AI-Powered Predictive Analytics in Financial Forecasting: Implications for Corporate Planning and Risk Management
Keywords:
Corporate Strategy, Risk Management, Financial Forecasting, Predictive AnalyticsAbstract
This study examines the influence of AI-powered predictive analytics on financial forecasting and its consequences for corporate strategy and risk management. Data from 300 individuals in the Delhi NCR region were acquired using a mixed-methods approach, which involved surveys and semi-structured interviews. The quantitative findings demonstrate substantial enhancements in the precision and efficiency of financial forecasting subsequent to the integration of artificial intelligence (AI), with a notable 15% augmentation in accuracy and a commendable 20% decrease in forecast errors. The ANOVA results indicate that there were consistent improvements in accuracy across different industries. Additionally, the correlation analysis reveals that there are positive associations between the adoption of AI and the use of advanced risk management strategies. Qualitative analysis uncovers the influence of artificial intelligence on corporate planning and proactive risk management. The results emphasize the capability of AI-driven predictive analytics to improve the ability of businesses to withstand and adjust to changes in a quickly changing environment.
Downloads
References
Adams, S., Arel, I., Bach, J., Coop, R., Furlan, R., Goertzel, B., ... Sowa, J. (2012). Mapping the landscape of human-level artificial general intelligence. AI Magazine, 33(1), 25–42.
Agostino, D., Saliterer, I., & Steccolini, I. (2022). Digitalization, accounting and accountability: A literature review and reflections on future research in public services. Financial Accountability and Management, 38(2), 152–176.
Baharudin, B., Lee, L. H., & Khan, K. (2010). A review of machine learning algorithms for text documents classification. Journal of Advances in Information Technology, 1(1), 4–20.
Bahrami, M., Bozkaya, B., & Balcisoy, S. (2020). Using behavioral analytics to predict customer invoice payment. Big Data, 8(1), 25–37.
Bertomeu, J., Cheynel, E., Floyd, E., & Pan, W. (2021). Using machine learning to detect misstatements. Review of Accounting Studies, 26(2), 468–519.
Chollet, F. (2021). Deep Learning with Python (2nd ed.). Shelter Island, NY: Manning.
Dai, J., & Vasarhelyi, M. A. (2017). Toward blockchain-based accounting and assurance. Journal of Information Systems, 31(3), 5–21. https://doi.org/10.2308/isys-51762
Dingli, A., Haddod, F., & Kluver, C. (2021). Artificial Intelligence in Industry 4.0. Cham: Springer International Publishing.
Faccia, A., & Mos¸teanu, N. R. (2019). Accounting and blockchain technology: From double-entry to triple-entry. The Business and Management Review, 10(2), 108–116.
Gerdes, H., Casado, P., Dokal, A., Hijazi, M., Akhtar, N., Osuntola, R., ... Cutillas, P. R. (2021). Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs. Nature Communications, 12(1), 1850. https://doi.org/10.1038/s41467-021-22108-w
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA: MIT Press.
Kureljusic, M., & Reisch, L. (2022). Revenue forecasting for European capital market-oriented firms: A comparative prediction study between financial analysts and machine learning models. Corporate Ownership and Control, 19(2), 159–178.
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of Machine Learning (2nd ed.). Cambridge, MA: The MIT Press.
Moll, J., & Yigitbasioglu, O. (2019). The role of internet-related technologies in shaping the work of accountants: New directions for accounting research. The British Accounting Review, 51(6), 100833.
Penman, S. H. (2013). Financial Statement Analysis and Security Valuation (5th ed., international edition). New York, NY: McGraw-Hill Education.
Russell, S. J., Norvig, P., Davis, E., & Edwards, D. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). London: Pearson.
Taulli, T. (2019). Artificial Intelligence Basics: A Non-Technical Introduction (1st ed.). Berkeley, CA: Apress.
Van Gerven, M. (2017). Computational foundations of natural intelligence. Frontiers in Computational Neuroscience, 11, 112.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Sunil Kumar Das, Urvee Tulsyan, Shoukath TK, Venkata Subrahmanyeswara Adithya Dwadas, Sayyad Jilani, Sharath Kumar Y.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.