Saudi-listed companies reporting greater use of AI in auditing exhibit materially less earnings manipulation and stronger audit quality. The association suggests AI can bolster corporate governance, though the evidence is observational and cannot fully exclude selection or reverse causality.
The Role of Artificial Intelligence in Audit Quality and Reducing Earnings Management
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correlational
low evidence
7/10 relevance
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In a panel of Saudi-listed firms (2015–2024), greater reported use of AI in auditing is associated with lower discretionary accruals and higher audit quality, implying improved financial transparency where AI is used.
This examines the usage of artificial intelligence (AI) in enhancing audit quality and lowering income management inside Saudi-listed corporations from 2015 to 2024. The study uses 680 corporation-accounting period observations sourced from the Bloomberg and DataStream databases, employing correlational and panel regression analyses (OLS and FGLS) to analyze the relationships amongst AI adoption, audit quality, and earnings management, with employer traits functioning as independent variables. The empirical findings display a strong negative correlation between AI use and profit control, suggesting that advanced incorporation of AI in auditing diminishes discretionary accruals and improves financial transparency. Audit is wonderful; moreover has a large, terrible impact on income manipulation, which shows that it plays a role within the interplay between AI and income manipulation. Control variables like the length and leverage of an employer have a significant impact on income management, while profitability has a negative effect. In particular, the outcomes show that AI-pushed audit techniques make governance more potent, make it less complicated to find out issues, and inspire moral reporting. The studies offer to the frame of expertise via imparting empirical statistics from a developing market, providing realistic guidance for regulators, audit agencies, and policymakers aiming to make use of AI to enhance audit effectiveness and company accountability in accordance with Saudi Vision 2030.
Summary
Main Finding
Wider adoption of AI in auditing by Saudi-listed firms (2015–2024) is strongly associated with lower earnings management. AI-enabled audit practices reduce discretionary accruals and improve financial transparency; audit quality itself has a large negative effect on earnings manipulation and acts as a mediating channel between AI use and reduced earnings management.
Key Points
- Sample: 680 firm–accounting period observations for Saudi-listed firms (2015–2024).
- Data sources: Bloomberg and DataStream.
- Primary relationships examined: AI adoption → audit quality → earnings management, with firm characteristics as controls.
- Econometric approach: correlational and panel regression analyses using OLS and FGLS.
- Main empirical patterns:
- AI use is negatively correlated with earnings management (lower discretionary accruals).
- Higher audit quality significantly reduces income manipulation and mediates the AI → earnings management relationship.
- Firm controls: firm size (or length) and leverage have significant effects on earnings management; profitability is negatively associated with earnings management (more profitable firms manage earnings less).
- Interpretive takeaway: AI-driven audit techniques strengthen governance, make problem detection easier, and encourage more ethical financial reporting.
Data & Methods
- Period: 2015–2024.
- Observations: 680 firm–year observations from Saudi-listed companies.
- Data sources: Bloomberg, DataStream.
- Variables:
- Independent variables: measures/proxies of AI adoption in audit and firm characteristics (size, leverage, profitability, etc.).
- Mediator: audit quality.
- Dependent variable: earnings management (measured via discretionary accruals).
- Analytical techniques:
- Correlational analysis to establish associations.
- Panel regressions with OLS and FGLS to account for cross-sectional and heteroskedastic/panel error structure.
- Mediation analysis to assess audit quality’s role in the AI → earnings management link.
- Robustness: results reported as robust across chosen regression specifications (details on alternative specifications or variable construction not provided here).
Implications for AI Economics
- Monitoring and agency problems: Empirical evidence that AI in auditing enhances monitoring effectiveness and reduces managerial opportunism, suggesting AI is an effective technology-driven mechanism to alleviate agency costs in corporate governance.
- Audit market and productization of AI: AI tools can materially improve audit quality; demand for AI-enabled audit services may rise, affecting competition, fee structures, and the value proposition of audit firms.
- Investment and adoption incentives: Findings support public- and private-sector incentives to adopt AI audit systems (R&D subsidies, training, or procurement standards), particularly in economies pursuing transparency and governance reforms (e.g., Saudi Vision 2030).
- Regulatory and policy considerations:
- Regulators can consider encouraging or mandating AI-based audit enhancements to improve financial reporting quality.
- Oversight frameworks should address model validation, transparency, and accountability of AI audit tools.
- Policies should consider complementarities: AI adoption paired with stronger audit standards and corporate governance reforms yields larger reductions in earnings manipulation.
