Artificial Intelligence as A Catalyst for Change in Accounting and Finance: Exploring Applications, Opportunities and Challenges

Artificial Intelligence (AI) is changing accounting and finance by shifting technology from a back-office automation role toward a broader capability for analysis, prediction, monitoring and decision support. This paper examines how Machine Learning, Natural Language Processing, Robotic Process Automation, Optical Character Recognition, Generative AI and Explainable AI are being applied across accounting and financial activities. An integrative literature review is used to synthesise recent peer-reviewed research and authoritative professional and regulatory publications, with particular attention to developments through 2026 and the Indian context. The review indicates that AI creates value through faster transaction handling, continuous anomaly detection, improved forecasting, document analysis, risk assessment and expanded access to financial insight. At the same time, the value of AI is conditional rather than automatic. Weak data, limited explainability, privacy concerns, cybersecurity threats, biased models, inaccurate generative outputs and excessive reliance on automated recommendations can undermine the quality of financial decisions. The paper identifies a further shift in professional work: accountants and finance professionals are likely to spend less time on repetitive processing and more time on exception analysis, interpretation, governance, advisory work and strategic decision support. Based on the synthesis, the paper proposes a Human-Centred AI Transformation Model linking AI capabilities to functional applications, process outcomes, professional transformation and organisational value, with data quality, governance, human oversight and AI literacy acting as safeguards. The paper contributes an integrated view of AI across accounting and finance and argues that sustainable adoption depends on combining technological capability with professional judgement, ethical responsibility and institutional controls. The study recommends investment in data foundations, formal AI governance, continuous professional development, explainability and human review for material accounting and financial decisions.