The world is growing more technological every day. While youngsters adapt to this ever-changing world quickly, the older generation is less accepting of the technology that is born today. Older generations remain more comfortable interacting with humans over machines and have a greater mistrust of technology. However, when the pandemic hit the world in 2020, it bit sharply into this mindset. It forced people to live virtually with limited human interaction. People had to move to a digital ecosystem for all purposes.
COVID-19 has significantly affected all aspects of human life, and wealth management is no exception. In a short time, COVID-19 accelerated earlier trends of digitization in the banking sector. The pandemic opened doorways that technology could use to find a seat in the banking and wealth management ecosystem. Today, in the post-pandemic world, a recent global survey of investors and wealth managers by Thoughtlab found that digital interaction and lower fees are among the six megatrends to have motivated people to welcome digital banking infrastructure.
Furthermore, as more youngsters handle intergenerational wealth accrued by their families, they prefer to work in a digital ecosystem. Youngers enjoy working with bankers who are digital natives like themselves. They reach out to professionals via social media networks, video calls, and chat applications. On the contrary, their parents limit themselves to client-advisor relationships.
In the Asia Pacific region, many FinTech firms have started offering courses related to wealth management. These courses are a big hit among the masses. AI and ML technology also gives personalized recommendations to users so they can manage their wealth effectively.
Bottlenecks in the current business model involving personalized research methodologies
Before diving into the challenges of a business model that offers personalized investment suggestions, let us first understand such a business model. A personalized research business model relies on the expertise of relationship managers who guide their clients on the investments they must make to reap maximum wealth. To offer relevant guidance, a relationship manager must spend at least a few hours daily analyzing markets and reports. Then, he makes presentations to educate his client. A relationship manager must repeat this cycle for each client based on the client's portfolio. This time spent by a relationship manager is non-revenue generating.
The problem starts here. Banks and other financial institutions are focused on revenue generation. Revenue generation can only happen if a relationship manager invests time in client acquisition and business development. However, often relationship managers cannot work on revenue generation because research work occupies them. Even if a relationship manager outsources research to an analyst or associate, they are responsible for the outcome. Therefore, they must spend time on research validation. Here, the number of hours a day, personal efficiency, and the sheer number of clients limit a relationship manager's capabilities and productivity.
For these reasons, a business model founded on personalized recommendations given by relationship managers cannot be scaled. However, this is not true for technology. Technology that uses AI and ML-based algorithms can take over the workload of a relationship manager and help them scale such a business model. We shall see how this is possible in the next section.
The importance of AI and ML technology in a personalized research
Conducting research is an important part of wealth management. Research helps relationship managers predict the right investments to make that will lead to large profits. Many wealth managers invest in good research teams that can continuously gather insights about markets, client portfolios, behaviors, trading patterns, and more. However, a single research team cannot do all the work. Technology, on the other hand, can do most of it.
Relationship managers can use technology to their advantage to conduct research effectively. They can input client feedback, expected outcomes, and other constraints into their models to help them predict investments better. They can use machine learning algorithms to identify the most relevant reading materials to recommend to their clients. Likewise, they can set up their digital ecosystem to continuously track aspirational data trends and notify them immediately when a change occurs.
Such automatic analysis by technology will also allow relationship managers to have more meaningful conversations with their clients and give them more precise recommendations.
A few takeaways
As the world is moving towards a digital ecosystem, investors expect smart solutions to their problems. This is the new normal, and the sooner the industries adapt to it, the better. While wealth management remains transaction-driven, integrating technology into its processes will allow for business growth and revenue generation for everyone involved.