How is generative artificial intelligence shaping the future of finance, accounting and investments in listed firms?
Samuel O. Onyuma () and
Anne L. Bulimu ()
Journal of Accounting, Business and Finance Research, 2025, vol. 20, issue 1, 37-50
Abstract:
Generative artificial intelligence, machine learning, and blockchain technologies are rapidly transforming various industries, and finance, accounting, and investment fields are no exception, as they can affect how financial data is analyzed and interpreted. This paper discusses these innovative trends and their implications for listed companies. Guided by technology acceptance theory and technology readiness theory, the paper employed an integrative review methodology to identify emerging constructs related to these three fields. These technologies can revolutionize finance, accounting, and investment processes by enhancing big data analytics, accuracy, efficiency, fraud detection, risk assessment, forecasting, reporting, client engagement, and the identification of functional anomalies. Experts can then focus on value-added tasks and improved collaboration among professionals. Adoption of these technologies can enable listed firms to streamline financial operations, automate repetitive tasks, and focus on strategic financial decision-making processes. Data analytics and predictive modeling enable the extraction of valuable insights from large datasets, facilitating proactive financial decision-making and providing value-added services to clients. Practitioners must upskill to adapt to new technologies, become strategic advisors, and embrace sustainable practices to drive business success in such a dynamic environment. While financial regulators must review their policies, universities and professional development bodies need to redesign their training curricula.
Keywords: Blockchain technology; Deep learning; FinTech; Generative artificial intelligence; Large language models; Machine learning. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spi:joabfr:v:20:y:2025:i:1:p:37-50:id:945
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