AI in Financial Forecasting: A Study on Artificial Intelligence Adoption in Corporate Finance

Author

Praveena Devi and Praveena Puppala


Abstract

 Traditionally, financial forecasting has been based on historical data, statistical models, spreadsheets and the expertise and experience of financial personnel. With the advent of big data, sophisticated computation, and artificial intelligence (AI) tools, financial forecasting can be made faster, more accurate, and more efficient. This research investigates the leveraging of Artificial Intelligence in Financial Forecasting in Corporate Finance and assesses the expected effects on the accuracy of forecasting, planning effectiveness and users' trust. Also examined are the key organizational and technical challenges impacting AI adoption, such as data quality, explainability, organizational readiness and resistance to change. A cross-sectional study was applied with a quantitative research design, prompted by a structured survey of finance, accounting and technology professionals whose knowledge or experience was related to financial forecasting using AI enabled technologies. The results show that there are perceived advantages over the accuracy of forecasts and planning efficiency using an AI-based forecasting approach over traditional forecasting approaches. Some of the findings do underscore, however, continuing questions about the quality, transparency, trust, and readiness of organizations. The study also suggests that AI is perceived more as a tool to aid in decision-making and assist finance professionals than as a superior method for making decisions. The results offer valuable guidance on successful and responsible implementation of AI in the financial planning and forecasting organization. 



Keywords

Artificial Intelligence, Financial Forecasting, AI Adoption, Corporate Finance, Machine Learning, Predictive Analytics, Financial Planning, AI Trust



Full Text:

Download Paper PDF


References


  1. Wasserbacher, H., & Spindler, M. (2022). Machine learning for financial forecasting, planning and analysis: Recent developments and pitfalls. Digital Finance, 4, 63–88.
  2. Dastile, X., Celik, T., & Potsane, M. (2020). Statistical and machine learning models in credit scoring: A systematic literature survey. Applied Soft Computing, 91, 106263.
  3. Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied Soft Computing, 90, 106181.
  4. Ahmed, S., Alshater, M. M., El Ammari, A., & Hammami, H. (2022). Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61, 101646.
  5. Kliber, A., et al. (2023). Forecasting in financial accounting with artificial intelligence: A systematic literature review and future research agenda. Journal of Applied Accounting Research, 25(1), 81–104.
  6. Cao, L. (2022). AI in finance: Challenges, techniques, and opportunities. ACM Computing Surveys, 55(3), Article 64.
  7. (2026). Supervision of artificial intelligence in finance: Challenges, policies and practices. OECD Artificial Intelligence Papers, No. 54. OECD Publishing.
  8. Giantsidi, S., & Tarantola, C. (2025). Deep learning for financial forecasting: A review of recent trends. International Review of Economics & Finance, 104, 104719.
  9. Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied Soft Computing, 93, 106384.
  10. Var, Ö., Durmuşoğlu, A., & Dereli, T. (2026). A review of nowcasting, forecasting and AI in economic indicator prediction. Computational Economics.
  11. Tang, Y., Song, Z., Zhu, Y., Yuan, H., Hou, M., Ji, J., Tang, C., & Li, J. (2022). A survey on machine learning models for financial time series forecasting. Neurocomputing, 512, 363–380.
  12. Nazareth, N., & Reddy, Y. V. R. (2023). Financial applications of machine learning: A literature review. Expert Systems with Applications, 219, 119640.
  13. Weber, P., Carl, K. V., & Hinz, O. (2024). Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature. Management Review Quarterly, 74, 867–907.
  14. Černevičienė, J., & Kabašinskas, A. (2024). Explainable artificial intelligence (XAI) in finance: A systematic literature review. Artificial Intelligence Review, 57, 216.
  15. Uren, V., & Edwards, J. S. (2023). Technology readiness and the organizational journey towards AI adoption: An empirical study. International Journal of Information Management, 68, 102588.
  16. Klein, T., & Walther, T. (2024). Advances in Explainable Artificial Intelligence (xAI) in Finance. Finance Research Letters, 70, 106358.
  17. Wang, P., & Ding, H. (2024). The rationality of explanation or human capacity? Understanding the impact of explainable artificial intelligence on human-AI trust and decision performance. Information Processing & Management, 61(4), 103732.
  18. Sabharwal, R., Miah, S. J., Fosso Wamba, S., & Cook, P. (2025). Extending application of explainable artificial intelligence for managers in financial organizations. Annals of Operations Research, 354, 309–339.

Share your valuable work from Social Media Buttons