Insight over instinct: AI’s role in rationalizing investment decisions

Purpose The contemporary world has seen remarkable progress in the realm of artificial intelligence, with advancements evident across various domains. This study aims to explore the impact of artificial intelligence on investment decision-making and behavioural biases, specifically focusing on anchoring, representative, overconfidence, loss aversion, and herding biases. Design/methodology/approach This study used a quantitative design, collecting data from 462 individual investors in India using a structured questionnaire. The measurement model was evaluated for reliability and validity, followed by structural model assessment for hypothesis testing. Bootstrapping was used to assess the statistical significance (t-values, p-values) of the path coefficients using PLS-SEM in SmartPLS 4.0. Findings The findings revealed a robust impact on the relationship between behavioral biases and ID. The utilization of AI demonstrated effective mitigation of biases such as anchoring and herding. However, biases like overconfidence, representative and loss aversion persist in the ID process, despite the integration of AI. Research limitations/implications This study focuses on Indian investors and five specific biases, suggesting future research to explore the other unexplored behavioral biases and examine the impact of AI adoption. The study recommends collaboration between government entities and regulatory bodies like the Securities and Exchange Board of India (SEBI) to establish government-led AI/robo-advisory services. It also recommends that fintech companies strengthen their AI services for investors, aiming to provide financial advice free of biases in ID. Originality/value The study’s uniqueness lies in exploring how integrating AI can effectively address and manage behavioral biases, thereby enhancing the ID process.