Geopolitical shocks, pandemics, inflationary spirals, and energy crises: over the past decade, uncertainty has become a societal feeling. Each new disruption destabilizes markets, raises investor anxiety, and underscores the need for accessible and reliable financial advice (1). While at the same time, financial robo-advisors (FRAs) have gained traction. These FinTech-driven platforms promise affordable, data-driven wealth management at scale (2), often marketed as a way to democratize finance and help investors keep a steady hand during turbulent times offering personalized investment recommendations(3). But do they really deliver?
To answer this, we must first understand what robo-advisors are, how they compare to traditional advisors, and what factors—personalization, trust, and accessibility—shape their effectiveness.
At their core, FRAs are algorithm-based systems that build and manage portfolios according to user data. Beketov et al. (2018), in one of the largest comparative studies to date, analyzed 219 robo-advisors across 28 countries. They found that most robo-advisors still rely on Modern Portfolio Theory, a traditional framework. However, the research also shows that the more technologically advanced the asset allocation model, the greater the assets under management (4). In other words, robo-advisors using more sophisticated models tend to achieve better results, as reflected in their ability to attract and manage larger portfolios. This indicates that technological advancement is not merely a marketing strategy, but a key factor directly linked to user trust and adoption and, consequently, financial success.
When stacked against human advisors, the picture becomes more complex. Uhl & Rohner (2018) compared robo-advisors and traditional advisors across performance indicators and concluded that FRAs outperform humans on two key fronts: they cut costs dramatically and they mitigate behavioral biases (5). In their view, human advisors are at a structural disadvantage, making FRAs potentially more effective at generating returns.
However, Harrison & Samaddar (2020) offer a counternarrative. Their study observed two human advisors and two robo-advisors over six months, concluding that the humans performed better (6). Nontheless, the methodological limitations—tiny sample, short timeframe—make their results more anecdotal than definitive. Still, what these contrasting findings really highlight is not a zero-sum competition but the need for nuance in evaluating advisory performance.
This nuance is increasingly reflected in practice. Kumar (2024) shows that the growing popularity of robo-advisory has not eliminated human advisors; many financial institutions have integrated FRAs into their existing service models, therefore pointing to a hybrid approach: robo-advisors handling standardized, scalable tasks, while humans provide tailored advice where emotional intelligence and complex judgment are required (7). Rather than disruptors, FRAs are becoming complementary tools in a broader advisory ecosystem.
If robo-advisors are to stand alongside human advisors, the central question becomes; how can advisory be strengthened through collaboration between human and digital solutions? And to what extent can personalisation, a key element of financial advisory, be achieved solely through robo-advisors? Robo-advisors build their recommendations on profiling factors such as risk tolerance, financial wealth, investment horizon, and goals. Yet despite personalization being one of their core promises, evidence highlights a gap between what is presented and what is actually achieved. Exploring personalization in the context of human–robo collaboration becomes key to understanding how technology can support, rather than replace, the human dimension of investment advice.
Tertilt & Scholz (2018), as well as Scherer & Lehner (2025), analyzed how robo-advisors translate questionnaire inputs into portfolio recommendations. Despite collecting a variety of information, they found that only two factors—risk aversion and time horizon—significantly drive allocations (8, 9). Other inputs, such as goals or financial experience, play a marginal role at best.
Faloon & Scherer (2017) go further, criticizing the opaque scoring models and standardized templates on which most FRAs rely (10). Helms et al. (2022) confirm this: a handful of questions dominate the process, leaving investors with the sense of a tailored plan, but in reality, their portfolio is little different from thousands of others (11).
This raises a deeper challenge: risk aversion itself is not static. Capponi et al. (2022) demonstrate that it evolves with age, income, financial literacy, and even macroeconomic shocks. An investor may appear risk-seeking in stable times, yet quickly shift to caution during downturns. Their model suggests that continuous interaction—not one-off questionnaires—is necessary to capture this dynamism (12).
