Most lenders didn’t set out to run credit, servicing, audit and risk on disconnected systems. Each platform was built to solve its own problem, and over time the gaps between them have become harder to ignore. In floor plan finance, those gaps show up between audits, when a lender knows where the collateral was on audit day but not what has happened since.
In this edited Q&A from the Monitor podcast, Brian Koprowski, president and general manager of DataScan by Solifi, tells Rita Garwood, editor in chief of Monitor and ABF Journal, why the barriers to connecting systems are as much cultural as technical, how continuous monitoring works in practice and why a connected data layer could move risk management from reactive to predictive. The conversation has been edited for length and clarity.
Rita Garwood: Most lenders didn’t choose to end up with disconnected systems. How did the industry get here?
Brian Koprowski: It’s an interesting dynamic. It’s more of an evolution than bad planning. Systems have evolved over time. Even in our ecosystem, the platform we have today didn’t exist five years ago. When you look at how lenders got to where they are, it’s been an evolution of systems developed over time. Credit systems were built to solve credit problems. Risk systems were built to solve risk problems. Servicing systems were built to solve servicing problems. Over time, lenders have seen that there’s probably a better way to pull those things together. So I don’t think it’s necessarily about disconnected applications. It’s about the future and how we start connecting the data in a better way.
Garwood: Where does the resistance to connecting all of those disparate systems — credit, servicing, audit, risk — actually come from? Is it technology? Is it budgets? Is it culture? Is it something else?
Koprowski: It’s probably all of the above. I probably should have just said yes to all of them. A lot of things come into play when you have different platforms and systems: different business owners, maybe different P&L owners in organizations, different budgeting processes. That makes it a big challenge for banks to pull everything together. Technology is becoming easier to integrate. APIs, cloud platforms and modern data architectures let lenders bring data and systems together more easily than they could even 60 days ago, with the evolution of AI. So it’s a continual evolution.
Budget is always a consideration, but the bigger challenge can be organizational, because different groups build their own processes over time, and that’s just how they operate. When somebody wants to make a move, the response is, “Great, but you need to replicate everything I do today.” What we tend to see is that clients struggle more with taking something out of the box than with replicating what they have in a legacy system. If you want to replicate everything you have in a legacy system, just modernize your legacy system and stay there, rather than looking at your operational component.
The question should be, how can we change to better fit today’s technology and modernize what we do operationally? That’s probably been the biggest challenge. It’s part cultural, part operational, but budget plays a factor, too. System transformation and evolution is not an inexpensive proposition for most lenders. That also creates a big challenge.
Garwood: What you described, people being used to doing things a certain way with their legacy system, is the cultural component. What does shifting those people to a more consolidated approach look like? How do you approach teams that are used to periodic, siloed processes?
Koprowski: It’s a challenge, because in most organizations there are business owners at every level who own different segments of the process, but ultimately somebody owns it. Those business lines roll up to a lender, to an institution, and somebody at the institution level has to have visibility into how each business unit is performing. In a bit of a microcosm in our world, we talk about risk and audits. We’re going through an evolution now, moving from a point-in-time physical audit business to a risk-based exception queuing system called Risk Gauge. That’s been a challenge, because you’re totally changing how people operate day in and day out.
Being able to pull data together and use it more consistently allows people to shift what they’re doing — moving from what I’d call a calendar-based system on the audit side to an event-based risk management system. We’re trying to show how data can help people spend their time more efficiently and focus on what they need to focus on, not a bunch of busy work because that’s what they’ve always done.
When you start to pull data together and look at the cultural shift for organizations, it takes a mindset of somebody at a higher level to say, “We really need to shift our approach here to be more effective at what we do.”
Garwood: I know floor plan financing is something you specialize in. Today, most floor plan audits happen every 30, 60 or 90 days. What’s actually happening at the dealership in the gap between those audits, and what does the lag cost a lender when a problem surfaces?
Koprowski: To address the first question, a lot can happen in a 30-, 60- or 90-day cycle, and it depends on the dealer. A dealer audited every 90 days, maybe even every 120 days, probably has really good credit quality. A dealer audited every 30 days probably doesn’t have the highest credit quality. So a lot of things can happen. The challenge with the physical audit today is that it’s only a point in time. On that day, we know exactly where all the collateral is or, if it’s not there, what happened to it. Was it sold? Is it on a rental or demo? We know the status of that collateral as of that day.
