Financial Robo-Advisors: A Comprehensive Review and Future Directions

Discover the growth, mechanisms, and implications of Financial Robo-Advisors in modern investing.

How should you invest your 401(k)? When I got my first job in the United States, my pension investment options were limited—there were only about 15 funds available. I wanted to invest more in IT companies, but I couldn’t. I remember wishing there were an automatic system that could allocate my investments across different asset classes based on return and risk. That’s essentially what financial robo-advisors do today.

But what are the pros and cons of these smart, automated investment tools?

Paper reviewed:

Nourallah, Mustafa and Öhman, Peter and Walther, Thomas and Nguyen, Duc Khuong, Financial Robo-Advisors: A Comprehensive Review and Future Directions (April 13, 2025). Available at SSRN: https://ssrn.com/abstract=5215748 or http://dx.doi.org/10.2139/ssrn.5215748

Summary

This comprehensive review examines the rise of Financial Robo-Advisors, their operational strategies, and their impact on investment decisions, highlighting both benefits and limitations.

Key Findings

Implications

Business and Policy Implications

Introduction

The rise of Financial Robo-Advisors (FRAs) represents a significant advancement in financial technology, offering households access to financial markets without time or place constraints. With the Assets under Management (AuM) in FRAs growing rapidly, understanding their impact and potential is crucial. This paper aims to provide a comprehensive review of the existing literature on FRAs, focusing on their design, adoption, and the behavioural finance issues surrounding them.

Background and Context

The financial advisory industry has witnessed a paradigm shift with the emergence of FRAs. These digital platforms use algorithms to provide personalized investment advice, often at a lower cost than traditional financial advisors. The growth of FRAs is driven by their ability to offer diversified investment portfolios, primarily through passive investment strategies via ETFs. The increasing popularity of FRAs has sparked a surge in research across various dimensions, including their design, the factors influencing their adoption, and their impact on investor behaviour.

The literature on FRAs is diverse, with studies examining the technology's capabilities, its advantages, and its limitations. Early research focused on comparing FRAs with traditional financial advisors, highlighting differences in cost efficiency and potential behavioural biases. Recent studies have delved into the specifics of FRA design, the factors influencing investor profiles, and the potential for improving these technologies.

One of the key areas of research is the design of FRAs and how they evaluate investor risk profiles. Studies have shown that FRAs typically rely on a limited number of factors, such as risk aversion and time horizon, to suggest portfolio allocations. This has raised concerns about the lack of individualization in FRA recommendations. Researchers have proposed various improvements, including the use of more sophisticated risk assessment methods and the integration of AI to enhance personalization.

The adoption of FRAs is another significant area of study. Factors such as ease of use, trust in the technology, and financial literacy have been identified as crucial determinants of FRA adoption. Demographic characteristics, including age and gender, also play a role, with younger investors and males being more likely to use FRAs. Understanding these factors is essential for financial institutions and policymakers aiming to promote the use of FRAs.

Behavioural finance issues related to FRAs have also garnered significant attention. Research has shown that FRAs can help mitigate certain behavioural biases, such as overconfidence and trend-chasing. However, concerns remain about the potential for FRAs to introduce new biases, particularly if the algorithms used are not transparent or are designed with inherent biases.

As FRAs continue to evolve, future research directions include exploring the integration of LLMs and generative AI to enhance user interaction and advice quality. Addressing privacy concerns and ensuring regulatory compliance will also be critical. Moreover, understanding why FRAs do not appeal to less financially literate individuals, who could benefit significantly from these technologies, is an important area for further study.

The remainder of this paper is structured as follows: The next section will delve into the methodology used for the systematic literature review, followed by a detailed analysis of the findings related to FRA design, adoption, and behavioural finance issues. Subsequent sections will discuss the implications of these findings and outline avenues for future research.

