The Use of Artificial Intelligence in Financial Forecasting: A Business Analyst’s Perspective

• Author(s): Arunkumar Yadava
• Paper ID: 1707626
• Page: 1112-1130
• Published Date: 26-03-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 9 March-2025

Abstract

Business strategy fundamentally relies on financial forecasting for organizations to predict market movements while evaluating risks before they invest with accurate information. Financial forecasting relied on different traditional approaches such as statistical modeling and historical data assessment and expert opinion. Modern financial forecasting receives a transformation through artificial intelligence through its integration of sophisticated machine learning algorithms along with big data analytics and automation techniques which boost both prediction precision and strategic choices. This research investigates how artificial intelligence enhances financial forecasting from the perspective of business analysts through an assessment of its industrial transformation. The application of artificial intelligence in forecasting depends on machine learning models as well as neural networks together with deep learning techniques which process extensive ranges of both organized and unorganized data. Forecasting precision becomes higher through AI by discovering complex patterns which human analysts commonly miss to spot in domains like stock market trends along with credit risk evaluation and demand projection. AI systems with natural language processing (NLP) capabilities examine financial reports and social media data as well as market news to deliver on-the-spot analytical findings. Through reinforcement learning models become adaptive since they automatically update their predictions using new information. Businesses need financial forecasting as their foundation in developing strategies because it lets them predict market patterns and evaluate possible dangers and invest with accurate knowledge. Financial forecasting relied on different traditional approaches such as statistical modeling and historical data assessment and expert opinion. AI technology now revolutionizes financial forecasting through its combination of sophisticated machine learning algorithms with big data analytics along with automated systems that improve both diagnosis quality and decision-making ability. This research investigates how artificial intelligence enhances financial forecasting from the perspective of business analysts through an assessment of its industrial transformation. AI-driven forecasting implements machine learning definitions as well as neural networks and deep learning process to analyze extensive amounts of data including structured and unstructured formats. AI helps analysts spot complex patterns which human analysts cannot detect so it enhances the accuracy of stock market trend predictions alongside credit assessment operations and market demand projections. The application of natural language processing (NLP) enables AI systems to evaluate financial news together with earnings reports as well as social media sentiment for real-time data analysis. Through reinforcement learning models become adaptive since they automatically update their predictions using new information.

Citations

IRE Journals:
Arunkumar Yadava "The Use of Artificial Intelligence in Financial Forecasting: A Business Analyst’s Perspective" Iconic Research And Engineering Journals Volume 8 Issue 9 2025 Page 1112-1130

IEEE:
Arunkumar Yadava "The Use of Artificial Intelligence in Financial Forecasting: A Business Analyst’s Perspective" Iconic Research And Engineering Journals, 8(9)