ORLANDO, Fla.—The ability for a credit union to be able to truly understand all the information it has on a member and members and to generate intelligent insights from it is at the center of a new “tech ecosystem” announced recently by Velera.
But what does that ecosystem entail, and what will the new solutions mean in practical terms for a credit union? Jeremiah Lotz. SVP, enterprise data & experience design with Velera, sat down with the CU Daily during the company’s recent VeleraLIVE event to discuss the new offerings.
Those new offerings include:
- Velera has launched a new cloud-native technology ecosystem the company said is designed to unify data, payments, risk and digital experiences for credit unions. The Velera Ecosystem includes Stellaris, the company’s technology stack, and Atmos, its intelligence layer, which together are intended to provide end-to-end connectivity across the Velera product suite, according to the company.
- Velera has introduced three new data-focused solutions it said are designed to enhance analytics, data exchange and marketing engagement for credit unions. The offerings — Atmos Exchange, Intelligence Point and a Marketing Engagement Platform — are powered by Stellaris and Atmos, which Velera said are its next-generation technology stack and intelligence layer, and are intended to demonstrate how its ecosystem connects data, insights and activation across the credit union experience, according to the company.
Here’s a look at what Lotz told the CU Daily:
The CU Daily: There are a lot of technology gets released at a meeting like this? What is it about what is being announced here (and reported above) that is unique? And how would you explain it in simple terms to people?
Lotz: I would say the biggest thing I would point out is that it’s an enabler for whatever our credit unions are looking to do. We are part of their larger ecosystem as a whole, and we don’t necessarily provide every touchpoint they have with their members. So, we have to create flexibility and allow what we establish as our payments ecosystem to be flexible enough that credit unions can experience it, engage with it, connect to it, get information from it as necessary, and ultimately design it in a way that allows them to create the best experience for their consumers.
That’s what our focus has been—creating it in a way that is modularized, modernized, and easy for them to consume in whatever way makes sense for the credit union.
The CU Daily: You said it’s an enabler. What did you mean by that?
Lotz: There are kind of two different ways, and I would say the common theme is experience. It could be experience for the consumer or experience for the credit union. If you think about the experience within the credit union, they want to make decisions quickly. They want to have as much information as possible to make those decisions and have curated insights, if you will, so they can look at patterns and behaviors and even begin to look at future predictions.
One of the things we’re doing with our ecosystem, and specifically the data within it, is creating models that allow a credit union to go in and see what behaviors they are seeing across their accounts. What does growth look like? What does fraud look like? If they start to consider future scenarios, what predictions might they be able to make from that data?
One of the things we’ve built into our data environment is AI capabilities. I can ask a simple question like: When you look at consumers of a certain age and their buying patterns, what trends have we seen over the last six months, and what does that forecast for future trends? That can allow a credit union to make decisions on products, pricing, and other areas.
The good news: all this technology produces a lot of data. The bad news: all this technology produces a lot of data. How does one get their arms around all of this without being overwhelmed?
It’s really about understanding the purpose for which you want to use the data. As an industry, we’ve invested a lot of time in data over the past several years. It’s been about getting all the data and putting it in one place, but it’s probably not all being used. I would say only a fraction of it is truly being used.
The biggest advice is to identify the key strategies that a credit union has. For example, maybe we talked earlier about attracting younger members, so that’s a key strategy. Then identify how to use the data to dig into that specific strategy. You’re not going to need all of the data for that scenario. Your next strategy might be to grow the credit union outside of its current footprint. So, what data do you need to understand performance in your current footprint?
Having all the data is important but using it in every scenario isn’t realistic. It’s really about identifying the goals and opportunities you want to explore.
The CU Daily: What if a credit union doesn’t know what it doesn’t know? How does it know what to ask or how to prompt the AI with the data?
Lotz: I would say that’s probably one of the strongest aspects of AI from a future perspective. Obviously, it needs prompts, but you can also go into the data application we’re launching—it has AI embedded in it—and prompt it to give you output from certain data. You can also prompt it to tell you, based on this information and other pieces you may not know, what you should be looking at or what other trends it points to.
It can look not only at your data but also begin to identify what might be happening outside your environment that could influence your path forward. Part of it is that, and I would also say the other piece is tying it directly to your overall objectives. Every credit union has a set of key strategies, so what are those strategies, and how do you leverage the data around them?
The CU Daily: How will Velera clients interact with this data? What will they see?
Lotz: From a credit union perspective, it’s really about how they can best serve the member. When you think about our ecosystem and how it enables risk capabilities, payments, and data, what our credit unions are going to experience is that when they go to serve a member through our application—whether it’s for card servicing or something else—they’ll be able to see that member’s past behavior. They’ll be able to see different scenarios, all within one application.
They’ll be able to move from helping a customer open an account to helping that same customer protect themselves and their card. Traditionally, moving from a servicing application to a fraud application to a data application becomes complex. What we’ve done is create an environment where these systems are talking to each other.
They will also see advice embedded into those applications. Part of what we’re embedding into our servicing applications is what we refer to as embedded analytics. When I pull up a credit union member, I can see certain behaviors on their card, but I can also see trends related to that member and past behaviors that might lead to a different kind of conversation with that consumer than in the past. Getting those capabilities and that information into the application allows for a more streamlined type of conversation.
The CU Daily: This sounds like very highly personalized marketing offers.
Lotz: It’s more than marketing—it’s personalized service. It’s not really about what’s next best for that consumer; it’s about what’s happening with that consumer right now and what behaviors I might need to discuss to help them protect themselves or get in front of a fraud or risk situation.
There is certainly the ability to include next-best actions or marketing opportunities, but it’s also focused on the reality of that consumer’s interaction and the service capabilities needed in the moment.
The CU Daily: What holes do these new solutions fill? What problem does it solve?
Lotz: From a data perspective, I would say the biggest gap we see is financial institutions being able to truly understand their information and generate intelligent insights from it. It’s one thing to pull a report and see historically that certain things happened—growth occurred, engagement happened, behaviors changed—but how do you get insights from that data that allow you, as a financial institution, to determine the next decision you should make?
From a data perspective, what we’ve incorporated are models that show the reasons behind certain behaviors. From an analytics standpoint, we’ve evolved from descriptive—telling you what happened in the past—to more prescriptive and predictive capabilities. It’s about helping you understand what occurred and then prescribing what action you should take next.
For example, based on this data, you should do X, Y, Z for this member to help them. Or, based on this information, you should take specific actions when designing a new product.
