It has been more than ten years since the birth of the first robo-advisors, and it is now evident that digital wealth management does not represent a “new” business but simply provides a new channel and new execution methods for a service that banks have been offering to their clients for decades.
Today, wealth management firms have the opportunity to differentiate their service model based on the economic value, attitudes, and preferences of their clients. This ranges from traditional human service, which has been dominant until now and remains the best option for clients with complex needs and substantial assets, to a fully digital service offered by robo-advisors, catering to young and digitally-savvy individuals. There are also various degrees of hybrid models in between.
The true difference lies in effectively using and integrating different channels to ensure the best possible service and a personalized and engaging experience for the client.
In all service models, human interaction will remain crucial, although its extent will vary depending on the chosen model. A digitally-driven service model may only require targeted access to a service center, whereas a successful hybrid approach requires more human interaction, resembling the traditional high-end (private) service model.
Personalized, scalable advice will be the key factor for commercial differentiation and value generation for wealth managers, regardless of the service model. According to a recent survey by Oliver Wyman, around 70% of clients consider the level of personalized advice as one of the most critical factors when choosing a wealth advisor, as well as the economic rationale to justify the cost of financial advice.
Today’s clients are more digitally sophisticated, have complex needs, and expect greater customization of advice over time. Wealth management systems and advisors need to learn and adapt to their preferences.
The good news is that while in the past wealth management had to find a compromise between personalization and scalability, data analytics and the use of machine learning now allow the aggregation and analysis of data across different systems (including CRM, financial planning tools, portfolio construction, and other third-party tools).
Even in the era of the Metaverse, complex financial decisions regarding long-term investment planning and significant assets will continue to be made with the support of well-prepared human advisors, with whom a trusting and personal relationship is established.
In Italy, major players in wealth management still rely on traditional networks of skilled (and well-compensated) human advisors. Digital investing does exist, but it is dominated by fully digital players like Moneyfarm and niche providers like Euclidea. The digital investment and savings channel offered by financial operators is still limited and mostly consists of apps targeting “micro-savers” or younger individuals, such as Gimme5 by AcomeA Sgr or the new Beewise by Azimut.
Wealthype’s data analytics platform for wealth management is designed to assist traditional networks, although it can also be applied to hybrid models or the 100% digital channel.
In fact, our machine learning algorithms have been trained to learn from the best practices of top human advisors, replicating their actions. After all, neural networks, which have existed for over 20 years, aim to replicate the functioning of the immensely powerful and sophisticated machinery that is the human brain. However, it is highly unlikely that even the most sophisticated artificial intelligence will ever surpass a skilled advisor who truly knows their client and builds trust and empathy with them.
The truth is that not all advisors are equally experienced, and not all clients can establish that level of communication and special trust. This is where algorithms come in, allowing the replication of the best experience for a much larger number of clients, benefiting both the company’s profitability and the advisor’s own success, while improving the overall service quality.
The Recommendation System by Wealthype, which I attempted to represent in the initial figure, places the advisor at the center and is designed to help them answer the questions that are key to success. Who is my client truly? What products do they need? Which clients are the most suitable for a particular product? How can I communicate with the client effectively?
It is clear that technology can surpass a person’s computational or memory capabilities, but we are still far from matching the kind of “intelligence” that is made up of a thousand nuances and psychological and emotional implications that define the relationship between a client and their financial advisor.
But if assistance is available, why not transform a skilled advisor into a super advisor?
At Wealthype, this is precisely what we focus on, and we have already helped thousands of advisors improve their work. For a demo of our tools, you can contact us using the link below and discover how to apply Machine Learning principles to your business processes.