Firms that disclose using AI in financial reporting show better audit outcomes in Nigeria — fewer restatements and audit-fee patterns consistent with higher audit quality — partly because AI disclosure correlates with greater transparency and stronger internal controls.

Artificial Intelligence Adoption in Financial Reporting and Audit Quality: Evidence from Nigerian Listed Firms

Akomolehin Fo, Oluwaremi Jb, Aluko Or, Famoroti Jo · Fetched April 22, 2026 · International Journal of Advanced Multidisciplinary Research and Studies

semantic_scholar correlational medium evidence 7/10 relevance DOI Source PDF

Using disclosure-based measures of AI adoption in Nigerian listed firms, the study finds AI adoption is positively associated with audit quality, with part of the effect mediated by improved reporting transparency and internal control quality.

This study examines the effect of artificial intelligence (AI) adoption in financial reporting on audit quality in an emerging market context, using evidence from Nigerian listed firms. Drawing on the governance substitution perspective, complemented by institutional and technology diffusion theories, the study argues that AI-enabled reporting systems enhance audit quality by strengthening firm-level governance mechanisms. Using a mixed-method quantitative design, the study combines content analysis of corporate annual reports with archival audit data and applies Structural Equation Modeling (SEM) to test both direct and indirect relationships. AI adoption is operationalized through a disclosure-based AI adoption index, while audit quality is modeled as a latent construct reflected by financial restatements and audit fees. The results reveal that AI adoption is positively associated with audit quality and that this relationship is partially mediated by improvements in reporting transparency and internal control quality. The These findings suggest that AI adoption enhances the reliability of financial reporting and the effectiveness of audits by reducing information asymmetry and strengthening internal monitoring processes. The study contributes to the literature by providing firm-level evidence from an underexplored emerging market setting, advancing methodological approaches to measuring AI adoption, and offering policy-relevant insights for regulators, audit firms, and corporate boards. Overall, the findings highlight AI adoption in financial reporting as a governance-enhancing mechanism with significant implications for audit quality in emerging economies.

Summary

Main Finding

AI adoption in financial reporting by Nigerian listed firms is positively associated with higher audit quality. This relationship is partially mediated by improvements in reporting transparency and internal control quality. The study interprets AI-enabled reporting systems as governance-enhancing substitutes in an emerging-market environment with weak enforcement.

Key Points

Theoretical framing: combines Governance Substitution Perspective with Institutional Theory and Technology Diffusion Theory to explain why firms adopt AI and how it affects audit outcomes.
Mechanisms: AI improves data validation, anomaly detection and continuous monitoring → increases reporting transparency and strengthens internal controls → reduces information asymmetry and the likelihood of restatements.
Audit quality measurement: treated as a latent, multidimensional construct (reflected by financial restatements and audit fees) rather than a single proxy.
Methodological contribution: uses content analysis of annual reports to build a disclosure-based AI adoption index and applies Structural Equation Modeling (SEM) to estimate direct and indirect effects.
Contextual relevance: Nigeria is used as a “critical case” because formal IFRS convergence coexists with weak enforcement, making technological governance substitutes particularly salient.
Risks noted: algorithmic bias, opacity of AI models, and potential widening of reporting/audit-quality disparities across firms with different resource levels.

Data & Methods

Design: mixed-method quantitative approach combining content analysis and archival data.
AI adoption operationalization: disclosure-based AI adoption index derived from annual-report narratives (captures presence/maturity of AI tools in reporting processes).
Audit quality proxies: financial restatements (ex post indicator of misstatements) and audit fees (proxy for audit effort/risk); modeled jointly as a latent audit-quality construct.
Empirical strategy: Structural Equation Modeling (SEM) to estimate direct effect of AI adoption on audit quality and indirect effects via reporting transparency and internal control quality.
Setting/sample: firm-level data from Nigerian listed firms (paper emphasizes cross-sectional variation, including Big Four vs non-Big Four auditors); specific sample size/years not reported in the excerpt.

