Artificial Intelligence-Assisted Financial Decision-Making and Investment Behaviour among Individual Investors in India: The Roles of Perceived Accuracy, Trust, Risk and Financial Literacy

The increasing availability of Artificial Intelligence (AI) is transforming the way individual investors access, interpret, and use financial information. However, empirical evidence regarding the relationship between AI usage and investment behaviour remains limited, particularly in the context of retail investors in India. This study examines the factors influencing AI-driven financial decision-making and investigates its relationship with the investment behaviour of individual investors. A structured questionnaire based primarily on five-point Likert-scale statements was administered to 380 individual/retail investors in India. The data were analysed using descriptive statistics, reliability analysis, exploratory factor analysis, Pearson correlation, multiple regression, regression diagnostics, and moderation analysis using IBM SPSS.
The findings indicate that perceived usefulness, perceived ease of use, and perceived accuracy significantly and positively influence AI usage, with perceived accuracy emerging as the strongest predictor (β = .486, p < .001). AI usage was positively and significantly associated with investment behaviour (r = .418, p < .001), while trust in AI showed a significant positive relationship with investment behaviour (r = .361, p < .001). In contrast, perceived risk demonstrated a significant negative relationship with investment behaviour (r = −.223, p < .001). The moderation analysis further examined the role of financial literacy in the relationship between AI usage and investment behaviour.
The study contributes to the emerging literature on AI in finance, technology adoption, behavioural finance, and investor decision-making by integrating technological and psychological determinants within a single framework. The findings highlight the importance of perceived accuracy, trust, and risk in understanding investors' use of AI for financial decision-making. However, the study is subject to the limitations of its cross-sectional design, self-reported measures, and non-probability sampling, which restrict causal inference and generalisability. Accordingly, the findings indicate that AI usage is positively associated with self-reported investment behaviour; they do not establish that AI causes superior investment performance or actual portfolio returns.

Artificial Intelligence; AI Usage; Investment Behaviour; Retail Investors; Financial Decision-Making; Trust in AI; Perceived Risk; Financial Literacy; Behavioural Finance.

1. Aristei, D., & Gallo, M. (2026). Financial literacy, robo-advising, and the demand for human financial advice: Evidence from Italy. Journal of Behavioral and Experimental Finance, 49, 101125. [Google Scholar] [Crossref]

2. Bianchi, M., & Brière, M. (2024). Human-robot interactions in investment decisions. Management Science. [Google Scholar] [Crossref]

3. Cardillo, G., & Chiappini, H. (2024). Robo-advisors: A systematic literature review. Finance Research Letters, 62, 105119. [Google Scholar] [Crossref]

4. D'Acunto, F., & Rossi, A. G. (2023). Robo-advice: Transforming households into rational economic agents. Annual Review of Financial Economics, 15, 543–563. [Google Scholar] [Crossref]

5. Manrai, R., & Gupta, K. P. (2023). Investor's perceptions on artificial intelligence (AI) technology adoption in investment services in India. Journal of Financial Services Marketing, 28, 1–14. DOI: 10.1057/s41264-021-00134-9. [Google Scholar] [Crossref]

6. Maheshwari, H., Samantaray, A. K., Sandhu, K., & Panigrahi, R. R. (2025). Insight over instinct: AI's role in rationalizing investment decisions. International Journal of Accounting & Information Management, 33(4), 672–692. [Google Scholar] [Crossref]

7. Rossi, A. G., & Utkus, S. (2024). The diversification and welfare effects of robo-advising. Journal of Financial Economics, 157, 103869. DOI: 10.1016/j.jfineco.2024.103869. [Google Scholar] [Crossref]

8. Tiwari, C. K., Bhat, M. A., Gupta, K., et al. (2026). Decoding investor behavior in the age of financial AI: Integrating cognitive, emotional, and social drivers of adoption and resistance in India. [Google Scholar] [Crossref]

9. Torno, M. (2023). Determinants of conventional and digital investment advisory decisions: A systematic literature review. Financial Innovation. [Google Scholar] [Crossref]

10. Zhu, H., Vigren, O., & Söderberg, I.-L. (2024). Implementing artificial intelligence empowered financial advisory services: A literature review and critical research agenda. Journal of Business Research, 174, 114494. DOI: 10.1016/j.jbusres.2023.114494. [Google Scholar] [Crossref]

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