Not transparent and incomprehensible: A qualitative user study of an AI-empowered financial advisory system

Abstract

AI-empowered and algorithm-driven automated financial advisory systems, also known as Robo-advisors, have been rapidly implemented by service providers and customers in financial service markets. Yet, few empirical studies investigate customers’ experience interacting with fully functional Robo-advisors in real-life scenarios. Also, it is still unknown how the design of the automated system can affect customers’ perception and adoption of this new technology. To mitigate these gaps, 24 participants with different levels of experience and understanding of financial investment were asked to use a Robo-advisor from a retail bank and perform the tasks. By conducting observations and retrospective post-test interviews, we find that participants do not fully perceive the social aspects supposed to be provided by Robo-advisors. The overarching problems are, among others, a lack of transparency and incomprehensible information. This results in distrust of the results generated by this system, which negatively affects customers’ adoption of the investment advice provided by the Robo-advisor. The potential of interactive data visualization is also detected. This work contributes to the understanding of customers regarding their perception and adoption based on their use of a functional Robo-advisor and proposes design takeaways for transparent and comprehensible automated advisory systems in financial service contexts.

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