• Dr. Thirupathi Kanchu
• Katta Nagaraju

The integration of Artificial Intelligence (AI) into financial reporting marks a transformative shift in the accounting and finance landscape. This study explores how AI technologies—such as Machine Learning, Natural Language Processing, Robotic Process Automation, and Generative AI—are reshaping traditional financial reporting practices by enhancing accuracy, enabling real-time analysis, improving decision-making, and reducing costs. Through a qualitative and thematic analysis of recent literature and professional insights, the research identifies key opportunities offered by AI, including enhanced compliance, predictive insights, and continuous monitoring. At the same time, it highlights significant challenges, such as algorithmic bias, lack of transparency, data privacy concerns, and workforce skill gaps. The study also outlines future directions, emphasizing the development of explainable AI, stronger human-AI collaboration, integration with technologies like blockchain, and the need for regulatory alignment and ethical standards. Overall, the research contributes to the growing discourse on the digital future of financial reporting, offering insights for academics, practitioners, and policymakers seeking to balance innovation with accountability and transparency.

Artificial Intelligence (AI), Financial Reporting, Machine Learning, Robotic Process Automation (RPA), Predictive Analytics, Explainable AI

"AI-POWERED FINANCIAL REPORTING: OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 8, page no.342-346, August-2025, Available :http://www.jetir.org/papers/JETIRHC06052.pdf

"AI-POWERED FINANCIAL REPORTING: OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 8, page no. pp342-346, August-2025, Available at : http://www.jetir.org/papers/JETIRHC06052.pdf