International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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The Synergy of Generative AI and Big Data for Financial Risk: Review of Recent Developments

Author(s) Satyadhar Joshi
Country United States
Abstract This paper presents a comprehensive review of the latest development in Generative AI and Big Data with application in Finance. 2025 is the year of Agentic AI, marking a pivotal shift in generative AI (Gen AI) and its integration with big data. This paper explores the synergies between Gen AI and big data, particularly in financial risk management, proposing strategies for deeper integration. By citing the latest white papers and focusing on developments over the past two years, we expand the scope of research in this transformative domain. Recent advancements in Gen AI, such as GPT-4, VAE-GANs, and explainable AI architectures, demonstrate significant improvements, including 25–40% gains in workflow efficiency and 18–30% reductions in error margins for financial and enterprise systems (based on current literature). We segment these developments to identify potential applications in market and credit risk, while addressing challenges such as the lack of universal Python full-stack architectures. To bridge this gap, we propose recommendations for seamless communication between Gen AI and big data systems, enabling scalable solutions and actionable insights. This paper underscores the role of Gen AI in transforming big data into operational excellence, setting the stage for future innovations in financial and enterprise analytics.
Keywords Gen AI, Big Data, Financial Risk, Synthetic Data, Gae Van models
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-01-19
Cite This The Synergy of Generative AI and Big Data for Financial Risk: Review of Recent Developments - Satyadhar Joshi - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.35488
DOI https://doi.org/10.36948/ijfmr.2025.v07i01.35488
Short DOI https://doi.org/g82gmx
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