This study investigates the evolving landscape of financial forecasting, with a specific focus on the integration of Artificial Intelligence (AI). In an era where financial markets are increasingly volatile and data-driven, traditional forecasting models fall short in delivering real-time, accurate insights. The research explores how AI technologies such as machine learning, deep learning, and natural language processing are transforming financial forecasting by enhancing accuracy, speed, and adaptability. Utilizing a mixed-methods approach, the study combines primary data collected via surveys with secondary data from extensive literature. Key findings highlight high awareness of AI among finance professionals and students, with machine learning and predictive analytics being the most recognized tools. The survey reveals concerns about data privacy, model transparency, and ethical implications, yet shows strong support for hybrid forecasting models that combine AI with human expertise. The study concludes that while AI offers significant advantages in financial forecasting, its adoption must be guided by ethical practices, regulatory frameworks, and transparency to ensure trust and responsible use. This project contributes to understanding the opportunities and challenges associated with AI in forecasting and offers actionable insights for professionals, educators, and policymakers in finance. Keywords Artificial Intelligence, Financial Forecasting, Machine Learning, Predictive Analytics, Ethics in AI, Transparency, Hybrid Models, Data Privacy, Deep Learning, Financial Modelling.This study investigates the evolving landscape of financial forecasting, with a specific focus on the integration of Artificial Intelligence (AI). In an era where financial markets are increasingly volatile and data-driven, traditional forecasting models fall short in delivering real-time, accurate insights. The research explores how AI technologies such as machine learning, deep learning, and natural language processing are transforming financial forecasting by enhancing accuracy, speed, and adaptability. Utilizing a mixed-methods approach, the study combines primary data collected via surveys with secondary data from extensive literature. Key findings highlight high awareness of AI among finance professionals and students, with machine learning and predictive analytics being the most recognized tools. The survey reveals concerns about data privacy, model transparency, and ethical implications, yet shows strong support for hybrid forecasting models that combine AI with human expertise. The study concludes that while AI offers significant advantages in financial forecasting, its adoption must be guided by ethical practices, regulatory frameworks, and transparency to ensure trust and responsible use. This project contributes to understanding the opportunities and challenges associated with AI in forecasting and offers actionable insights for professionals, educators, and policymakers in finance. Keywords Artificial Intelligence, Financial Forecasting, Machine Learning, Predictive Analytics, Ethics in AI, Transparency, Hybrid Models, Data Privacy, Deep Learning, Financial Modelling.
Kumar et al. (2025) studied this question.