A Classification of Decision Automation and Delegation in Digital Investment Management Systems

Abstract

Digital investment management systems, commonly known as robo-advisors, provide new alternatives to traditional human services, offering competitive investment returns at lower cost and customer effort. However, users must give up control over their investments and rely on automated decision-making. Because humans display aversion to high levels of automation and delegation, it is important to understand the interplay of these two aspects. This study proposes a taxonomy of digital investment management systems based on their levels of decision automation and delegation along the investment management process. We find that the degree of automation depends on the frequency and urgency of decisions as well as the accuracy of algorithms. Notably, most providers only invest in a subset of funds pre-selected by humans, potentially limiting efficiency gains. Based on our taxonomy, we identify archetypical system designs, which facilitate further research on perception and adoption of digital investment management systems.