AI and the Evolution of Personalization in Robo-Advisors
Personalization alone is not enough—trust is the true currency of financial advice. And here, robo-advisors face both unique advantages and persistent hurdles.
For some, FRAs inspire trust precisely because they lack human conflicts of interest. Brenner & Meyll (2020) show that investors wary of biased or self-interested advisors often prefer the perceived neutrality of algorithms (14). For others, adoption is a matter of familiarity: Gerlach & Lutz (2021) find that once people try FRAs, their perception of risk declines and their sense of benefit increases (15). Trust, in other words, grows through use.
Yet not all trust is intrinsic. Holzmeister et al. (2023) demonstrate that confidence in the logic of algorithms strongly shapes willingness to delegate decisions (16). Transparency matters, too: Scherer & Lehner (2025) highlight that clear explanations of portfolio choices and visible safeguards against fraud are decisive for long-term adoption (9).
Perhaps most striking is the role of traditional institutions. Okat et al. (2025) reveal that trust in banks spills over into trust in robo-advisors, significantly boosting FinTech adoption (17). This reinforces the idea that FRAs are not replacing banks but extending their reach, drawing credibility from established institutions.
If trust explains why investors adopt robo-advisors, their impact on behavior explains why they stay. D’Acunto et al. (2019) find that FRAs reduce common behavioral biases, including trend-chasing, the disposition effect, and rank bias, while promoting broader diversification (18).
Moreover, the facilitated accessibility is especially valuable for vulnerable groups. D’Hondt et al. (2019) show that individuals with limited financial education or lower incomes—those most at risk during crises—benefit disproportionately from FRA use (19). By reducing barriers and stabilizing decision-making, robo-advisors serve not only efficiency but also inclusion.
Still, this inclusion has limits. Oberrauch & Kaiser (2024) observe that FRA users do not improve their financial literacy over time (20). They may make better decisions thanks to automation, but they do not necessarily understand them better. For true empowerment, robo-advisory must be paired with external financial education provided by institutions. Without this, accessibility risks becoming dependence.
Robo-advisors thrive in uncertainty because they offer stability, affordability, and scalability. They discipline investor behavior, broaden access to financial services, and, when integrated into institutional frameworks, gain the trust necessary for adoption. But their limitations—opaque personalization, static profiling, lack of educational impact—remain real.
The future likely lies in complementarity. Human advisors provide empathy and judgment; robo-advisors ensure efficiency and discipline. AI-driven profiling can bridge the gap in personalization, while financial institutions can supply the education that robo-advisors alone cannot. Together, these elements point to a model of advice that is not only more resilient but also more inclusive, a particularly valuable quality in times of geopolitical and economical instability.
This publication’s content is solely intended for informational and promotional purposes. This publication is not intended to provide contractual offers or professional financial advice.
Sources:
Onabowale, O. (2024). The rise of AI and Robo-Advisors: Redefining financial strategies in the digital age. International Journal of Research Publication and Reviews, 6.
Dias, F. S. (2025). An Introduction to Robo-Advisors, Notable Fintech Implementations, and Underlying Theory. In Fintech and the Emerging Ecosystems: Exploring Centralised and Decentralised Financial Technologies (pp. 285-300). Cham: Springer Nature Switzerland.
Tahvildari, M. (2025). Integrating generative AI in Robo-Advisory: A systematic review of opportunities, challenges, and strategic solutions. Multidisciplinary Reviews, 8(12), 2025379-2025379.
Beketov, M., Lehmann, K., & Wittke, M. (2018). Robo advisors: quantitative methods inside the robots. Journal of Asset Management, 19(6), 363-370.
Uhl, M. W., & Rohner, P. (2018). Robo-advisors versus traditional investment advisors: An unequal game. The Journal of Wealth Management, 21(1), 44.
