• Shishir Tewari

Abstract Enterprise systems now use artificial intelligence systems in financial forecasting to achieve both substantial accuracy and efficiency in predictive analytics. This paper investigates how machine learning (ML) methods boost financial forecasting models through their evaluation against established statistical forecasting procedures. The research uses deep learning among other machine learning algorithms and time series forecasting models and reinforcement learning techniques to determine their forecasting power for financial trends and risk management and enterprise decision support capabilities. The research performs a comparative examination to evaluate AI forecasting capabilities regarding accuracy levels together with flexibility and processing speed performance. The analysis draws conclusions about AI model performance through analysis of genuine financial market data in shifting business elements. This analysis studies important issues that affect data quality as well as model interpretability and the expense of computation. The analyzed AI-driven models achieve much better results than traditional forecasting systems when processing extensive complex datasets using automated systems requiring little human supervision. Enterprises that use artificial intelligence for financial forecasting develop stronger abilities to make decisions and lower their risks while planning for strategy development. Maximizing AI’s potential requires the solution of data bias and regulatory compliance and ethical concerns. The research adds to current AI-driven financial forecasting scholarship by establishing practical findings about enterprise system ML model applications and advantages and constraints. Future scholarly inquiry needs to develop hybrid AI strategies which unite specialist knowledge with state-of-the-art machine learning platforms to optimize both accuracy and reliability of forecasting outcomes.

AI-powered forecasting, machine learning, financial prediction, enterprise systems, deep learning, time series analysis, risk assessment.

"AI-POWERED FINANCIAL FORECASTING: ENHANCING ACCURACY WITH MACHINE LEARNING IN ENTERPRISE SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 10, page no.i252-i263, October-2023, Available :http://www.jetir.org/papers/JETIR2310735.pdf

"AI-POWERED FINANCIAL FORECASTING: ENHANCING ACCURACY WITH MACHINE LEARNING IN ENTERPRISE SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 10, page no. ppi252-i263, October-2023, Available at : http://www.jetir.org/papers/JETIR2310735.pdf