Robo-advisors (RAs): the programmed self-service market for professional advice

Abstract

PurposeThis conceptual paper draws together an interdisciplinary approach to robo-advisors (RAs) as an example of an early and successful example of automated, programmed professional services.Design/methodology/approachLittle is known about the forces driving this change in the delivery of professional service. This work explores the drivers of RAs, the degree of disruption incurred by the introduction of RAs, and how, as RAs advance, trust in algorithmic authority aids in legitimating RAs as smart information.FindingsFrom the firms' perspective, the drivers include rebranding occasioned by the financial crisis (2008), the widening of the client base and the “on-trend” nature of algorithmic authority guided by artificial intelligence (AI) embedded in RAs. This examination of the drivers of RAs indicates that professional service automation is aligned with information society trends and is likely to expand.Practical implicationsExamining RAs as an indicator of the future introduction of programmed professional services suggests that success increases when the algorithmic authority in the programmed serves are minimally disruptive, trustworthy and expand the client base while keeping the knowledge domain of the profession under control of the industry.Originality/valueTreating RAs as an early instance of successfully embedding knowledge in AI and algorithmically based platforms adds to the early stages of theory and practice in the monetization and automation of professional knowledge-based services.

Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research

“…Table 6 provides an overview of identified themes, including (1) perceptions on innovation/digital disruption (Rasiwala and Kohl, 2021), (2) customer satisfaction (Piris and Gay, 2021), (3) service failure (Castillo et al , 2020), (4) robo-advisor (Bhatia et al. , 2020; Wexler and Oberlander, 2021), (5) regulation (Aggarwal, 2021), (6) AI implications for digital marketing (Mogaji et al. , 2020) and (7) value creation (Payne et al.…”

“…Instead, qualitative studies seek to develop themes to gain new insights into a research domain, while conceptual articles aim to advance theoretical considerations (Fossey et al, 2002). Table 6 provides an overview of identified themes, including (1) perceptions on innovation/digital disruption (Rasiwala and Kohl, 2021), (2) customer satisfaction (Piris and Gay, 2021), (3) service failure (Castillo et al, 2020), (4) roboadvisor (Bhatia et al, 2020;Wexler and Oberlander, 2021), ( 5) regulation (Aggarwal, 2021), (6) AI implications for digital marketing (Mogaji et al, 2020) and (7) value creation (Payne et al, 2021a;Riikkinen et al, 2018).…”

“…Few studies investigated AI in the insurance context. Included research analyzed chatbots in supporting customer value creation (Riikkinen et al, 2018), assessed managers' perspectives on chatbots (Jang et al, 2021), reviewed the applicability of AI in insurance fraud detection (West and Bhattacharya, 2016) and conceptualized the role of insurance robo-advisors (Wexler and Oberlander, 2021). Various avenues for future research remain unexplored.…”

“…5.3.4 Conceptual propositions. Finally, future research should empirically test the proposed relationships considered in various conceptual articles (Aggarwal, 2021;Mogaji et al, 2020;Payne et al, 2021a;Riikkinen et al, 2018;Wexler and Oberlander, 2021). For example, Payne et al (2021a) propose numerous AI-service exchange antecedents, including consumer characteristics, such as risk tolerance, need for human interaction or enjoyment, or supporting institutional actor characteristics, such as AI technology infrastructure and data sharing among network actors.…”

“…For example, Payne et al (2021a) propose numerous AI-service exchange antecedents, including consumer characteristics, such as risk tolerance, need for human interaction or enjoyment, or supporting institutional actor characteristics, such as AI technology infrastructure and data sharing among network actors. On the other hand, Wexler and Oberlander (2021) provide around robo-advisory characteristics, including trustworthiness, disembodiment, customizability and user-friendliness. Lastly, future research can further examine the framework and relationships proposed by Mogaji et al (2020), by empirically testing how AI can assist financially vulnerable customers.…”