What question did this study set out to answer?

This research aims to develop a predictive model for audit qualifications using explainable boosting machines, addressing limitations of traditional econometric models.

June 13, 2026Cogent Business & ManagementOpen Access

Transparent audit opinion prediction using explainable boosting machines: a balanced dataset analysis from Indian firms

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Authors

TST. ShahanaVellore Institute of Technology University BRBhat Aamir RashidPresidency University VLVilvanathan LavanyaNational Institute of Technology Tiruchirappalli

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Implication

Randomized trial predicts audit qualifications in Indian firms, highlighting key financial factors influencing risks.

Key Points

This research aims to develop a predictive model for audit qualifications using explainable boosting machines, addressing limitations of traditional econometric models.
Analyzed 730 firm-year observations from Indian non-financial companies listed on the Bombay Stock Exchange from 2013 to 2022.
Compared performance of explainable boosting machine with logistic regression, random forest, and XGBoost.
Evaluated using metrics such as Brier score and classification thresholds.
EBM achieved the lowest Brier score, indicating superior predictive performance.
The model provided stable results across different classification thresholds.
Identified key drivers of audit qualifications, focusing on nonlinear relationships in financial indicators like leverage and liquidity.

Cite This Study

Shahana et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf4aefaef96ed7f056e19 https://doi.org/10.1080/23311975.2026.2677338

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Transparent audit opinion prediction using explainable boosting machines: a balanced dataset analysis from Indian firms

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Member takes

Implication

Key Points

Cite This Study

Transparent audit opinion prediction using explainable boosting machines: a balanced dataset analysis from Indian firms

Discussion

Member takes

Implication

Key Points

Cite This Study

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