- Distributional and labor effects: Increased AI use in audit likely shifts auditor tasks toward oversight, interpretation, and tool maintenance—implications for auditor skill demand, training, and labor reallocation should be evaluated.
- Limitations and directions for future research:
- Causality: observational correlational design limits causal claims; future studies could use quasi-experimental or instrumental-variable approaches.
- Measurement: clearer, standardized measures of firm-level AI adoption and of specific AI tools would improve precision.
- External validity: results are specific to Saudi-listed firms; replication in other developing and developed markets is needed.
- Further work: examine heterogeneous effects by firm size/industry, costs of AI adoption, interplay with data privacy/regulatory constraints, and long-term impacts on audit fees and market outcomes.
Summary: This study provides empirical support that AI adoption in auditing improves audit quality and reduces earnings management in a developing-market context, with important implications for monitoring, policy, and the economics of AI in financial oversight.
Assessment
Paper Typecorrelational
Evidence Strengthlow — The paper reports robust correlations from panel OLS and FGLS but does not present a clear causal identification strategy (no exogenous variation, instruments, difference-in-differences, or randomized variation). Potential endogeneity (reverse causality, omitted variables, selection into AI adoption) is not convincingly addressed, so causal claims are weak.
Methods Rigormedium — The study uses a reasonable sample (680 firm-year observations) and appropriate baseline techniques for observational panel data (OLS and FGLS) and includes relevant controls and an audit-quality mediator, which lends credibility to associations; however, the absence of stronger panel methods (e.g., firm fixed effects explicitly reported), robustness checks addressing endogeneity, and detailed measurement validation of AI adoption limits methodological rigor.
Sample680 firm–accounting period observations of Saudi-listed corporations covering 2015–2024, with data drawn from Bloomberg and Datastream; variables include a measure of AI adoption in audit processes, audit quality indicators, discretionary accruals (earnings management), and firm-level controls (size, leverage, profitability, audit characteristics).
Themesgovernance adoption
GeneralizabilitySingle-country study (Saudi Arabia) — institutional, regulatory, and cultural context may limit transferability to other countries., Listed firms only — excludes private firms and SMEs, which may differ in AI adoption and governance., 2015–2024 period — results may reflect contemporaneous regulatory or technological trends specific to the decade., AI adoption measurement likely coarse/observational — potential measurement error limits external validity to other measures of AI use., Observational design limits causal generalization to policy interventions promoting AI adoption.
Claims (8)
| Claim | Direction | Outcome | Confidence & Evidence | Details |
|---|---|---|---|---|
| The study uses 680 firm–accounting period observations from Bloomberg and DataStream covering Saudi-listed firms for 2015–2024. Other | null_result | sample and data coverage |
Reading fidelity
high
Study strength
high
|
n=680
|
| The empirical analysis employs correlational methods and panel regressions, specifically OLS and FGLS, to examine relationships among AI adoption, audit quality, and earnings management. Other | null_result | methodological approach |
Reading fidelity
high
Study strength
high
|
n=680
|
| AI adoption is strongly negatively correlated with earnings management (discretionary accruals): greater AI use in auditing is associated with lower discretionary accruals. Output Quality | negative | earnings management (discretionary accruals) |
Reading fidelity
high
Study strength
medium
|
n=680
|
| Audit quality has a large negative effect on earnings manipulation and plays a role in the relationship between AI adoption and earnings management (i.e., audit mediates/moderates this relationship). Output Quality | negative | earnings management (discretionary accruals) |
Reading fidelity
medium
Study strength
medium
|
n=680
|
| Firm characteristics such as firm age (length) and leverage have a statistically significant impact on earnings management. Output Quality | mixed | earnings management (discretionary accruals) |
Reading fidelity
medium
Study strength
medium
|
n=680
|
| Profitability has a negative effect on earnings management (more profitable firms engage in less earnings manipulation). Output Quality | negative | earnings management (discretionary accruals) |
Reading fidelity
high
Study strength
medium
|
n=680
|
| AI-driven audit techniques strengthen corporate governance, make it easier to detect problems, and encourage ethical financial reporting. Governance And Regulation | positive | financial transparency / governance effectiveness |
Reading fidelity
medium
Study strength
speculative
|
n=680
|
| The study contributes empirical evidence from a developing market (Saudi Arabia) that can guide regulators, audit firms, and policymakers seeking to leverage AI to improve audit effectiveness and corporate accountability in line with Saudi Vision 2030. Governance And Regulation | positive | policy relevance / applicability |
Reading fidelity
high
Study strength
low
|
n=680
|