Leveraging Generative AI for Real-Time Financial Forecasting Accuracy in Cloud ERP Environments

Authors

  • Harshini Gadam, Abhishek Upadhyay, Subhankar Panda

Keywords:

Generative AI, Cloud ERP, Financial Forecasting, Real-Time Analytics, Probabilistic Models, Hybrid Architectures

Abstract

The integration of generative artificial intelligence (AI) into cloud-based Enterprise Resource Planning (ERP) systems has revolutionized real-time financial forecasting by addressing the limitations of traditional statistical models. This paper examines the technical frameworks, integration methodologies, and performance enhancements achieved through generative AI models such as Transformers, Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) in cloud ERP architectures. By optimizing data pipelines, reducing latency, and enhancing scalability, generative AI demonstrates a 24.3% improvement in forecasting accuracy (measured by RMSE) compared to classical methods. The study also evaluates compliance challenges, ethical risks, and emerging trends such as quantum-inspired AI and federated learning.

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Published

05.04.2024

How to Cite

Harshini Gadam. (2024). Leveraging Generative AI for Real-Time Financial Forecasting Accuracy in Cloud ERP Environments. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 4943 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7496

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

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