Main Results

The systematic literature review on Financial Robo-Advisors (FRAs) reveals two primary streams of research: "Asset Management" and "Behavioural Finance." The asset management stream focuses on the design and improvement of FRAs, while the behavioural finance stream investigates issues related to the adoption and biases associated with FRAs.

Asset Management

The asset management stream is further divided into two sub-streams: "FRA Design" and "Design Improvement." Studies on FRA design compare FRAs with traditional human financial advisors, examining aspects such as cost efficiency and potential behavioural biases. For instance, Uhl & Rohner (2018) conclude that FRAs are advantageous in terms of cost efficiency and mitigating behavioural biases. Beketov et al. (2018) provide an overview of 219 FRAs from 28 countries, finding that Modern Portfolio Theory is the primary framework used by these technologies.

FRA Design

Research on FRA design highlights the importance of investor profiling and the factors considered in suggesting portfolios. Tertilt & Scholz (2018), Scherer & Lehner (2023), and Scherer & Lehner (2025) analyze portfolio recommendations from FRAs and identify risk aversion, wealth, time horizon, experience, and investment goals as key factors. However, these studies raise concerns that FRAs might categorize investors into pre-built portfolios rather than tailoring advice to individual characteristics.

Design Improvement

The design improvement sub-stream proposes various methods to enhance FRA technology. Byun et al. (2023) and Ko et al. (2023) suggest using encryption methods to improve security and privacy. Scherer (2017) emphasizes the importance of rebalancing functions in FRA design. Capponi et al. (2022) propose a model that improves personalization through client interaction, showing that the frequency of interactions decreases with behavioural biases. Alsabah et al. (2021) develop a framework that allows FRAs to quantify and assess investors' subjective risk tolerance based on their portfolio choices.

Behavioural Finance

The behavioural finance stream is divided into "FRA Adoption" and "Biases Issues." Studies on FRA adoption examine factors influencing households' decisions to use FRAs, such as usability, trust, and demographic characteristics. Nourallah (2023) finds that social media information and public information by FRAs encourage young retail investors to adopt this technology. Isaia & Oggero (2022) survey young adults in Italy, finding that financial literacy and postgraduate education are significant predictors of FRA adoption.

FRA Adoption

Research on FRA adoption highlights the role of trust, financial literacy, and demographic factors. Oehler et al. (2022) conduct a quasi-experiment, finding that FRA users are more willing to take risks and have better statistical knowledge. Piehlmaier (2022) links the adoption of FRAs to overconfidence, showing that investors with higher perceived than actual financial knowledge are more likely to use FRAs.

Biases Issues

Studies on biases issues investigate how FRAs can mitigate or exacerbate investment biases. D'Acunto et al. (2019) report that FRAs reduce the disposition effect, trend-chasing, and rank effect among investors. D'Hondt et al. (2020) argue that FRAs can benefit investors with low education and limited income. However, Oberrauch & Kaiser (2024) show that while FRAs correct certain behavioural biases, they do not promote deeper financial knowledge or long-term skill development.

Methodology Insights

The systematic literature review follows a structured process, starting with a comprehensive search of databases such as Scopus and Web of Science. The search query includes keywords related to FRAs, and the results are filtered based on language, document type, and source type. The final sample consists of 50 articles published in finance journals, which are then analyzed using the Pitching Research Framework by Faff (2024).

This methodology is important because it provides a comprehensive overview of the existing research on FRAs, allowing for the identification of key findings, gaps, and future research directions. The use of a systematic literature review ensures that the analysis is thorough and unbiased, providing a reliable foundation for understanding the current state of research on FRAs.

Analysis and Interpretation

The findings from the systematic literature review have significant implications for both the design and adoption of FRAs. The research highlights the need for more sophisticated methods to improve FRA design, particularly in terms of personalization and security. The use of AI and machine learning techniques is identified as a promising area for enhancing FRA capabilities.

