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.

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

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.

Retention is a third very key use case. What you are looking at here is using the power of parallel computing to scan through all that data and look at those inverse patterns. When is the client engaging less? It is very difficult for human beings to detect that and find those correlations. It Is going to be a much better result if you have an algorithm doing that for you helping to score people that might potentially be at risk and then providing the next-best-actions and relevant messaging to engage them with. What was the last thing that they showed interest in? What was the last thing you discussed with them? The system ensures that you are circling back to them showing that you have been listening and acting on their concerns or questions.

Hortz: Talk to us more about how AI gets integrated into a firm. What are AI data lakes and how can they work as a strategic backbone for a firm?

Stevenson: A data lake is really a collection of databases or one database with a range of disparate data. Typically, unstructured data could be CRM data, but it could also be website visits, marketing data, as well as all the typical data like portfolios, books and records, and your trade execution systems. So, it is gathering all of that data in one place.

The idea behind it is that you can then combine it in interesting ways to create reports and generate insights. Data lakes ideally can provide one golden source of truth with your data and then have your different systems feeding into that data center and feeding from that. That is an ideal scenario. Especially, if you are a mid to larger size firm, say one hundred Plus advisors where access and usability of that data can be very valuable.

Hortz: Do you really have to be super tech-forward to do these things?

Stevenson: That is a great question. And the simple answer is in the question Do you need to know how Google works to use search? The answer is no. I think that there is a big misconception throughout financial services and certainly in wealth management in particular where advisors have a Terminator-type perspective of AI. Meanwhile, it is relatively innocuous.

It is really just doing similar things to what Google is doing for you, but across all of your data versus imagine you trying to search across the internet or look through the Yellow Pages manually yourself. That is what advisors are doing today with their data. Trying to piece together all their data is literally like going through the Yellow Pages versus using AI to connect all your data and surface what is relevant so that you do not have to do that work.

Hortz: How has your firm grown from being an AI data analytics vendor to now being a platform and strategic partner that brings innovative services to the industry?

Stevenson: Another great question. As we have grown as a company, our platform has become more and more robust including self-service functionality. We really have developed an end-to-end set of capabilities that include the processing of structured and unstructured data. All of the types of integrations that you would need to connect up to the daily workflow of an advisor or an asset manager, wholesaler as well, as a single engine; an AI-based engine to be able to do a lot of work automatically.

And then we have developed an entire range of deep learning-based algorithms to detect very useful information and unstructured data and then match that against instruments in the marketplace and all that existing structured data. Almost 90% of the data in our firm is unstructured. And so today, most wealth managers only use about 10% of what is really available to them. So, we designed a platform that is unlocking all of that. We have also added the Salesforce and CRM plugins to deliver insights into the workflow.

We are particularly good at doing data-driven next-best-actions. And then we have dashboards to analyze all of the usages of that data for managers and business users to be able to bring insights into their business. We have packaged all that into a platform-as-a-service and are able to work with firms that cater to many clients, like our latest strategic partner FLX Networks, to be able to lift the boats for everybody. By partnering with FLX, we can provide a backbone to their operations to help them offer next-generation capabilities including the data analysis capabilities and all those use cases we discussed leveraging all their data for all of their wealth and asset management clients. It brings very powerful economies of scale to FLX clients.

We also have a partnership with Tata Consulting Group which largely works with large financial services firms. We work very well together in a type of “Intel Inside” type approach where some or all parts of our platform could fit into a large wirehouse infrastructure to meet specific needs. That is where we have a distinct advantage. We are solely focused on the financial services domain and have the ability to process unstructured data better than most competitors and the whole package can be deployed in less than three months. That greatly accelerates time to insights and time to markets.

Hortz: Do you have any recommendations or advice for financial professionals on how to begin or better integrate next-generation digital infrastructure into their firms?

Stevenson: Have the right mindset and goal going in. I can break it down even more simply to the end in mind. AI in wealth management helps advisors focus on their clients with the data that matters most to engage and retain clients and generate new revenues by providing highly personalized services. That is it really, as simple as that.

We would then recommend some of the industry leaders in this tech enablement space, that we work very closely with, to help you in making decisions on your digital journey.

Certainly, from a consulting standpoint, we can recommend Craig Iskowitz and his Ezra group as they have a very good data analysis package for small to midsized firms where you can get a clear view of where you stand on your digital infrastructure.

Gavin Spitzner and his Wealth Consulting Partners is well known in the industry and has really deep expertise with wealth management leveraging technology. He is certainly at the forefront of AI and the deployment of data-driven strategies.

Then there is John O'Connell and his Oasis Group with their financial technology coaching services and Joel Bruckenstein and t3 Consulting Services, a tactical expert at deploying cutting-edge technology in an effective way.

Tata Consulting Group and EY are especially recommended if you are an especially large firm. We can make great introductions into a Tata and equally into EY for that level of engagement.