AI-DRIVEN DECISION MAKING IN MODERN FINANCIAL TRADING

Abstract

This research paper investigates the psychological and behavioural dynamics of Artificial Intelligence (AI) adoption in modern financial trading, with a specific focus on the Indian retail trading ecosystem. As AI transforms global finance evolving from back-office automation to cognitive systems capable of autonomous sentiment analysis and execution its impact on the individual trader’s decision-making process remains underexplored, particularly in emerging markets. Grounded in the Stimulus-Organism-Response (S-O-R) theoretical framework, this study develops and tests a conceptual model that links traders’ perceptions of AI toolscategorized as Perceived Accuracy, Perceived Efficiency, and Perceived Riskto the quality of their Decision-Making and subsequent Self-Assessed Profitability. The model further examines the moderating roles of Age and Gender. Utilizing a quantitative methodology, data was collected via a structured questionnaire from 427 active Indian retail traders using AI-driven platforms. Analysis using Structural Equation Modeling (SEM) and Process Macro reveals three key findings. First, Perceived Accuracy (β = 0.382, p<0.001) and Perceived Efficiency (β = 0.354, p<0.001) are significant positive drivers of decision-making quality, while Perceived Risk (β = -0.291, p<0.001) is a strong inhibitor. Second, decision-making quality fully mediates the relationship between AI perceptions and profitability. Third, significant moderating effects exist: Age amplifies the positive effect of Perceived Efficiency on decision-making for younger traders, and Gender significantly influences the perception and impact of risk, with female traders demonstrating a stronger negative sensitivity to Perceived Risk. The study concludes that AI’s value in trading is not inherent but is psychologically mediated, and its adoption is not uniform but demographically contingent. Recommendations are provided for traders, FinTech developers, and regulators to foster a more transparent, trustworthy, and effective human-AI symbiotic trading environment.