The wealth management advisory ecosystem is undergoing major disruption primarily because of the shifting demographic as wealth shifts hand from Baby Boomers to the millennials. Digital technologies are acting as catalysts to this disruption, and the expectations of both the investor and advisor are changing rapidly.
As more advisors retire from the industry and the next generation of advisors joins it, new advisors will look to bridge the gap between tradition and innovation. Furthermore, transitioning from a commission-based transactional approach to a fee-based advisory model now requires advisors to deliver a more holistic approach based on goals — requiring a deeper understanding of a client's needs. With that in mind, it's important for leaders in the wealth management industry to understand how exactly new technologies intend to augment the expertise of advisors.
What Is Driving This Transformation?
There are several factors that are fueling this transformation, but key amongst them is the change in the expectation from the investors. Both a demographic shift and technology are reshaping how investors view their relationship with their advisors. Some of the key factors driving transformation in the wealth management ecosystem include:
Key Technology Disruptors
Digital transformation has been a key disruptor in the banking and financial services industry. The wealth management advisory ecosystem is no exception and is being increasingly impacted by the technology revolution going on elsewhere. Some of the key technology enablers that are accelerating this disruption are as follows.
Actionable Insights: These can come from cloud-based platforms that leverage data for richer insights, running data analytics to provide insights and client sentiment analysis.
Increase Visibility Through Integration: Integrations like operations control with interconnected products and solutions, an open API architecture that can easily integrate with other sub-systems and a 360-degree customer view are a few options.
Interconnectedness: Connecting with other firms can look like integrating with other wealth management platforms, thereby expanding the offerings. It can also look like tapping distribution services in order to harness critical insights with market access to other financial intermediaries.
Other trends that may grow from continued technological advancements include robo advisors, goal-based investing, sustainable investing, demographic transition and extreme personalization through a state-of-the-art CRM.
To address the needs of the new set of investors and meet their expectations, both seasoned and new financial advisors can look to analytics and cognitive tools to gather a deep understanding of client needs. These insights can assist in understanding each individual client and their financial goals to offer tailored products and services and deliver a better and differentiated wealth management experience.
Deep learning tools accomplish these functions by using advanced analytics and cognitive computing. These advanced techniques provide an opportunity for advisors to address the market differently. I want to walk through four of the ways deep learning is working on adding to the skills of advisors.
Segmenting Clients By Their Behaviors
Client segmentation will be based on each client's behavioral profile and transactions. Advisors will no longer have to segment their clients by arbitrary asset ranges, for example under $500,000, $1 million to $2 million and so forth. Using machine-learning capabilities, you can identify the unique segments in an advisor's book of business. With this knowledge, financial advisors can manage their clients in a more personalized manner.
Predicting Life And Financial Events
Life event detection and prediction proactively service a client's needs better. Clients increasingly expect that advisors anticipate their needs. With the life event detection and prediction capabilities of an AI/ML-based solution, financial advisors can gain a deeper look into the lives of clients. As a result, they can proactively service their clients' financial needs, reaching out at the right time with the right advice or offer.
Predicting Client Attrition
Financial advisors spend countless years growing relationships only to see their efforts lost because of unexpected attrition. When advisors apply analytics to review data on all client's interactions with the firm, typically a pattern will emerge to provide them with advance warning that the client is preparing to reduce business in favor of another firm.
Identifying Affinity To A Given Offer Or Campaign
Using the analytics engine one can identify new business opportunities and provide increased personalization. For financial advisors to offer solutions tailored to clients' financial needs, they need to understand their propensity toward various products. The AI/ML model delivers a product propensity model to do just that. Understand the personality of the client and find the best approach for each client's needs based on their propensity to various financial solutions
One of the key issues with the advisor community is its long reliance on deep relationships and trust to do their business. Wealth is changing hands from Baby Boomers to the millennials is beginning to upset this established client/advisor relationship. While aspects of this tried and true approach will remain, technology will also find its place. Disruption through automated advisory platforms which rely on complex data analytics and real-time market data is further adding fuel to the fire.
While disruption can be scary it also provides an opportunity to learn — and potential opportunities for those advisory firms that can begin to integrate new technology. Deep learning tools based on an advanced AI/ML engine will help deliver behavior-based client segmentation to financial advisors. It contributes to a comprehensive view of the client by integrating multiple data sources and helps them use this data to provide a fact-driven approach to personalized, holistic advice. In addition, it leverages advanced analytics and cognitive computing to deliver the actionable insights that financial advisors increasingly expect, allowing them to better anticipate client needs and build client engagement.
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