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Effect of Artificial Intelligence Adoption on the Quality of Financial Reporting in Nigerian Deposit Money Banks

Abstract

The rapid advancement of Artificial Intelligence (AI) has transformed the global financial services industry by revolutionizing accounting, auditing, financial reporting, and decision-making processes. Artificial Intelligence technologies—including machine learning, robotic process automation (RPA), natural language processing, predictive analytics, intelligent data mining, and cognitive computing—have increasingly been integrated into banking operations to improve efficiency, enhance accuracy, automate routine accounting tasks, and strengthen internal control systems. In Nigeria, Deposit Money Banks (DMBs) have accelerated the adoption of AI-driven technologies in response to increasing competition, digital transformation, regulatory demands, cybersecurity challenges, and the growing volume of financial transactions. AI-powered accounting and financial reporting systems enable banks to process large volumes of financial data in real time, minimize human errors, improve data integrity, strengthen fraud detection mechanisms, automate financial statement preparation, and facilitate compliance with accounting standards and regulatory reporting requirements. Despite these potential benefits, concerns remain regarding the extent to which Artificial Intelligence adoption translates into higher-quality financial reporting within the Nigerian banking sector. Issues relating to technological infrastructure, data quality, cybersecurity risks, algorithmic bias, implementation costs, regulatory uncertainty, and the availability of skilled personnel continue to influence the effectiveness of AI deployment in financial reporting processes. Consequently, there is an increasing need to examine the impact of Artificial Intelligence adoption on the quality of financial reporting among Nigerian Deposit Money Banks. Against this background, this study investigates the effect of Artificial Intelligence adoption on the quality of financial reporting in Nigerian Deposit Money Banks.The study is anchored on the Technology Acceptance Model (TAM), the Resource-Based View (RBV), and Systems Theory. The Technology Acceptance Model explains how perceived usefulness and perceived ease of use influence the adoption and utilization of Artificial Intelligence technologies within accounting and financial reporting functions. The Resource-Based View posits that AI capabilities constitute valuable strategic resources capable of improving operational efficiency, information quality, innovation, and organizational performance. Systems Theory views financial reporting as an integrated organizational system in which technological innovations, accounting processes, human resources, and internal controls interact to produce reliable financial information. Collectively, these theoretical perspectives provide a comprehensive framework for understanding the relationship between Artificial Intelligence adoption and financial reporting quality.The study adopts a quantitative research design utilizing a structured questionnaire administered to accountants, finance managers, chief financial officers, internal auditors, external auditors, compliance officers, information technology specialists, risk managers, financial controllers, and senior management personnel in selected Deposit Money Banks operating in Nigeria. A stratified random sampling technique will be employed to ensure adequate representation of respondents across the banking industry. Primary data collected from respondents will be analyzed using descriptive statistics to summarize respondents' demographic characteristics and perceptions regarding AI adoption and financial reporting practices. Structural Equation Modeling (SEM) will be employed to examine the relationship between Artificial Intelligence adoption and financial reporting quality. The measurement model will be evaluated using reliability and validity tests, including Cronbach's Alpha, Composite Reliability (CR), Average Variance Extracted (AVE), and Confirmatory Factor Analysis (CFA). 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 robustness, validity, and reliability of the empirical findings.The study anticipates that Artificial Intelligence adoption will have a significant positive effect on the quality of financial reporting in Nigerian Deposit Money Banks. AI technologies are expected to improve the accuracy, completeness, timeliness, consistency, comparability, and reliability of financial reports by automating accounting processes, reducing manual processing errors, enhancing transaction recording, strengthening internal control systems, and improving financial data analysis. AI-powered systems are also anticipated to facilitate real-time financial reporting, improve compliance with International Financial Reporting Standards (IFRS) and regulatory reporting requirements, enhance fraud detection capabilities, and support informed managerial decision-making. Furthermore, AI adoption is expected to improve transparency, reduce information asymmetry, strengthen investor confidence, enhance audit readiness, and increase the credibility of published financial statements. Consequently, banks that successfully integrate Artificial Intelligence into their accounting and financial reporting systems are expected to produce higher-quality financial reports than institutions relying predominantly on conventional accounting processes.This study is expected to make significant theoretical and empirical contributions to the literature on accounting, financial reporting, banking, financial technology, and information systems by providing comprehensive evidence on the relationship between Artificial Intelligence adoption and financial reporting quality within the Nigerian banking sector. Unlike previous studies that primarily examined AI adoption in relation to operational efficiency, fraud detection, or audit quality, this research specifically investigates its implications for financial reporting quality in Deposit Money Banks. The findings will provide valuable insights for bank executives, accountants, auditors, regulators, policymakers, the Central Bank of Nigeria (CBN), the Financial Reporting Council of Nigeria (FRCN), the Nigeria Deposit Insurance Corporation (NDIC), the Nigerian Exchange Group (NGX), financial technology providers, professional accounting bodies, investors, and academic researchers regarding the strategic importance of Artificial Intelligence in enhancing the credibility and usefulness of financial reporting. The study will also provide evidence-based recommendations for strengthening AI integration, improving digital accounting infrastructure, enhancing professional capacity development, reinforcing regulatory oversight, and promoting high-quality financial reporting practices that support financial stability, corporate governance, and investor confidence within Nigeria's banking industry.

Keywords: Artificial Intelligence adoption, financial reporting quality, Deposit Money Banks, accounting information systems, financial reporting, banking technology, digital transformation, Structural Equation Modeling (SEM).