Leveraging Artificial Intelligence For Enhanced Financial Data Analysis: Implications For Accounting Transparency And Risk Management

Abstract

This study examines the transformative role of artificial intelligence (AI) in enhancing financial data analysis, accounting transparency, and risk management within the Nigerian Stock Exchange (NSE) context. Using data collected from 2014 to 2023, the study employs logistic regression analysis to explore the relationships between financial misreporting and key variables, including leverage ratio (LR), return on assets (ROA), revenue growth (RG), AI-Generated anomaly scores (AIGAS), audit quality (AUQ), corporate governance (CORG), board independence (BInD), and firm size (lnFSIZE). The dependent variable, financial misreporting, is measured through the earnings management index (EMI), which captures the extent of earnings manipulation through accruals. The findings reveal critical insights into the dynamics of financial reporting and governance. A significant inverse relationship between leverage and financial misreporting suggests that higher leverage reduces managerial opportunism through enhanced creditor scrutiny. Conversely, the study highlights that firms with higher ROA are more likely to engage in financial misreporting, driven by profitability pressures. AIGAS effectively detect irregularities, emphasizing AI's pivotal role in mitigating risks and ensuring transparency. The results also underscore the significance of firm size, as larger firms exhibit lower tendencies toward financial misreporting, reflecting better governance and regulatory oversight. The study integrates theoretical perspectives from agency theory, information asymmetry theory, and risk management theory to contextualize its findings. It argues that AI-powered tools can bridge principal-agent gaps, reduce informational disparities, and enhance risk prediction, ultimately fostering a robust framework for financial accountability. By synthesizing empirical results with theoretical insights, this research provides a compelling case for adopting AI-driven solutions in financial data analysis. The implications extend to policymakers, auditors, and corporate stakeholders, offering actionable strategies for leveraging AI to promote financial integrity, accountability, and governance excellence. This study contributes to the literature by demonstrating the practical and theoretical significance of AI in addressing challenges in financial reporting and governance, particularly in emerging economies.