The impact of artificial intelligence on financial reporting and compliance: Opportunities, challenges, and ethical considerations

Apooyin, Agboola (2025) The impact of artificial intelligence on financial reporting and compliance: Opportunities, challenges, and ethical considerations. International Journal of Science and Research Archive, 15 (1). pp. 914-926. ISSN 2582-8185

Artificial Intelligence (AI) is transforming financial reporting and compliance by automating processes, improving accuracy, and enhancing decision-making. AI-driven technologies such as machine learning, natural language processing, and robotic process automation enable real-time data analysis, fraud detection, and regulatory adherence. These innovations reduce human error, streamline operations, and increase transparency in financial reporting. However, AI integration presents challenges, including data security risks, algorithmic bias, and regulatory uncertainties. The lack of standardized AI governance frameworks raises concerns about accountability and ethical decision-making in financial reporting. Additionally, AI-driven financial models may unintentionally reinforce biases, leading to potential compliance violations and reputational risks. Ethical considerations, such as fairness, transparency, and data privacy, must be addressed to ensure responsible AI adoption. Organizations must implement robust AI governance, conduct regular audits, and ensure human oversight in AI-driven financial processes. The role of regulatory bodies is crucial in establishing guidelines that balance innovation with compliance requirements. Despite the challenges, AI offers immense opportunities for financial reporting, including predictive analytics for risk assessment, automated compliance monitoring, and enhanced fraud detection mechanisms. By leveraging AI responsibly, organizations can achieve greater efficiency, accuracy, and compliance in financial reporting. Future research should focus on developing standardized AI regulatory frameworks, mitigating bias in AI-driven financial models, and enhancing AI transparency.