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Effect of Artificial Intelligence on Audit Quality in Nigerian Audit Firms

Abstract

Artificial Intelligence (AI) has emerged as one of the most transformative technologies reshaping the accounting and auditing profession by enhancing the efficiency, accuracy, and effectiveness of audit processes. AI technologies—including machine learning, natural language processing, robotic process automation (RPA), intelligent data analytics, predictive analytics, computer vision, and expert systems—enable auditors to automate routine audit tasks, analyze large volumes of financial data, identify anomalies, assess risks, and generate real-time audit insights. Unlike traditional audit approaches that rely heavily on manual sampling and periodic testing, AI-powered auditing facilitates continuous auditing, comprehensive transaction analysis, fraud detection, and improved professional judgment through advanced analytical capabilities. In Nigeria, audit firms are increasingly adopting AI-driven audit technologies in response to growing client expectations, digital transformation, evolving regulatory requirements, and the increasing complexity of financial reporting and business operations. The adoption of AI is expected to improve audit quality by strengthening audit planning, enhancing audit evidence collection, increasing the accuracy of risk assessment, improving fraud detection, reducing audit errors, and supporting more informed audit opinions. However, the implementation of AI in auditing also presents challenges, including high acquisition and implementation costs, inadequate technological infrastructure, cybersecurity risks, limited technical expertise, ethical concerns, data privacy issues, and resistance to organizational change. Furthermore, empirical evidence regarding the effect of Artificial Intelligence on audit quality among Nigerian audit firms remains limited and inconclusive, particularly within the context of developing economies. Against this background, this study investigates the effect of Artificial Intelligence on audit quality in Nigerian audit firms. The study is anchored on the Technology Acceptance Model (TAM), the Resource-Based View (RBV), and the DeLone and McLean Information Systems Success Model. The Technology Acceptance Model explains that audit professionals are more likely to adopt AI technologies when they perceive them as useful in improving audit efficiency and easy to integrate into existing audit processes. The Resource-Based View posits that AI capabilities constitute valuable strategic resources capable of enhancing organizational competence, innovation, and sustainable competitive advantage within audit firms. The DeLone and McLean Information Systems Success Model argues that high-quality information systems improve system quality, information quality, user satisfaction, and organizational performance, thereby enhancing overall audit quality. Collectively, these theoretical perspectives provide a comprehensive framework for explaining the relationship between Artificial Intelligence adoption and audit quality in Nigerian audit firms. The study adopts a quantitative research design using a structured questionnaire administered to external auditors, audit partners, audit managers, senior auditors, information technology auditors, forensic accountants, quality assurance officers, and other professionals working in selected audit firms across Nigeria. A stratified random sampling technique will be employed to ensure adequate representation of respondents from international audit firms, national audit firms, and medium-sized indigenous audit firms. Primary data collected from respondents will be analyzed using descriptive statistics to summarize respondents' demographic characteristics and perceptions regarding Artificial Intelligence adoption and audit quality. Structural Equation Modeling (SEM) will be employed to examine the effect of Artificial Intelligence on audit quality. The measurement model will be evaluated using Cronbach's Alpha, Composite Reliability (CR), Average Variance Extracted (AVE), and Confirmatory Factor Analysis (CFA) to establish the reliability and validity of the research instrument. Additional diagnostic tests, including multicollinearity assessment, common method bias analysis, and model fit indices such as the Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR), will be conducted to ensure the adequacy, consistency, reliability, and robustness of the structural model. The study anticipates that Artificial Intelligence will have a significant positive effect on audit quality in Nigerian audit firms. The adoption of AI technologies is expected to improve audit efficiency by automating repetitive audit procedures, facilitating continuous auditing, enhancing transaction testing, and reducing human errors associated with manual audit processes. AI-powered analytical tools are also anticipated to strengthen risk assessment, improve fraud detection, identify unusual financial transactions, enhance audit evidence collection, and support more accurate professional judgments. Furthermore, Artificial Intelligence is expected to improve audit documentation, increase compliance with International Standards on Auditing (ISAs), strengthen quality control processes, reduce audit completion time, and enhance the credibility and reliability of audit opinions. Audit firms effectively integrating AI into their audit methodologies are therefore expected to achieve higher audit quality, greater operational efficiency, stronger client confidence, improved regulatory compliance, and enhanced competitive advantage. Conversely, inadequate technological infrastructure, insufficient AI expertise, weak cybersecurity controls, limited investment in digital technologies, and resistance to innovation may constrain the effective utilization of AI and reduce its potential benefits for audit quality. Consequently, effective adoption of Artificial Intelligence is expected to contribute significantly to improving audit quality, strengthening financial reporting credibility, and enhancing the overall effectiveness of the auditing profession in Nigeria. This study is expected to make significant theoretical and empirical contributions to the literature on auditing, accounting information systems, artificial intelligence, and professional accounting practice by providing robust evidence on the relationship between Artificial Intelligence adoption and audit quality in Nigerian audit firms. Unlike previous studies that focused primarily on computer-assisted auditing techniques or general information technology adoption, this research provides a comprehensive evaluation of AI as a strategic technological capability influencing audit quality using primary data and Structural Equation Modeling (SEM). The findings will provide valuable insights for the Financial Reporting Council of Nigeria (FRCN), the Institute of Chartered Accountants of Nigeria (ICAN), the Association of National Accountants of Nigeria (ANAN), audit firms, professional accounting bodies, regulators, policymakers, technology providers, and academic researchers regarding the strategic importance of Artificial Intelligence in improving audit effectiveness and strengthening public confidence in audited financial statements. The study will also provide evidence-based recommendations for expanding AI adoption, enhancing digital audit competencies, strengthening cybersecurity governance, improving professional training, promoting investment in advanced audit technologies, reinforcing compliance with auditing standards, and fostering sustainable innovation within Nigeria's auditing profession.

Keywords: Artificial Intelligence, audit quality, audit firms, auditing, machine learning, robotic process automation (RPA), accounting information systems, Structural Equation Modeling (SEM), International Standards on Auditing (ISAs), Nigeria.