What question did this study set out to answer?

This study aims to evaluate the impact of AI tools on financial reporting quality and fraud detection in Nigerian manufacturing firms.

June 21, 2026Open Access

Adoption of Artificial Intelligence in Accounting Practices and Its Implications for Financial Reporting

Key Points

This study aims to evaluate the impact of AI tools on financial reporting quality and fraud detection in Nigerian manufacturing firms.
Ex-post facto research design employed with a population of 53 manufacturing firms quoted on the Nigerian Exchange Group.
Ordinary least square regression used to validate hypotheses regarding AI's impact on financial reporting and fraud detection.
Beneish M-Score model applied for binary classification of firms based on financial manipulation.
No significant usage of AI tools in improving financial reporting quality (P > 0.05).
Significant effect found for AI in enhancing fraud detection, with AUC and R-square > 70%.
Recommendations for increased investment in AI technologies for fraud detection in Nigeria's financial systems.

Abstract

This study examined the adoption of artificial intelligence in accounting practices and its implications for financial reporting of manufacturing firms in Nigerian from (2013-2023). Two research objectives guided the study and corresponding two hypotheses was developed for the study. Specifically, the study focused on evaluating the usage of AI-driven tools such as machine learning and robotic process automation (RPA) in determining financial reporting quality also to assess the effectiveness of AI in enhancing fraud detection in Nigeria, using Beneich M-Score model as proxy for fraud. Ex-post facto research design was employed in the study. The population of the study included fifty-three (53) manufacturing firms quoted on the Nigerian Exchange Group (NGX) as at 31st December 2026. Amongst other preliminary analysis and tests, the ordinary least square regression was employed in validating the hypothesis one Also, a threshold was applied where any firm with an MSCORE > -2. 22 is classified as a Manipulator, while those below are Non-Manipulators". This binary classification allows AI algorithms to learn the specific financial "signatures" of fraudulent reporting in the Nigerian industrial sector. The study found no significant usage of AI-driven tools such as machine learning and robotic process automation (RPA) in determining financial reporting quality (P > 0. 05) ; The study also found a significant effect of AI in enhancing fraud detection in Nigeria, using Beneich M-Score model as proxy for fraud (AUC, R-square > 70%). Consequent on the above findings, It was therefore recommended amongst others that Nigerian firms should increase investment in the adoption and integration of AI-driven tools and that regulatory authorities, audit firms, and corporate organizations should incorporate AI-based fraud detection systems, particularly predictive models such as the Beneish M-Score and other machine learning techniques, into their monitoring and auditing frameworks as the adoption of these technologies will enhance the early detection and prevention of fraudulent financial reporting practices and strengthen the integrity of Nigeria's financial reporting environment

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