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Financial Planning and Analysis (FP&A) has evolved from traditional budgeting and reporting to more dynamic and sophisticated approaches by integrating advanced analytics techniques like Machine Learning and Artificial Intelligence. This transformation has made FP&A highly efficient, enabling data -driven decision -making and positioning it as a strategic business partner. Adopting advanced analytics in FP&A offers numerous benefits, including improved decision -making, efficient processing of large datasets, increased operational efficiencies, and reduced regulatory compliance risk. However, organizations have been slow to embrace these approaches due to challenges related to infrastructure overhaul, talent acquisition, and change management. A comprehensive literature review explores the importance, application, benefits, and challenges of adopting advanced analytics in FP&A. It also includes case studies that exemplify how organizations utilize advanced analytics techniques to enhance financial management and optimize decision -making processes. Walmart leverages AI and ML for data -driven decision -making, demand forecasting, pricing optimization, and competitor analysis. This approach maximizes revenue, profitability, and customer value. American Express implements advanced fraud detection technologies powered by deep learning models, resulting in improved fraud detection accuracy and real -time transaction monitoring to safeguard customer interests. AXA Insurance utilizes AI and ML to predict high -loss driving accidents and introduce personalized pricing, leading to improved financial management. These case studies demonstrate the practical applications and benefits of adopting advanced analytics techniques in FP&A, empowering organizations to make informed decisions and achieve financial optimization. Despite the challenges, embracing advanced analytics in FP&A has the potential to revolutionize FinancialManagement and improve overall Business Performance.
Pranay Mungara (Sun,) studied this question.