The Impact of Blockchain-Generative AI Integration: A Study on Financial Reporting with a Special Reference to Accuracy, Efficiency, and Trust - Hossain, Md Afzal; Rana, Arif (IUBAT Review, 2026)
This qualitative study investigates the transformative potential of integrating block chain and generative AI in financial reporting, specifically examining impacts on accuracy, efficiency, and trust. Based on a comprehensive review of literature from 2020 to 2025, this paper synthesizes current academic understanding. The study aims to determine the role of block chain in ensuring data integrity and auditability, assess AI's capacity for automating processes and enhancing analytical capabilities, and explore the combined impact of these technologies on stakeholder trust. The findings indicate that block chain’s inherent immutability and transparency significantly improve the accuracy of financial data. Simultaneously, generative AI enhances efficiency by automating tasks and providing real-time insights. However, the effect on trust is complex, as block chain’s transparency contrasts with the opacity of certain AI algorithms, underscoring the need for explainable AI (XAI). Agency Theory and the Resource-Based View provide theoretical support for the argument that this integration can improve financial reporting quality and efficiency. The study emphasizes the importance of transparent and well-governed applications to fully realize the benefits of this technological convergence. Policy recommendations include the development of adaptable regulatory frameworks, the promotion of standardization, and investment in education to effectively manage this evolving technological context. IUBAT Review—A Multidisciplinary Academic Journal, 8(2): 239-263