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Next Generation Digital Infrastructure–What It Looks Like And Why It Matters



[The pandemic business environment has clearly demonstrated that the benefits of being a digital firm go well beyond cost and time savings. Building a firm-wide digital infrastructure has become more of a survival imperative and the only way to win in a world of accelerating change and disruption. You just cannot move paper and disparate data fast enough to address our rapidly changing operating environment. This digital Darwinism is really about business resiliency and the ability to grab major new opportunities at the speed of relentless change.


But what exactly does this next-generation digital infrastructure look like, how do you best implement a digital strategy, and why does it vitally matter for your financial services firm?


To get better insights on these questions, we reached out to Institute member Nathan Stevenson, CEO of ForwardLane – an AI Insights Platform and Next Best Action engine used by leading RIAs, broker-dealers, wirehouses, and large wealth and asset management firms to drive growth and deepen client relationships. Being an active player in helping build this new modern infrastructure as a strategic technology partner to the industry, we asked Nathan to share his perspectives and experiences on how this new digital infrastructure is allowing financial firms to build the future.]


Bill Hortz: Can you help us understand what digital transformation and modern digital infrastructure looks like?

Nathan Stevenson:
I think that starting from the client experience is really the best way of thinking about digital. When you log into Apple TV or Netflix or Spotify, everything is personalized with your interactions. And I think increasingly, end customers are expecting this digital-first experience.


In relating your question to financial services specifically, there are really two parts to it. First, is the before. Advisors have typically only seen and engaged with their clients at most once a quarter. According to a Celent study, they then spend more than 50% of their time just in data gathering and analysis in preparation for those engagements. So really, the amount of time that they actually ended up spending with clients is only about 15% of their time. This was the before when the old world was not in touch with clients as frequently as they want to be in touch.


Life is changing, the markets are changing….and very rapidly. The modern client wants more engagement. One, tell me what matters quickly and easily…and give me the flexibility to do things the way that I want to do things without having to involve you as the advisor. Two, they want to be notified when something significant is happening in their financial picture. Inflation is happening to everyone. Longevity is happening to everyone. Clients are starting to realize they might live longer and what is going to happen if they live another 10 years longer than they expect? A lot of concerns are happening for a lot of people.


Research by Capgemini and others are pointing to the fact that advisors are missing out on this. I think that the digital experience is all about creating a personalized, digital-first, data-driven experience which is thoroughly supported by the advisor relationship as a financial coach, a wellness coach, and somebody that you can trust. But to be in that position, advisors need the digital tools and infrastructure to act on and realize this position.


Hortz: What are some of the key use cases for this new infrastructure?

Stevenson:
The key components of this new enabling infrastructure are AI and machine learning and it is quite interesting how they enable this new infrastructure. The first way, or use case, is helping to scan through all of that disparate structured and unstructured data. It would take a human being quite a long time to be able to go through all that data for every client, and then be able to determine and make a list of most important actions. A machine learning system can say, Let’s analyze all of those clients at the same time. The system can then rank them, score them, and then re-sort and re-rank their data on a daily or weekly basis. What that is doing is giving you a kind of rough prioritization of all your clients so that you can better focus your time on the clients with the most vital engagement points and when action is most needed.


With AI you can separate out use cases that can lead to new revenues. There are a lot of different data sets and ways and means to look at that data and then apply the same logic, machine learning logic, and ranking scoring logic to that use case. This can also be applied to lead gen and prospecting and that might be looking at data that specifically relates to clients you do not even have already. So that is a first great use case.


The second big use case is the deepening of client relationships. Here you are going to be looking at different types of data with respect to how frequently clients interact, if they want to interact, and what channels are they interacting through. What has been discussed and what has not been discussed? Data capture and analysis can also demonstrate how individual clients stack up to advisor or the advisory firm segmentation standards.



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