Predictive Budgeting and Planning with AI in Oracle EPM: Automating Financial Projections

The integration of Artificial Intelligence (AI) into financial planning processes has revolutionized traditional budgeting and forecasting methodologies. This paper delves into the application of AI-driven predictive models within Oracle's Enterprise Planning and Budgeting Cloud Service (EPBCS) to automate financial projections, thereby fostering dynamic and adaptive budgeting frameworks. Through a comprehensive analysis, we contrast traditional budgeting approaches with AI-enhanced methods, explore the core functionalities and limitations of EPBCS, and examine the implementation of AI-powered predictive models. We also address the challenges associated with AI adoption in financial planning and propose best practices for seamless integration. Our findings suggest that while AI integration offers substantial improvements in forecasting accuracy and operational efficiency, careful consideration of data governance, model interpretability, and compliance is imperative for successful implementation.

Authors

Publication Details

Journal
Journal of Electrical Systems
Published
2025-03-04
DOI
https://doi.org/10.52783/jes.8361
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

Predictive Budgeting and Planning with AI in Oracle EPM: Automating Financial Projections

Ramsundernag Changalva
Journal of Electrical Systems
Big Data and Business Intelligence
article

Predictive Budgeting and Planning with AI in Oracle EPM: Automating Financial Projections

Ramsundernag Changalva
article en

Abstract

The integration of Artificial Intelligence (AI) into financial planning processes has revolutionized traditional budgeting and forecasting methodologies. This paper delves into the application of AI-driven predictive models within Oracle's Enterprise Planning and Budgeting Cloud Service (EPBCS) to automate financial projections, thereby fostering dynamic and adaptive budgeting frameworks. Through a comprehensive analysis, we contrast traditional budgeting approaches with AI-enhanced methods, explore the core functionalities and limitations of EPBCS, and examine the implementation of AI-powered predictive models. We also address the challenges associated with AI adoption in financial planning and propose best practices for seamless integration. Our findings suggest that while AI integration offers substantial improvements in forecasting accuracy and operational efficiency, careful consideration of data governance, model interpretability, and compliance is imperative for successful implementation.

Journal of Electrical SystemsVol. 20(11s)
Openalex Percentile: Top 94%
Big Data and Business Intelligence
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