Artificial Intelligence in Finance and Accounting: A Critical Review of Applications, Transformative Impact, Challenges and Future Research Directions

Artificial Intelligence (AI) is progressively reshaping the conceptual and operational foundations of finance and accounting. What began primarily as the automation of routine and rule-based processes has evolved into the application of machine learning, deep learning, natural language processing, predictive analytics and intelligent automation to increasingly complex financial and accounting decisions. Against this background, the present paper critically reviews the literature on the impact of AI on finance and accounting, with particular attention to financial forecasting, bankruptcy prediction, financial statement analysis, auditing, fraud detection, credit-risk assessment, investment analysis, organizational performance and professional transformation. The review demonstrates that AI has considerable potential to enhance computational efficiency, predictive capability, anomaly detection and decision support. However, the literature does not establish technological superiority as an automatic consequence of AI adoption. Rather, the effectiveness of AI is contingent upon data quality, model selection, organizational readiness, employee competencies, governance arrangements and the ability of professionals to interpret algorithmic outputs. An important theme emerging from the literature is the transition of accounting and finance from predominantly retrospective and transaction-oriented functions toward more predictive, analytical and strategically embedded roles. At the same time, increased reliance on algorithmic decision-making introduces concerns relating to explainability, bias, data privacy, cybersecurity, professional accountability and regulatory oversight. The review therefore argues that the long-term significance of AI lies less in the replacement of finance and accounting professionals than in the reconfiguration of professional work through human-AI complementarity. The paper concludes by identifying gaps concerning implementation, longitudinal organizational outcomes, explainable AI, professional competencies and emerging-economy contexts, thereby providing directions for future research.