critical role of meticulous feature selection and model calibration in optimising predictive analytics. This research provides valuable insights for auditors, regulatory bodies, and policymakers, enhancing audit quality predictions and supporting financial reporting integrity in the Indian context.

Keywords: ensemble learning; audit qualifications; financial reporting; machine learning models; predictive analytics; Indian firms. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=149981 (text/html)
Access to full text is restricted to subscribers.

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:ids:ijpmbe:v:21:y:2025:i:4:p:499-537

Access Statistics for this article

More articles in International Journal of Process Management and Benchmarking from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().