If you’re not doing an audit for 60 days, you’re effectively flying blind. You’re relying on the activity at the dealer to represent where you are, but you don’t really know. Without systems that show you data on a daily basis, payment trends could be inflating through that 60-day period. There could be cash flow issues at the dealer, personnel changes, leadership changes. All kinds of things can happen. The data isn’t necessarily going to tell you that, but it will indicate a change in the dealer’s behavior.
That’s what we’re trying to get people to understand and focus on: There are trends that could be leading indicators of a risk or a loss that you may not see with just a physical audit. Every risk manager in the business, whether on the auto side or another asset side, will tell you a physical audit is still the most trusted and best way to validate a dealer’s collateral — to show up, look at the dealer and touch the assets. But again, that’s only a point in time.
What we’re trying to do is move people to continuous monitoring, with data-driven feeds that say, “You have a lot of great dealers over here; don’t worry about them. Here are your troubled dealers or troubled assets that you need to track down. Go look over here, based on the data.” We did a retail data test — I think it was 2024 — of about 1.5 million VINs. We matched retail sales data with payoff trends in our floor plan database. What we found is that a good percentage of dealers paid off their floor plan the same day they were funded by a retail lender, which is awesome.
But 17% to 18% of the units were paid off after the five-day grace period. That represented about $6 billion to $8 billion of collateral paid off outside the parameters of the loan covenants. Most dealers pay it off, and we’ve been out visiting dealers who say, “As soon as we get funded, we pay off the floor plan. Not a big deal,” which is great. But the problem is the 17% to 18% outside the covenants. You could have a major loss there if you’ve got a dealer going south. The data gives us a lot more perspective on what’s going on day to day.
Garwood: What’s the first thing that changes for a risk team once data starts moving in real time instead of on the periodic audit cycle?
Koprowski: The first thing that changes is prioritization. Where are they spending their time today? They spend a lot of time just knowing the schedule of audits, having people there to reconcile the audits and working with the dealer. But they only find trouble when they run across it in an audit. The phrase that resonated with me is that the data helps the team spend less time finding the needles and more time dealing with the needles. When you’re looking for a needle in a haystack, which sometimes happens in an audit — looking for missing units, or units that were sold or funded in violation — you really have to stumble across them.
A physical audit is a little bit of luck, based on when you did the audit. But if you had data that pointed you to the needles you need to look at, you wouldn’t have to go find them. You’d just identify where they are and then deal with them. I think prioritization of how they spend their time, and how much more effective they could be with it, will resonate with lenders.
Garwood: Solifi has talked about connecting with retail lenders, DMS systems and OEM-reported data. What does continuous monitoring look like in practice once those pieces are in place?
Koprowski: The key to your question is “pieces.” I don’t think there’s one silver bullet. There’s not one piece of data that gives you continuous monitoring. In the floor plan world, you have servicing data, payoff data and flooring data. You also have retail sales data. As I mentioned, we’re doing tests there, and we’re advancing pretty heavily in that area. Retail sales information is an indicator that a unit was sold, which is a big piece of information the floor plan provider wants to know. You also have data in the dealer management system, which is beneficial if you have it. You may have OEM information, such as warranty registration data, which is huge. You also have all the physical audit results. And you can mix in location-based data, like GPS data.
It’s not any one of those pieces of data that makes continuous monitoring real. It’s bringing them all together in a platform, where each item helps piece together the story of the risk picture. That’s what we’re trying to build: a platform that ingests all these disparate data sources, pulls them together and allows lenders to drive the risk management process more accurately with the data.
Garwood: New collateral types like EVs and other nontraditional assets have been showing up on more books lately. How do they expose the weaknesses in legacy periodic risk frameworks?
Koprowski: It’s a great question. I think there’s more challenge in the retail space, when you’re underwriting a vehicle for three to six years and you’ve got residual risk, or you’re trying to ascertain your equity position in a vehicle as a lender. From a floor plan perspective, I don’t think it changes a whole lot. Units are sitting on a lot and getting sold. From a core business perspective, it doesn’t change a ton. What it does help with is the data available out of these vehicles. Think of connected-car data. What you get from an EV is much greater than what you get from a traditional internal combustion engine vehicle in a lot of cases, because those cars are inherently connected. You get better access to data sooner than with most traditional vehicles. For floor planning, though, what we normally need is location-based data, which is readily available in the majority of vehicles today.
