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AI-Driven Financial Data Analytics: Unleashing the Power of SAP FICO for Predictive Accounting

© 2024 by IJACT

Volume 2 Issue 3

Year of Publication : 2024

Author : Parveen Singh Hoshiar Singh

:10.56472/25838628/IJACT-V2I3P114

Citation :

Parveen Singh Hoshiar Singh, 2024. "AI-Driven Financial Data Analytics: Unleashing the Power of SAP FICO for Predictive Accounting" ESP International Journal of Advancements in Computational Technology (ESP-IJACT) Volume 2, Issue 3: 153-166.

Abstract :

This article is written to understand the utilization of Artificial Intelligence (AI) with the SAP FI and CO module named FICO for predictive accounting. SAP FICO, coupled with the application interface of Artificial Intelligence, including Machine Learning (ML) and Natural Language Processing (NLP), enables the capacity to consider future financial scenarios in financial planning. Some advantages of predictive accounting include forecasting cash flows, better planning of account budgets, and managing risks. Hence, the paper focuses on cooperation between AI and SAP FICO, using different methodologies, frameworks, and practical cases for their application. Key benefits and challenges are also presented to provide a broad overview of this emerging innovative technology.

References :

[1] Khatri, D. K., & Renuka, A. (2024). Optimizing SAP FICO Integration with Cross-Module Interfaces.

[2] Okungbowa, A. (2015). SAP ERP financial accounting and controlling: configuration and use management. Apress.

[3] Padhi, S. N. (2009). SAP® ERP financials and FICO handbook. Jones & Bartlett Learning.

[4] Khatri, D. K., Jain, S., & Goel, O. (2024). Impact of S4 HANA Upgrades on SAP FICO: A Case Study. Journal of Quantum Science and Technology, 1(3), 42-56.

[5] Truong, M., & Nguyen, L. (2022). The integration of Big Data Analytics and Artificial Intelligence for enhanced predictive modeling in financial markets. International Journal of Applied Health Care Analytics, 7(1), 24-34.

[6] Pillai, V. (2023). Integrating AI-Driven Techniques in Big Data Analytics: Enhancing Decision-Making in Financial Markets. Valley International Journal Digital Library, 25774-25788.

[7] Machireddy, J. R., Rachakatla, S. K., & Ravichandran, P. (2021). AI-Driven Business Analytics for Financial Forecasting: Integrating Data Warehousing with Predictive Models. Journal of Machine Learning in Pharmaceutical Research, 1(2), 1-24.

[8] Olorunyomi, T. D., Sanyaolu, T. O., Adeleke, A. G., & Okeke, I. C. (2024). Analyzing financial analysts' role in business optimization and advanced data analytics.

[9] Al-Okaily, M., & Al-Okaily, A. (2024). Financial data modeling: an analysis of factors influencing big data analytics-driven financial decision quality. Journal of Modelling in Management.

[10] Fehrenbacher, D., & Ghio, A. (2023). Data Sources for Predictive Analytics and Decision Making: A Management Perspective. In Handbook of Big Data and Analytics in Accounting and Auditing (pp. 209-234). Singapore: Springer Nature Singapore.

[11] Derindere Köseoğlu, S., Ead, W. M., & Abbassy, M. M. (2022). Basics of Financial Data Analytics. In Financial Data Analytics: Theory and Application (pp. 23-57). Cham: Springer International Publishing.

[12] Burdick, D., Evfimievski, A., Krishnamurthy, R., Lewis, N., Popa, L., Rickards, S., & Williams, P. (2014, June). Financial analytics from public data. In Proceedings of the International Workshop on Data Science for Macro-Modeling (pp. 1-6).

[13] Moolchandani, S. Advancing Credit Risk Management: Embracing Probabilistic Graphical Models in Banking.

[14] Adelakun, B. O., Onwubuariri, E. R., Adeniran, G. A., & Ntiakoh, A. (2024). Enhancing fraud detection in accounting through AI: Techniques and case studies. Finance & Accounting Research Journal, 6(6), 978-999.

[15] Wunderlich, F., & Memmert, D. (2021). Forecasting the outcomes of sports events: A review. European journal of sports science, 21(7), 944-957.

[16] Stenmark, C. K., Antes, A. L., Wang, X., Caughron, J. J., Thiel, C. E., & Mumford, M. D. (2010). Strategies in forecasting outcomes in ethical decision-making: Identifying and analyzing the causes of the problem. Ethics & behavior, 20(2), 110-127.

[17] Skaburskis, A., & Teitz, M. B. (2003). Forecasts and outcomes. Planning Theory & Practice, 4(4), 429-442.

[18] Ashton, R. H. (1974). The predictive-ability criterion and user prediction models. The Accounting Review, 49(4), 719-732.

[19] Fikri, N., Rida, M., Abghour, N., Moussaid, K., & El Omri, A. (2019). An adaptive and real-time-based architecture for financial data integration. Journal of Big Data, 6, 1-25.

[20] Ozbas, O. (2005). Integration, organizational processes, and allocation of resources. Journal of Financial Economics, 75(1), 201-242.

[21] Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & electrical engineering, 40(1), 16-28.

Keywords :

AI-Driven, SAP FICO, Predictive Accounting, Financial Forecasting, Machine Learning.