AI-Powered Predictive Analytics in Financial Forecasting: Implications for Corporate Planning and Risk Management


  • Sunil Kumar Das, Urvee Tulsyan, Shoukath TK, Venkata Subrahmanyeswara Adithya Dwadas, Sayyad Jilani, Sharath Kumar Y.


Corporate Strategy, Risk Management, Financial Forecasting, Predictive Analytics


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.


Download data is not yet available.


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.

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.

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.




How to Cite

Sunil Kumar Das. (2024). AI-Powered Predictive Analytics in Financial Forecasting: Implications for Corporate Planning and Risk Management. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3512–3516. Retrieved from



Research Article