Assessing the Value of Robo-advisors in Private Investing: The Role of AI and Automation

Topic

This master thesis examines how financial professionals assess the value and limitations of robo-advisors, automation and artificial intelligence (AI) in private investing and wealth management. In this context, it also considers how these technologies may influence the future role of human financial advisors and whether advisory services are likely to develop toward more hybrid models combining technological efficiency with human expertise. In addition, a benchmarking of selected Swiss digital investment and wealth management providers analyzes how current market solutions differ in terms of accessibility, costs, automation, personalization and human support.

Relevance

The topic is relevant because digitalization, automation and AI are increasingly changing how investment services are delivered. Robo-advisors can make investing more accessible, cost-efficient and scalable, especially for younger investors, digitally oriented clients and individuals with smaller investment amounts. At the same time, investment advisory remains closely connected to trust, emotions, risk perception, accountability and long-term financial responsibility. While existing research often focuses on investor adoption or technological capabilities, less attention has been given to the perspective of financial professionals. Their experience is important because they are directly involved in evaluating, explaining and positioning robo-advisory and AI-supported solutions in practice.

Results

The findings show that robo-advisors are valued for standardized, accessible and cost-efficient investing, especially for clients with smaller portfolios, simple investment needs and a preference for digital self-service. However, they are less suitable for complex financial situations involving taxation, retirement, inheritance or broader wealth planning. Trust, emotional support and accountability remain important reasons why human advisors are still needed. AI is therefore perceived mainly as a support technology rather than a full replacement. Overall, the thesis concludes that financial advisory is likely to develop toward hybrid models combining automation, AI-supported efficiency and human judgement.

Implications for Practitoners

• Segmented advisory models: Practitioners can take away that robo-advisors, AI and human advisory should be matched to different client needs rather than treated as one universal solution.
• Strategic automation: Automation can create value in standardized investment processes, especially when applied to suitable clients, situations and tasks.
• Hybrid task allocation: Digital solutions can serve standardized needs efficiently, while complex or emotionally sensitive situations remain connected to human support.
• Trust, transparency and AI governance: Institutions need to explain processes, data use, human review and accountability.
• Changing advisor role: Advisors may increasingly act as interpreters, behavioural guides and trusted counterparts.

Methods

The thesis follows a qualitative and exploratory research design. Ten semi-structured expert interviews were conducted with financial professionals from different areas of the Swiss financial sector, including retail banking, private banking, wealth management, robo-advisory, digital asset management and B2B investment infrastructure. The interviews were analyzed using qualitative content analysis based on Mayring’s method and coded with soIware Taguette. In addition, a benchmarking of selected Swiss digital investment and wealth management providers was conducted. The benchmarked providers were True Wealth, Findependent, Selma, VIAC Invest, Inyova, Digifolio by BLKB and PostFinance E-Asset Management. The combination of interviews and benchmarking made it possible to connect professional perceptions with observable provider models in the Swiss market.