Harrison, J. P., & Samaddar, S. (2020). Who is better at investment decisions: Man or machine?. The Journal of Wealth Management, 23(3), 70-84.
Gupta, S., Taneja, S., Kumar, V., Ozen, E., & Vardari, L. (Eds.). (2024). Robo-advisors in Management. IGI Global.
Tertilt, M., & Scholz, P. (2018). To advise, or not to advise—how robo-advisors evaluate the risk preferences of private investors. The Journal of Wealth Management, 21(2), 70-84.
Scherer, B., & Lehner, S. (2025). What drives robo-advice?. Journal of Empirical Finance, 80, 101574.
Faloon, M., & Scherer, B. (2017). Individualization of robo-advice. The Journal of Wealth Management, 20(1), 30.
Helms, N., Hölscher, R., & Nelde, M. (2022). Automated investment management: Comparing the design and performance of international robo‐managers.European Financial Management, 28(4), 1028-1078.
Capponi, A., Olafsson, S., & Zariphopoulou, T. (2022).Personalized robo-advising: Enhancing investment through client interaction. Management Science, 68(4), 2485-2512.
Alsabah, H., Capponi, A., Ruiz Lacedelli, O., & Stern, M. (2021). Robo-advising: Learning investors’ risk preferences via portfolio choices. Journal of Financial Econometrics, 19(2), 369-392.
Brenner, L., & Meyll, T. (2020). Robo-advisors: a substitute for human financial advice?. Journal of behavioral and experimental finance, 25, 100275.
Gerlach, J. M., & Lutz, J. K. (2021). Digital financial advice solutions–Evidence on factors affecting the future usage intention and the moderating effect of experience. Journal of Economics and Business, 117, 106009.
Holzmeister, F., Holmén, M., Kirchler, M., Stefan, M., & Wengström, E. (2023). Delegation decisions in finance. Management Science, 69(8), 4828-4844.
Okat, D., Paaso, M., & Pursiainen, V. (2025). Trust in traditional finance and consumer fintech adoption. The Review of Corporate Finance Studies, 14(2), 408-438.
D’Acunto, F., Prabhala, N., & Rossi, A. G. (2019). The promises and pitfalls of robo-advising. The Review of Financial Studies, 32(5), 1983-2020.
D'Hondt, C., De Winne, R., Ghysels, E., & Raymond, S. (2019). Artificial intelligence alter egos: who benefits from robo-investing?. arXiv preprint arXiv:1907.03370.
Oberrauch, L., & Kaiser, T. (2024). Digital interventions to increase financial knowledge: Evidence from a Pilot RCT (No. 16811). IZA Discussion Papers.
Holzmeister, F., Holmén, M., Kirchler, M., Stefan, M., & Wengström, E. (2023). Delegation decisions in finance. Management Science, 69(8), 4828-4844.
Okat, D., Paaso, M., & Pursiainen, V. (2025). Trust in traditional finance and consumer fintech adoption. The Review of Corporate Finance Studies, 14(2), 408-438.
D’Acunto, F., Prabhala, N., & Rossi, A. G. (2019). The promises and pitfalls of robo-advising. The Review of Financial Studies, 32(5), 1983-2020.
D'Hondt, C., De Winne, R., Ghysels, E., & Raymond, S. (2019). Artificial intelligence alter egos: who benefits from robo-investing?. arXiv preprint arXiv:1907.03370.
Oberrauch, L., & Kaiser, T. (2024). Digital interventions to increase financial knowledge: Evidence from a Pilot RCT (No. 16811). IZA Discussion Papers.
Shen, Z., Wang, Z., Chew, J., Hu, K., & Wang, Y. (2025). Artificial intelligence empowering robo-advisors: A data-driven wealth management model analysis. International Journal of Management Science Research, 8(3), 1-12.
ŞAHiN, E., Arslan, N. N., & Özdemir, D. (2025). Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning. Neural Computing and Applications, 37(2), 859-965.