The State of Robo-Advisory Design: A Systematic Consolidation of Design Requirements and Recommendations

Although robo-advisors offer potential benefits for enhancing investment decisions, financial decision-makers remain reluctant to utilize advice from robo-advisors, a form of artificial intelligence designed to convey newfound investment insights in a particularly intuitive way. To increase robo-advice utilization, numerous scholars have investigated various facets and design features aimed at making robo-advisors more appealing to investors. However, these proposed designs often focus only on specific aspects and are spread across various technological and domain contexts. Despite some overlapping ideas, there is little consistency between them. This has led to incoherent notions of what constitutes effective financial robo-advisor design among scholars and practitioners. To address this gap, we conduct a systematic literature review to synthesize existing knowledge. Based on 14 years of research, our study identifies six categories of design requirements and eight categories of design recommendations for robo-advisory. Our structured juxtaposition and synthesis of prevalent robo-advisor requirements and recommendations facilitate holistic research and practical implementations for viable financial robo-advisory systems.