Artificial Intelligence Adoption and Financial Reporting Quality: Evidence from Listed Consumer Goods Firms in Nigeria

Abstract

This study examined the relationship between Artificial Intelligence (AI)-powered accounting automation, AI-driven financial analytics, earnings quality, and accrual quality among listed firms in Nigeria. The increasing integration of artificial intelligence into accounting and financial reporting processes has heightened the need to determine whether AI-enabled technologies contribute to the quality and reliability of reported financial information. The study adopted an ex post facto research design and utilised secondary data from the audited annual reports of selected listed firms in Nigeria on the Nigerian Exchange Group (NGX) covering the period from 2020 to 2025. A census approach was employed, including all firms that satisfied the study's inclusion criteria. AI-powered accounting automation and AI-driven financial analytics served as the explanatory variables, while earnings quality and accrual quality were the dependent variables. Data were analysed using descriptive statistics, Pearson Product-Moment Correlation, Variance Inflation Factor (VIF), the Hausman specification test, and Fixed Effects panel regression. The findings revealed that AI-powered accounting automation had a positive and statistically significant relationship with earnings quality among listed firms in Nigeria. The study found that Artificial Intelligence Adoption (AIA) has a positive and statistically significant effect on Financial Reporting Quality (FRQ) among listed consumer goods firms in Nigeria. The Fixed Effects regression produced a coefficient of 0.087 (p < 0.001), indicating that greater AI adoption is associated with improved financial reporting quality. The control variables also produced significant results. Firm size, return on assets, and audit quality had positive and significant relationships with financial reporting quality, while leverage had a negative and significant relationship. The regression model explained 73.1% of the variation in financial reporting quality (R² = 0.731), and the overall model was statistically significant (F = 25.84, p = 0.000). The findings indicate that greater adoption of AI is associated with higher-quality financial reporting among listed consumer goods firms in Nigeria, with firm characteristics and audit quality also contributing to reporting outcomes. It was recommended, among others, that larger and smaller listed firms should ensure that their organisational structures, accounting departments, internal control systems, and financial reporting processes are sufficiently developed to support accurate and reliable reporting.