This study examines investor heterogeneity in relative priorities for AI-based financial services using a large-scale survey of Japanese online investors. We use data from the 2026 wave of the “Survey on Life and Money,” administered by Rakuten Securities and Kadoya Lab at Hiroshima University. The final analytical sample comprises 14,432 respondents. Respondents selected their first-, second-, and third-preferred AI-based services from 11 options, which were grouped into five functional categories: administrative procedure proxy services, consultation for troubles and emergencies, education and literacy support, information provision and planning support, and advisory and management for investment. Because respondents were required to select their top three preferred services, the dependent variables capture relative priorities rather than absolute willingness to use AI services. Binary probit and ordered probit models show that investor characteristics are associated with relative priorities across service categories, although the estimated marginal effects are generally modest. Information provision and planning support is more strongly prioritized by male respondents, more-educated respondents, and those with greater household financial assets. Advisory and management services are more strongly prioritized by higher-income and more impatient respondents, while risk aversion is negatively associated with this category. Additional robustness checks suggest that these patterns are not explained entirely by unequal category sizes, although option-level results reveal some within-category heterogeneity. These findings suggest that AI-based financial services should reflect investor heterogeneity while recognizing that service categories may contain diverse underlying functions.
Nabeshima et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: