The promise of financial democratization through robo-advice has encountered hurdles, from data security concerns to a lack of human involvement. Marie Brière, head of investor intelligence at Amundi Investment Institute, sheds light on the barriers faced by adopters of robo-advisory technology. Despite these challenges, the adoption of robo-advice has demonstrated significant benefits for users.
Amundi’s robo-adviser, implemented in 2017, engages in human-robot interactions to guide users in their investment decisions. Through a series of questions about objectives and preferences, the robo-adviser offers personalized portfolio recommendations. Notably, research on nearly 20,000 robo-takers revealed that users saved more and showed a 9% increase in inclination towards equity investments after utilizing the robo-adviser. The key to this success lies in empowering individuals to maintain control over their investment decisions.
Enhanced choices and performance
The research further highlighted that most users followed the robo-adviser’s recommendations and proactively rebalanced their portfolios to align with the targeted allocation. Implementing systematic rebalancing, a practice often overlooked by retail investors, proved highly beneficial, contributing to a 2% to 3% increase in returns. Interestingly, these positive effects were more pronounced for investors with smaller portfolios, indicating that automated financial advice can promote financial inclusion.
Machine bias and algorithmic concerns
Addressing concerns about machine bias and algorithmic accuracy, Brière acknowledges that robo-advisers typically rely on simple algorithms. While there are gains from personalizing asset allocation based on individual characteristics, there is also a risk of over-engagement, particularly during market downturns. To mitigate this, the focus should be on stable personal characteristics that can be precisely estimated, reducing the potential for biased recommendations.
Building trust in Robo-advice
Trust emerges as a critical factor in both financial decisions and the success of robo-advisory services. Survey evidence indicates a general lack of trust in algorithms, with AI still facing skepticism. To build trust, giving users some control over the algorithm is essential. Human-robot interactions, where the robot proposes advice and the human makes the final decision, can enhance trust. Additionally, transparency and explainability play key roles. Offering clients insights into the recommendation process, even if based on a complex model, fosters trust and confidence.
Addressing concerns and increasing adoption
While incorporating more complex AI into robo-advice poses challenges, recent advances in explainable artificial intelligence (XAI) offer potential solutions. “Explainability” allows for clarifying recommendations based on complicated models without divulging the entire algorithm. Disclosing the economic scenarios affecting algorithm accuracy and informing clients about limitations contributes to building trust. Strengthening interactions with clients through alerts about portfolio deviations can be leveraged as opportunities for education, explaining the reasons behind deviations and recommending rebalancing.
Strengthening trust and adoption
As robo-advisory services evolve, addressing concerns and increasing adoption require a multifaceted approach. The emphasis on XAI, allowing for explanation without full transparency, aligns with the need for trust and understanding. Strengthening client interactions through informative alerts enhances not only user experience but also contributes to financial education. Ultimately, the success of robo-advice hinges on its ability to adapt to user needs, instill trust, and bridge the gap towards financial inclusivity.
In navigating the complex landscape of robo-advisory, the industry stands at a crossroads where technology and human engagement must coexist to create a seamless and trustworthy financial ecosystem.
