A couple of years ago, Erick Gonzalez, Leader of our DA practice in LatAm, was featured in a Spotlight article on the role of Analytics in Wealth Management. This insightful piece sparked follow-up questions that inspired him to craft an additional article. Sadly, the article never made it to publishing. Now, looking back at the unpublished the article, its enduring relevance is striking.
Erick’s initial article delved into the transformative waves of analytics within the realm of Wealth Management. When it was written, we were navigating the first wave, characterized by the explosive growth of information access, thanks to the emergence of platforms delivering real-time news and market updates. Erick’s foresight anticipated a second wave, one in which the challenge would be harnessing the wealth of available information to make informed decisions.
Today, we find ourselves within the very second wave Erick had envisaged two years prior. The tools to gather all the necessary information are now at our disposal. However, the current task is to efficiently process this information, derive meaningful analyses, and share personalized insights with our valued clients.
In the following sections, you’ll discover the insights from the original article, explaining the waves of analytics transformation in the realm of Wealth Management. Additionally, we offer an updated perspective on the present state of data and analytics within the field of wealth management.
From the Archives: Wealth Management - Regression to Advice and the role of Analytics
In the original Spotlight article from 2021, Erick hypothesized that the focus in the Wealth Management will regress back to the advice component and progress in analytics solutions will shift accordingly. He expected the quality, timeliness, accuracy, and fit of the advice that Relationship managers or Financial Advisors provide to be the main differentiator driving forward as opposed to pure access to digital platforms and information.
He then went on to describe the two broad distinct waves of digital analytics transformation. Back in 2021, the first wave was ongoing and rapidly advancing to maturity. This wave saw the digitalization and proliferation of Investment Management platforms and self-service tools like robo-advisors and programmatic trading platforms driven by APIs or RPA. There are three main components of this wave that changed the landscape for Wealth and Investment management:
- Robust platforms that provide access and visibility to individual portfolio with real time monitoring and updates,
- Real time or near real time access to market and trading Information,
- And access to trading platforms at low cost, low hassle.
This by extension forced transformation in other areas like pricing structure, market intelligence, and content creation and dissemination. All of this lead to an explosion in the demand for roles in data engineering, cloud computing, web development, and some pockets of data science; namely the roles required to build the platforms and connect the data streams that feed them.
The second wave is not exclusively a consequence of the first wave but is very much influenced by it and can be understood by looking at examples of how software and analytics disrupted other industries as the technology wave reached maturity.
Benedict Evans explained it best in an article called “Outgrowing Software” which outlined how once software companies transformed industries and the software reached a high level of maturity, the subsequent challenges reverted to traditional industry challenges. For example, no one is debating music streaming anymore but fundamental questions like profit split between record labels and artists are still debated, same as brand and promotion.
Erick predicted that the Wealth and Investment Management industry was on a similar path. “In a few years, no one will debate if wealth clients should have access to trade or view their portfolio or news through an app, we will however be debating the accuracy and quality of the investment advice, because at the end of the day, once the new car smell wears off, what wealth clients care about is their portfolio,” he stated in his 2021 article.
This is where the second wave of Data and analytics would focus, it would go deeper into any process in the value chain that could close the gap between information and advice. What this would mean for data and analytics is a higher demand for cognitive analytics, Natural Language Processing, and DevOps functions as well as the continuation of Data Engineers and Data Scientists.
Some more traditional advisors will argue that clients in the High-Net-worth category and above still lean more toward the personalized services provided by Advisors and not towards the digital platforms. This might be true to some degree, but it doesn’t exclude the segment from wanting to reap the benefits of having seamless online access to their portfolio and information. One will not replace the other but will become the new expected standard. Even if the adoption of digital platforms varies through generations, it’s fair to say that as wealth transfers to younger generations, the digital component will be seen as ubiquitous, a purely pay-to-play investment for Financial Services Institutions to be considered as Advisory providers.
Fast Forward to Today
Today we find ourselves in the second wave. The technological infrastructure is firmly in place, platforms abound, and the influx of information has shifted from a mere sprinkle to an oceanic deluge.
Now, advisors face the pressing task of effectively processing the vast information available. They need the assistance of AI and ML to help them absorb data and extract insights that harmonize with specific portfolios. Then, they can send those personalized insights to their high-net-worth clients.
New generations of investors don’t want information dumped on them. If they were looking for news updates, they could easily find the information themselves. What they really desire is tailored advice, crafted for their specific portfolio and individual needs. Enter generative AI.
Generative AI can take that deluge of information, memorize it, summarize it, and go even further to personalize it and ensure that it fits with what you need based on the questions that you’re asking it.
Nonetheless, one thing that hasn’t changed is the need for an advisor. At this point, everybody is likely aware that you need to be cautious with generative AI. You cannot blindly trust the analysis of generative AI tools, especially when it comes to something as impactful as portfolio analysis. Therefore, the insights provided by generative AI cannot be given to investors directly without the expertise of the advisor in the middle.
Conclusion
Erick was spot on when he said that in a few years, no one would debate if wealth clients should have access to trade or view their portfolio or news through an app, but we will be debating the accuracy and quality of the investment advice. We are still trying to close the gap that he mentioned between information and advice. While generative AI has the potential to get us there, we can’t rely on it fully yet.
However, generative AI can be a valuable tool in helping save advisors significant time in drawing personalized insights that they can review and share with their clients. As the demand for personalization continues to grow and the investment environment is constantly changing, tools that help advisors deliver deeper and more meaningful insights cannot be overlooked.
TL;DR
In 2021, Erick Gonzalez highlighted the role of analytics in wealth management, suggesting that the industry would experience transformative waves influenced by advancements in technology. The first wave, as he identified, was driven by the proliferation of digital platforms like real-time news platforms, robo-advisors, and trading platforms. These platforms drastically altered the landscape of wealth and investment management, emphasizing the importance of data engineering, cloud computing, and web development. Gonzalez foresaw a second wave, focused on the quality of investment advice and leveraging advanced technology to bridge the gap between information and actionable insights.
Currently, we’re in the midst of this second wave. As technology has evolved, the sheer volume of accessible information has become overwhelming. Advisors today leverage AI and machine learning to process this information, extracting tailored insights for their high-net-worth clients. However, while generative AI can rapidly analyze and summarize vast amounts of data, it’s essential to approach its insights with caution, especially concerning portfolio analysis. The human touch of an advisor remains invaluable, ensuring that advice is accurate and of the highest quality. Gonzalez’s prediction about the critical role of quality investment advice in the future of wealth management has come to fruition, with technology serving as a tool to enhance, not replace, human expertise.
