Review and Analysis of Advanced Analytics in Financial Services
DOI:
https://doi.org/10.53469/jgebf.2024.06(07).12Keywords:
Financial services, Artificial Intelligence, Machine Learning, Deep LearningAbstract
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 Financial Management and improve overall Business Performance.
References
Dario Amodei et al., “Concrete Problems in AI safety, ” ArXiv preprint arXiv: 1606.06565 [CrossRef] [Google Scholar] [Publisher link]
Adrian Bănărescu, “Detecting and Preventing Fraud with Data Analytics, ” Procedia Economics and Finance, vol.32, pp.1827 - 1836, 2015. [CrossRef] [Google Scholar] [Publisher link]
Mark J. Bennett, and Dirk L. Hugen, “Financial Analytics with R: Building a Laptop Laboratory for Data Science, ” Cambridge University Press, 2016. [CrossRef] [Google Scholar] [Publisher link]
Richard Brealey, Stewart Myers, and Franklin Allen, Principles of Corporate Finance, 13th Edition, McGraw
-Hill Education. [Google Scholar] [Publisher link]
Maurizio Filippone, 2019. [Online]. Available: https: //www.axa. com/en/insights/using - artificial - intelligence - to - better - calculate - thefuture
Gray, G. L., and Alles, M., Data Fracking Strategy: Why Management Accountants Need It, Management Accounting Quarterly [Google Scholar] [Publisher link]
Tony Guida, Big Data and Machine Learning in Quantitative Investment, John Wiley & Sons. [Google Scholar] [Publisher link]
Daniel Jurafsky, and James H. Martin, Speech and Language Processing (3rd edition) Pearson, 2022. [Google Scholar] [Publisher link]
John Koetsier, 2020. [Online]. Available: https: //www.forbes. com/sites/johnkoetsier/2020/09/21/50 - less - fraud - how - amex - uses - ai - toautomate - 8 - billion - risk - decisions/?sh=534d2e701a97
W. James Murdoch et al., “Interpretable Machine Learning: Definitions, Methods, and Applications, ” PNAS Nexus, [CrossRef] [Google Scholar] [Publisher link]
Oesterreich, Thuy Duon et al., “The Controlling Profession in the Digital Age: Understanding the Impact of Digitization on the Controller's Job Roles, Skills and Competences, ” International Journal of Accounting Information Systems, vol.35, no. C, 2019. [CrossRef] [Google Scholar] [Publisher link]
Ryan Owen, 2021. [Online]. Available: https: //emerj. com/ai - sector - overviews/artificial - intelligence - at - american - express/
Nitin Prajapati, Influence of AI and Machine Learning in Insurance Sector, pp.1 - 10, 2021. [Google Scholar]
Foster Provost, and Tom Fawcett, Data Science for Business: What You Need to Know about Data Mining and Data - Analytic Thinking. O'Reilly Media, Inc, 2021. [Google Scholar] [Publisher link]
Stephen Ross, Randolph Westerfield, and Bradford Jordan, Fundamentals of Corporate Finance, 12th edition, McGraw - Hill Education, 2022. [Google Scholar] [Publisher link]
Stuart Russell, and Peter Norvig, Artificial Intelligence: A Modern Approach, 4th edition, Pearson. [Google Scholar] [Publisher link]
Matt Taddy, Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions, McGraw
-Hill Education, 2019. [Google Scholar] [Publisher link] Parth A Kulkarni / IJCTT, 71 (6), 17 - 24, 2023 24
Hal Varian, Artificial Intelligence, Economics, and Industrial Organization, National Bureau of Economic Research, 2018. [CrossRef] [Google Scholar] [Publisher link]
Alex Woodie, 2019. [Online]. Available: https: //www.datanami. com/2019/03/22/how - walmart - uses - gpus - for - better - demandforecasting/
https: //www.miquido. com/blog/predictive - analytics - in - fintech/
Parth A Kulkarni, “Advanced Analytics Driven Financial Management: An Innovative Approach to Financial Planning & Analysis”, International Journal of Computer Trends and Technology Volume 71 Issue 6, 17 - 24, June 2023 ISSN: 2231–2803 / https: //doi. org/10.14445/22312803/IJCTT - V71I6P103 © 2023 Seventh Sense Research Group®
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