The leader of an analytics business that provides technology for companies in the mortgage space says they are on a mission to set a new standard for data integrity and trusted automation.
According to Steve Smith, CEO and co-founder of BaseCap Analytics, there is a need industry-wide for clean, accurate records. By leveraging validation technology, companies can streamline internal processes and let their employees focus on activities that enhance productivity.
BaseCap Analytics can also help companies with regulatory reporting and compliance. The company recently announced a pilot with Cenlar FSB, the nation’s leading residential mortgage subservicer.
“We are pleased to pilot BaseCap’s solutions in an effort to strengthen our ability to comply with an increasingly complex web of mortgage servicing regulations,” Cenlar Senior Vice President of Loan Operations William Moffett said in a statement.
Moffett added that providing greater data transparency aligns with their commitment to using new technology to advance the homeowner experience.
Smith recently sat down with Editor Kimberley Haas to talk about his business and how they help companies improve the accuracy of their data to get ahead of the competition.
Haas: So, Steve, I understand you’re the CEO of BaseCap Analytics, and I’m hoping maybe you can start by telling us a little bit about yourself, and how you got into this field.
Smith: I think that’s always an interesting question… How did you end up in mortgage? Everyone has a unique story and I think mine is also fairly unique from that perspective.
So I’m the CEO and the co-founder of BaseCap. I met the other co-founder at Morgan Stanley around the time of the financial crisis. We were working at Morgan Stanley to help the organization make automated business decisions. It could be something like, “Do you understand the cost of capital before executing a trade?” And the only way you could do that was with good, clean, accurate data.
Myself and the co-founder were sitting at the desk many late nights eating delivery dinner. And we said, “Hey, we’re pivoting spreadsheets. We’re doing things very manually. There’s got to be a better way to address what essentially is the garbage in, garbage out problem with data quality.”
That was sort of our realization that this is a big problem. And we took a step back and started doing research and, looking deeper, we realized that this is a $3 trillion problem. And it plagues really any organization out there that’s trying to make good business decisions.
You’re looking at the information you have as a company, and you’re basing your business decisions off of that information. So we said, “We’re going to solve this problem. It’s a massive problem.”
That was sort of our intro and how we ended up in the mortgage space.
We were founded in 2012. We were a services-based business until 2019, which is when we launched the product. We spent two and a half years actually developing the product prior to having our first commercial customer. And I think that’s one of the big differentials as a product company that makes us successful.
We self-funded the developmental product, and we had seen certain pains in the market that because we were a services-based business, we were able to look at and go, “Hey, you know what? Everyone’s asking us to solve this problem with services. You know, what would be a better solution to meet that need? It’d be a software or product-based offering.”
So we’re not directly out there just throwing people at the problem. There’s a better way. There’s a technology-based way that addresses this need.
Haas: Where are you headquartered and how many employees do you have?
Smith: We’re headquartered out of New York City. We have 35 employees, and we’re a virtual company, so we’re actually in 15 different states.
Haas: So 15 different states. Give me an example of the type of clients that you typically work with.
Smith: Absolutely. I am restricted in terms of speaking in detail about the Cenlar pilot due to a non-disclosure. But what I can tell you is their needs as a company are 100% industry-wide.
Haas: What are some of those industry-wide needs then? I mean, if you can’t talk specifically, and I understand, obviously, the reason why you can’t, what are some of the needs from the industry that you’re trying to solve?
Smith: When you think about it, one way to conceptualize it is a loan file.
If you’ve been through a mortgage closing before, there’s a lot of documents you sign, right? And those go into a folder, right? And then they, from the borrower’s perspective, they sort of disappear.
Did they disappear? No, they’re absolutely still filed somewhere. Everyone relies on them because they’re the official record of the loan.
Think about this as eight different entities coming together at all times, touching and needing this information, and people copy it, people take versions of it. The need for one clean, accurate record that’s consistent across all of these parties is a major need and challenge in the industry today.
I’ll give you some examples because keep in mind, a mortgage loan is not this thing that lasts for five minutes. It is a 30-year asset and from that perspective, it has a long life.
An investor may want to sell a loan as an example. So you’re an investor, you put this money into the loan, you own it, and now you want to move it. Because the loan has 30 years left in its life, and the borrower is tied to their house, it’s not like you just dissolve it, it has to go somewhere. So you sell it to the new investor.
That loan moves from the servicer that the current investor uses to a new servicer. So from servicer A to servicer B, the data points, that loan file, if you will, has to get lifted and shifted over to the new servicer.
I was just actually talking with a servicer today. For each loan that moves, if they do an initial scrub and they find a couple of problems with the data quality that comes over, they spend four to five hours per loan scrubbing data.
It’s a barrier to the business. And this is only in cases when they’re doing that level of review. They can’t always do that level of review. Lots of problems get missed.
Haas: How does BaseCap help with some of these things?
Smith: We have a client and they were doing a manual review on every loan. They originated over a hundred data points that were checked against documents.
With BaseCap, they were able to basically get to an auto-approved rate of half their loan volume automatically. So instead of having to go in and check each document in each data point, half of them had no problems, no issues. They can get auto-cleared.
After you automate with a vendor like BaseCap, you’re able to save that volume and go, “Okay, we’re not going to look here because there’s no issues here.”
Now for the other 50% where there’s problems, technology helps there. Again, because you’re not doing a hundred-point review technology can go, “Oh, here are the three things that are wrong.”
If you can get people to the three exceptions that matter to them, they can then fix those things and they can fix them better. They can go, “Okay, we constantly see these three issues, let’s work with the origination team and solve them strategically and solve them in the right way.”
That’s where there’s always this AI versus human element. And I’m always a big believer in it’s not one or the other, it’s both. It’s how do you use technology to work smarter and do a better job elsewhere where the people truly add value and make a difference in the business?
Ultimately when it comes to AI and machines and technology, those are best applied to repeatable use cases, things where there’s a lot of it. It’s no one’s favorite job to open a hundred PDFs in a day and compare them to the loan system. There’s things that people’s intellect can be applied to that have greater value for the business.
Haas: What are some of your goals for 2024 at BaseCap?
Smith: We had a really exciting 2023 to close, honestly. 2024 is just picking up on a lot of the exciting progress that came out of 2023.
We’re growing as a company. We’re starting to take on distribution partners.
I know we’re primarily talking about a mortgage base here, but this really is applicable to any data problem out there. So you can apply this to water quality data, you can apply this to healthcare data, as an example. We’re starting to see some of those early use cases as well coming into the platform.
We have generative AI add-ins that are being released. And our roadmaps for gen AI are super exciting. We were doing all the beta releases last year, so those things are starting to see production from a production use case perspective.
Those are just a couple of the items in terms of 2023 and where 2024 is headed.
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