One thing I’ll add, based on the second part of your question about other asset classes: With power sports, marine and RVs, a lot of those assets aren’t connected today. There’s not a lot of connected technology, or IoT, in some of those verticals, but that’s becoming more prevalent daily, which gives more access to data from OEMs and manufacturers that can be leveraged for floor plan risk management.
Garwood: Examiner expectations are rising around the timeliness and defensibility of risk monitoring. What does that mean in practice for a lender that’s still running a 30-, 60- or 90-day audit cycle?
Koprowski: Examiners and regulators play a heavy role in the process today, certainly with banks. A lot of our captive lenders aren’t regulated like banks, but most of our captives are also starting up banks for various reasons, so they may all be regulated at some point. From what we can tell, regulators are open to understanding what a new process could look like. It’s not about them dictating to the industry what needs to be done. They’re looking at what existing lenders are doing today and opining on whether that is sufficient collateral management.
What we need to do is give lenders access to data and reports that say, “I have access to this information, here’s what I did with it and here’s how I monitored the collateral with that piece of data.” Having reporting and actionable, demonstrable outcomes of how you used data to manage assets is going to be critical to shifting where risk management goes with regulators.
Garwood: You’ve said no single provider will ever have one application that does everything. How does Solifi bring credit, servicing, audit and risk data together without pretending there’s one app for all of it?
Koprowski: When somebody says they have one app that covers everything, I’m a bit skeptical. Most customers want best-of-breed, especially from a servicing perspective. When you look at Solifi’s business today — ABL, equipment lease, floor plan and now, with our latest acquisition, Inovatech and the auto retail platform they have — most of those users in an institution are different. There’s not one user logging into all four of those applications. They’re different business lines. They may even report up to different parts of the bank. The key is not that the applications are one, because I don’t think that will ever happen, and I don’t think anybody should be shooting for it.
The question is how those applications interact at the data layer to give clients access to all their data, to understand their business and their risk positions across all of those platforms. If we have retail sales data in Inovatech, for instance, how is that flowing into DataScan automatically? If you’re a client of both, it should happen automatically. You shouldn’t have to worry about it. A dealer should be the same in Inovatech, in DataScan, in our servicing platform or in our risk management platform. Users could have one login experience for Solifi, but they may be logging into separate applications.
So it comes down to how we build a connected architecture where applications share information through a common data layer. That’s what we’re striving for at Solifi. As we’ve done acquisitions over the last 12 to 18 months, the question has been how we build that infrastructure at the data layer so data can flow between the applications for the long-term benefit of our customers.
Garwood: Customers probably don’t want three separate data feeds from a wholesale solution. They probably just want to look at one source. What does unification actually solve for them day to day?
Koprowski: A lot of customers are looking at vendor consolidation. They want as few data feeds and as few security vulnerabilities or risks as possible, and that generally comes with vendor consolidation. Even in our wholesale platforms, we have three separate applications with different data feeds. Some customers are saying, “It’s great that I’m doing business with Solifi wholesale, but I don’t want three different data feeds that I have to ingest, configure and manage. I want one data feed from you that brings all of your activity across your platform into ours.” That’s where the trends are going.
That’s why bringing the data layer together across the ecosystem will benefit customers long term. Consistent definitions, consistent identifiers, one integration strategy and ultimately the ability to consume the information without rebuilding connections for every platform is where the larger lenders want to go.
Garwood: You mentioned that Solifi handles floor plan, equipment lease and ABL. All of those run differently under the hood. At what point does that data come back together in a way customers can actually use, and what does that unlock strategically?
Koprowski: The underlying workflows and systems will remain independent and different. Where it comes together is at the data layer. There are customers, assets, obligations, transactions, risk events and payment behaviors. Once you establish a common data architecture around those things, you can begin to create a portfolio-based approach from a data layer perspective. That’s where people are looking to go. For a lender, it creates a much broader view of the customer relationship. They may have dealers or consumers who operate across two or three of our different platforms. What does the relationship mean to the bank from a deposit perspective, from a mortgage perspective, from an equipment lending perspective, from a floor plan perspective? Customers want to understand that, and it all starts with the combined data layer.