Implications for AI Economics

Information asymmetry and market efficiency: AI in reporting can reduce information frictions, improving the credibility of financial statements and potentially lowering the cost of capital for better-disclosing firms.
Agency and governance economics: AI acts as a technological governance substitute in weak-institution settings, altering the balance between private internal monitoring and public enforcement.
Audit market effects: AI-enabled reporting may change audit demand and pricing—by lowering restatement risk it can reduce some audit risk premia, but complexity/new audit tasks may shift effort and fee structures (audit-fee proxy interpretation is context-dependent).
Distributional consequences: AI adoption is likely uneven (larger/more resourced firms adopt earlier), which can widen heterogeneity in reporting quality and market outcomes across firms and sectors.
Policy and regulation: regulators should promote standardized AI-related disclosures, encourage auditor preparedness for algorithmic systems, and strengthen oversight frameworks for AI transparency and validation to realize governance benefits while mitigating risks.
Research methods for AI economics: supports use of disclosure-based indices and latent-variable methods (e.g., SEM) to study AI impacts when direct adoption metrics are unavailable; highlights need for longitudinal and causal designs to address endogeneity.
Open questions for economics research: causal impact of AI on audit fees and long-run auditor behavior; cross-country comparisons of AI as governance substitute; welfare implications of unequal AI diffusion in capital markets.

Limitations noted by the study (implicit): reliance on disclosure narratives (variation/standardization issues), potential measurement limits of proxies (audit fees/restatements), and the need for stronger causal identification in future work.

Assessment

Paper Typecorrelational Evidence Strengthmedium — Uses firm-level archival data, a multi-item disclosure-based AI index, and SEM to model latent audit quality and mediation — providing coherent, multi-measure evidence — but inference is observational with no exogenous source of variation, leaving open endogeneity (omitted variables, reverse causality) and measurement-error concerns. Methods Rigormedium — Methodological strengths include mixed methods (content analysis) to construct an AI adoption measure, use of archival audit metrics, and SEM to handle latent constructs and mediation; weaknesses include potential subjectivity or mismeasurement in disclosure coding, possible common-method bias, unclear robustness checks for endogeneity, and no quasi-experimental identification strategy. SamplePublicly listed Nigerian firms (annual reports coded for AI disclosures) combined with archival audit data (financial restatements and audit fees); exact sample size and years not specified in the summary, but the design uses firm-level annual reports and audit filings from Nigerian listed companies. Themesgovernance adoption IdentificationAssociational analysis: AI adoption proxied by a disclosure-based index from annual reports, linked to a latent audit-quality construct (restatements and audit fees) using Structural Equation Modeling (SEM) to estimate direct and mediated effects; identification rests on covariate adjustment and SEM assumptions rather than exogenous variation or quasi-experimental techniques. GeneralizabilitySingle-country study in an emerging-market (Nigeria) — results may not generalize to developed markets with different regulatory and audit environments, Sample limited to listed firms — excludes private firms and smaller companies, Disclosure-based AI measure may not reflect actual AI usage intensity or quality, and disclosure practices vary across firms and contexts, Potential industry composition effects — findings may be driven by sector-specific reporting/audit norms, Observational design limits causal extrapolation to other institutional contexts

Claims (7)

Claim	Direction	Confidence	Outcome	Details
AI adoption is positively associated with audit quality in Nigerian listed firms. Output Quality	positive	high	audit quality (latent construct reflected by financial restatements and audit fees)	0.3
The positive relationship between AI adoption and audit quality is partially mediated by improvements in reporting transparency. Output Quality	positive	high	audit quality (mediated by reporting transparency)	0.3
The positive relationship between AI adoption and audit quality is partially mediated by improvements in internal control quality. Output Quality	positive	high	audit quality (mediated by internal control quality)	0.3
AI-enabled reporting systems strengthen firm-level governance mechanisms (e.g., reporting transparency and internal controls), which enhances audit quality (governance substitution perspective complemented by institutional and technology diffusion theories). Output Quality	positive	medium	firm-level governance mechanisms (reporting transparency, internal control quality) and resulting audit quality	0.18
The study operationalizes AI adoption using a disclosure-based AI adoption index, representing a methodological advancement for measuring firm-level AI adoption in financial reporting. Adoption Rate	positive	high	AI adoption (measurement / adoption index)	0.3
AI adoption enhances the reliability of financial reporting and the effectiveness of audits by reducing information asymmetry and strengthening internal monitoring processes. Output Quality	positive	medium	financial reporting reliability and audit effectiveness (via reduced information asymmetry, improved internal monitoring)	0.18
The paper provides firm-level empirical evidence from an underexplored emerging market context (Nigerian listed firms) on the relationship between AI adoption in financial reporting and audit quality. Other	positive	high	contextual evidence (country-level / sample scope) for AI adoption effects	0.3