The role of robo-advisors in behavioural finance, shaping investment decisions

This study focuses on using artificial intelligence-driven financial robo-advisors in the investment decision-making domain by retail investors. Based on heuristic and prospect theory, the researcher aims to examine how AI-powered financial robo-advisors (AIPFRA), through their perceived interactivity, personalisation, autonomy, algorithm interpretation and structural assurance, influence retail investors’ loss aversion and overconfidence biases, ultimately enhancing robo-advisor usage in investment decision-making. Using purposive sampling, data from 461 retail investors were collected and analysed through structural equation modelling using SmartPLS 4.0. The results indicate that, except for structural assurance, robo-advisors’ perceived personalisation, interactivity, autonomy and algorithm transparency substantially mitigated investors’ overconfidence and loss-aversion biases, enhancing robo-advisor usage for investment decision-making. The study highlights the significance of financial literacy in moderating the relationship between biases and the use of robo-advisors. The study contributed by extending Heuristic and Prospect Theories to the AI-driven robo-advisor investment ecosystem. The study comprehensively explains how AI-powered robo-advisor design features impact investor psychology and adoption by bridging behavioural biases such as loss aversion and overconfidence with technology mechanisms like interactivity, personalisation, autonomy, algorithm interpretability and structural assurance. The results can help financial institutions implement user-centric advisory tools and help fintech developers create more bias-aware robo-advisors.