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Corporate investments in artificial intelligence and audit costs: does audit quality matter?

Research Abstract


 

Purpose

This study aims to investigate the influence of clients’ investments in artificial intelligence (CINV_AI) on audit costs within the Chinese context. Furthermore, this study moderates the role of audit quality on the relationship between corporate investments in AI and audit costs.

Design/methodology/approach

To test the hypotheses, this study uses an ordinary least squares regression using a final sample of 26,654 firm-year observations spanning the period 2016–2023. To mitigate potential endogeneity concerns, the researchers adopted the instrumental variable technique, specifically the two-stage least squares method.

Findings
This study reveals that corporate investments in AI has a statistically significant positive effect on audit costs, suggesting that clients with high investments in AI-increasing operational complexity and risk, increasing audit effort, improving audit efficiency and ultimately incurring higher audit costs. Furthermore, the results indicate that audit quality positively and significantly reinforces the link between corporate investments in AI and audit costs. Finally, the robustness tests support the main findings and confirm their validity.
Practical implications

This paper provides valuable insights for corporate managers, investors and auditors. For managers and investors, it emphasizes that AI implementation constitutes a substantial investment, encompassing considerable direct expenditures on assets and technology, as well as indirect costs such as increasing audit costs. For auditors, it emphasizes that these AI investments necessitate more audit effort and team members with specific IT expertise.

Originality/value

The results provide new evidence contributing to the recent inconclusive literature that investigates the impact of client IT capabilities (AI) on audit costs. To the best of the authors’ knowledge, this is the first study to investigate the moderating role of audit quality in the relationship between corporate AI investment and audit costs.

Keywords:

IT capabilities, Artificial intelligence, AI, Audit costs, Audit quality, China

Research Authors
Mohsen Anwar Abdelghaffar SalehCorresponding Author; Shadi Emad Areef Alhaleh; Abdelkarim Mahmoud Mohamed; Sameh Abdelsalam Mustafa
Research Date
Research Journal
Journal of Financial Reporting and Accounting
Research Member
Mohsen Anwar Abd ELghaffar Saleh Ali

Enhancing Insurance Fraud Detection Accuracy with Integrated Machine Learning and Statistical Methods

Research Abstract

The insurance industry plays a critical role in managing risks and providing financial security globally. However, the industry faces challenges, particularly with the increasing complexity of fraudulent activities. To address these challenges, this work seeks to construct suitable decision models by integrating methods such as feature discretization, feature selection, data resampling, and binary classification in order to create a prediction system for identifying insurance fraud. The research investigates various scenarios, including different combinations of classifiers, feature selection methods, feature discretization techniques, and data resampling strategies, and the performance of the predictive system is evaluated using established metrics. The experimental results revealed that integrating multiple methodologies during data preprocessing significantly enhances the performance of classification models. The model that utilizes the KBD + RFE + Over + RF scenario achieves the highest AUC and F1-score, indicating exceptional performance in detecting insurance fraud. Our research demonstrates that the proposed models’ ability to predict insurance fraud has been significantly enhanced by utilizing resampling methods and highlights the importance of these techniques in improving the efficiency of the utilized integrated artificial intelligence techniques. In addition, the article concludes that the insurance industry can greatly benefit from modern predictive methods to make sound decisions.

Research Authors
Ahmed Abdelreheem Ahmed Mohamed Khalil
Research Date
Research Department
Department of Statistics, Mathematics and Insurance
Research File
Khalil-2025-Computational_Economics.pdf (2.68 MB)
Research Journal
Computational Economics Journal
Research Member
Ahmed Abd El-Reheem Ahmed Mohamed
Research Publisher
Springer
Research Rank
1
Research Website
https://link.springer.com/article/10.1007/s10614-025-11074-0
Research Year
2025

Disclosure of goodwill-related key audit matters and stock price crash risk: evidence from China

Research Abstract

Purpose

This study aims to investigate whether and how the disclosure of key audit matters (KAMs) or critical audit matters (CAMs) affects stock market reaction by examining the association between goodwill-related KAMs and stock price crash risk (SPCR) in the Chinese context.

Design/methodology/approach

The authors use ordinary least squares (OLS) regression to estimate the impact of goodwill-related to KAMs disclosure on SPCR based on a sample of 26,593 firm-year observations from Chinese A-share companies listed on the Shanghai and Shenzhen Stock Exchanges from 2016 to 2023. To check the consistency of the findings, the authors use propensity score matching (PSM), alternative measure of goodwill-related KAMs, control for the extreme impact of the COVID-19 pandemic and the two-way cluster-robust standard errors.

Findings

The findings of this study indicate a negative association between goodwill-related to KAMs disclosure and SPCR, suggesting that auditors’ increased disclosure of goodwill as a KAM reduces corporate opacity and constrains managerial discretion in avoiding the recognition of goodwill impairment. Such proactive disclosure enhances audit quality and the transparency of the auditor’s work, thereby improving investors’ perceptions of risk and ultimately contributing to a reduction in SPCR. Finally, the findings remain consistent across a variety of robustness checks.

Practical implications

This paper provides valuable insights for regulators, standard setters, investors and auditors. For example, this research provides a deeper understanding of the economic impacts of KAMs, offering valuable insights for regulators and standard setters.

Originality/value

To the best of the authors’ knowledge, this unique paper has built upon existing research on the specific account-related KAMs disclosure by offering new insights into the impact of goodwill-related KAM disclosures from the perspective of SPCR. Moreover, the results provide new evidence contributing to the recent inconclusive literature that investigates stock market reactions to KAMs. Finally, the findings confirm standard setters’ expectations regarding the importance of KAMs reporting in enhancing the informative value of audit reports to investors.

Research Authors
Mohsen Anwar Abdelghaffar Saleh; Dejun Wu
Research Date
Research Department
Accounting Department
Research Journal
Journal of Financial Reporting and Accounting (2025)
Research Member
Mohsen Anwar Abd ELghaffar Saleh Ali
Research Publisher
Emerald Publishing
Research Rank
Scopus and Web of Science(Q1)
Research Website
https://doi.org/10.1108/JFRA-03-2025-0163
Research Year
2025
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