AI - Powered Financial Planning and Analysis (FP&A) Using Cloud Computing
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Research Paper | Computer Science and Information Technology | United States of America | Volume 13 Issue 7, July 2024 | Popularity: 5.1 / 10


     

AI - Powered Financial Planning and Analysis (FP&A) Using Cloud Computing

Goutham Sabbani


Abstract: In 2020, a multinational corporation, FMCG company Unilever, reduced financial forecasting errors by 30% over six months by integrating AI - powered financial planning and analysis using cloud computing. This underscores the transformative impact of AI on financial planning [5]. Over the past few years, AI - driven FP&A has evolved from essential automation tools to machine learning algorithms and cloud computing capabilities. This evolution has made businesses more accurate predictions, streamlined financial processes, and enhanced decision - making. In this paper, we will delve deeper into the critical components of AI - powered FP&A, including the role of machine learning algorithms in predictive analysis, the benefit of cloud computing in data storage and processing, and case studies demonstrating the successful implementation of these technologies. Additionally, we will discuss future trends and potential barriers to adopting AI and cloud computing with a financial planning and analysis framework.


Keywords: AI - Powered Financial Planning and Analysis (FP&A), Cloud Computing, Machine Learning Algorithms, Predictive Analysis, Data Storage, Processing


Edition: Volume 13 Issue 7, July 2024


Pages: 362 - 365


DOI: https://www.doi.org/10.21275/SR24705234246


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Goutham Sabbani, "AI - Powered Financial Planning and Analysis (FP&A) Using Cloud Computing", International Journal of Science and Research (IJSR), Volume 13 Issue 7, July 2024, pp. 362-365, https://www.ijsr.net/getabstract.php?paperid=SR24705234246, DOI: https://www.doi.org/10.21275/SR24705234246

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