This research paper examines the impact of Artificial Intelligence (AI) on investment decision-making using secondary data and existing literature. The study analyzes key AI tools—including machine learning, deep learning, natural language processing, and predictive analytics—and their applications in algorithmic trading, portfolio optimization, and robo-advisory services. Findings indicate that AI significantly enhances forecasting accuracy, improves risk management, reduces human biases, and strengthens overall decision efficiency compared to traditional approaches. The analysis also highlights differing adoption patterns between retail and institutional investors. Despite the advantages, the study identifies critical challenges such as data privacy concerns, algorithmic transparency issues, ethical risks, and potential over-reliance on automated systems. The paper concludes that while AI is reshaping the financial landscape and investment practices, responsible integration supported by strong regulation and continuous monitoring is essential for sustainable use.
Vandana Saini (Sat,) studied this question.
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