The analysis also reveals that trust, financial literacy, and demographic characteristics play crucial roles in FRA adoption. Understanding these factors can help in developing targeted strategies to promote the adoption of FRAs among diverse investor groups.

Furthermore, the review underscores the potential of FRAs to mitigate certain investment biases, but also notes that these technologies are not a panacea. The findings suggest that FRAs can be beneficial for investors with limited financial knowledge, but there is a need for further research on how to make FRAs more appealing and accessible to this group.

The insights from this review can inform the development of more effective FRAs, improve investor outcomes, and contribute to a more inclusive financial advisory ecosystem. As the financial technology landscape continues to evolve, understanding the dynamics of FRAs will be crucial for both practitioners and researchers.

Practical Implications

The findings of this comprehensive review on Financial Robo-Advisors (FRAs) have significant practical implications for businesses, managers, and investors. The growing Assets Under Management (AuM) in FRAs, expected to reach $2334 billion by 2028, underscores the importance of understanding the dynamics of this technology.

Real-World Applications

  1. Increased Accessibility: FRAs enable households with limited wealth and income to participate in financial markets, promoting financial inclusion.
  2. Personalized Investment Advice: FRAs can offer tailored investment strategies based on individual risk profiles, investment goals, and financial situations.
  3. Mitigating Behavioral Biases: FRAs can help reduce investment biases, such as the disposition effect, trend-chasing, and rank effect, leading to better investment decisions.

Strategic Implications

  1. Competitive Advantage: Businesses that adopt and effectively utilize FRAs can gain a competitive edge in the financial advisory market.
  2. Innovation and Improvement: Continuous innovation in FRA design and functionality is crucial to stay ahead in the market and address emerging challenges.
  3. Regulatory Compliance: FRAs must comply with regulations, such as the General Data Protection Regulation (GDPR), to ensure the secure handling of sensitive investor data.

Who Should Care

  1. Financial Institutions: Banks, investment firms, and other financial institutions should care about FRAs as they transform the financial advisory landscape.
  2. Investors: Both individual and institutional investors can benefit from understanding FRAs, as they offer a potentially more efficient and cost-effective way to manage investments.
  3. Regulators: Regulatory bodies need to stay informed about FRAs to ensure that appropriate guidelines are in place to protect investors and maintain market integrity.

Actionable Recommendations

Specific Actions

  1. Invest in FRA Technology: Financial institutions should consider investing in FRA technology to enhance their service offerings and remain competitive.
  2. Improve FRA Design: Continuous improvement in FRA design, including the integration of Large Language Models (LLMs) and generative AI, can enhance user interaction and advice quality.
  3. Enhance Data Security: Implementing robust data security measures is crucial to protect sensitive investor information and maintain trust in FRAs.

Implementation Considerations

  1. Industry-Research Collaborations: Collaboration between industry practitioners and researchers can facilitate the development of more sophisticated and effective FRAs.
  2. User Education: Educating users about the benefits and limitations of FRAs can improve adoption rates and user satisfaction.
  3. Regulatory Compliance: Ensuring that FRAs comply with relevant regulations is essential to avoid legal and reputational risks.

Conclusion

Main Takeaways

  1. Growing Importance of FRAs: FRAs are transforming the financial advisory industry by making investment advice more accessible and potentially more effective.
  2. Need for Continuous Innovation: The FRA landscape is evolving, and continuous innovation is necessary to address emerging challenges and improve user outcomes.
  3. Importance of Regulatory Compliance: Ensuring compliance with regulations is critical to maintaining trust and integrity in the FRA market.

Final Thoughts

The rise of FRAs represents a significant shift in the financial advisory industry, offering both opportunities and challenges. As the technology continues to evolve, it is crucial for businesses, investors, and regulators to stay informed and adapt to the changing landscape. By doing so, they can harness the potential of FRAs to create a more inclusive and efficient financial advisory ecosystem. The future of FRAs holds much promise, and ongoing research and development will be key to realizing this potential.