Garwood: You mentioned that Solifi has been building out its floor plan and equipment finance presence significantly over the last several years. What’s driving equipment lenders to add floor plan as a way to grow their lease portfolios?
Koprowski: It’s an interesting dynamic. Historically, we’ve been primarily focused on auto. About 85% of our portfolio is auto-based. But we started pushing hard on the equipment sector about three to five years ago as we looked to grow other asset classes, and we’re up to 15% to 20% of our business now being non-auto: marine, power sports, equipment, agriculture and other asset classes. From an equipment lease perspective, offering floor plan can help equipment lenders drive more business through their equipment lease platforms.
There’s a benefit to the dealer if they have one source of funding for both floor plan and equipment leasing — or even potentially retail transactions, which we can now offer through the Inovatech acquisition. There are real benefits for smaller dealers who are lending and leasing on equipment to have a floor plan lender that’s working both sides of the fence. They get a broader relationship with one lender. We’ve pushed that concept with lenders: You can grow your equipment leasing business if you offer floor plan, because you’re getting more turns of the inventory and more bites at the apple on the lease side. It can increase your bottom line not only on the floor plan side but also on the equipment lease side.
We’ve seen some pretty good case studies of where that has benefited equipment lenders, and we’re trying to push that even further through our involvement with the ELFA.
Garwood: Let’s say a lender wants to modernize but can’t replace everything at once. Where should it start?
Koprowski: Each lender has to identify that for itself by looking at its problem, not the platform: What are they trying to solve, and what platform is best suited to do that? We had a customer recently that wanted to reduce a legacy platform. On our side, we call this non-floor plan loans. Think of mortgages, working capital loans, things of that sort. They had a legacy system tracking all that, and we did some of it in our core servicing platform. We partnered with them to ask what they were trying to do. They said, “We need to get modern; we need to retire a legacy application. We’d love to have one spot for our dealers to log in and look at their statements for floor plan, for the mortgage and for the working capital line — a one-stop shop, one pane of glass for the dealer.”
We worked with them over the last two years to develop functionality in our platform to move all that business to our current platform and give their dealers that one-pane-of-glass experience. We launched that product in August, a huge two-year effort. We onboarded about $6 billion worth of collateral to the platform for a large lender. It helped reduce a legacy platform, but it also solved the problem from the dealer’s perspective, which was getting a more streamlined interface from the lender, and we accomplished that as well.
So I’d say don’t start with a rip and replace. Start with connect and improve. Find out where your best connection points are and improve on those, rather than trying to replace an entire platform. But it all starts with identifying the business problem you’re trying to solve.
Garwood: Last question, to zoom out across our entire conversation. What does a connected data layer ultimately change about how credit, audit, servicing and risk teams work together on a day-to-day basis?
Koprowski: Like a lot of what we’re seeing in the industry today, it comes back to AI. Everything we hear and see is AI. I love that commercial where the guy just keeps talking about AI in a meeting. That’s kind of where we’re going. But what can really evolve with a data layer is having data that can be accessed across the organization — from credit, from servicing, from risk management — and pulling it all together to leverage large language models, or even building your own model, to be more predictive about risk and business outcomes. Traditionally, the industry has been more reactive. Everything is based on what has happened over the last 30, 60 or 90 days.
We’re not predicting the future over the next 30, 60 or 90 days. We’re just changing what we do over the next 30, 60 or 90 days because of the past. Where I think these models and predictive analytics can go is to take organizations to be a lot more predictive on all sides of their business, not just one little sector. Having that connected data layer is the key to unlocking the blind spots that organizations have when they can’t see the data in one place and can’t analyze it the way large language models can today.
So I’m really excited about what a connected data layer can bring to the industry and to customers, and how we can use it to evolve toward a more predictive, rather than reactive, operating style.
Garwood: It’s definitely going to be exciting to see what happens. Everything’s changing so rapidly that it’ll be here before we know it.
Koprowski: The advancements are changing daily, so you’ve got to stay ahead. Unfortunately, a lot of institutions are sitting on 10-, 15- or 20-year-old systems, and the replacement process is hard and difficult. You can’t take advantage of all these new technologies until you get to the point where you can leverage a connected data layer. Even cloud infrastructure is still a bit of a concern for some. To leverage all of that, you have to modernize your stack and your platform.
