Automation of investment advisory services: Exploring the landscape of robo-advisors

Abstract

In an era defined by fastpaced technological advancements and the expansion of AIbased solutions, the traditional financial advisory field is reshaping. A FinTech innovation, robo-advisory, offers customers the possibility of fully digital and costeffective investment advice, challenging traditional solutions.

Despite robo-advisors reducing costs, providing immediate personalised solutions, and generally improving investment performance, it seems unlikely that they will completely replace traditional advisory at this stage. Challenges tied to limited interaction, surface-level customer profiling and technical integration remain, indicating that various hybrid models are the most probable options for now. Still, new research and advancements in technology have the potential to significantly change the field and answer growing demand from younger generations.

This thesis presents a review of the current robo-advisory landscape, assessing the benefits and challenges it offers from various perspectives. Furthermore, robo-advisors’ potential for the future is discussed, proposing future research targets and tangible implications to practice. Current literature is reviewed, and a professional is consulted in a semistructured interview to provide thorough research.