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Q&A: Freddie Mac looks to stay ahead of the tech curve

Everyone in the mortgage industry is looking for the next big technological innovation, and the government-sponsored enterprises (GSEs) aren’t immune to this challenge.

Andy HigginbothamAndy Higginbotham, chief operating officer of Freddie Mac’s single-family operations, knows the situation well. A former chief technology officer, Higginbotham worked for decades for lenders like Chase Bank and Citibank, leading their technological integrations in the primary mortgage market to stay ahead of the competition. He spoke to Scotsman Guide Media about doing these tasks for a GSE, as well as working to make sure that lenders and borrowers are taking advantage of the latest tech advances to streamline their processes — and maybe even save a little money along the way.

What is Freddie Mac doing to stay at forefront of technological innovation in the mortgage industry?

Well, one of the things that we do is that we have a whole team that we call our business-partner integration team. They basically kind of scan the industry for potential fintechs (financial-technology companies) — and really any partner — that a loan originator might want to use in the overall loan-origination process. We have relationships with, really, all of them. And as we mostly hear from our lending community, they'll come to us and say, “Well, we're working with such-and-such vendor and such-and-such business partner.”

For us, we just make sure that we're constantly building our tools to integrate to all of those vendors that lenders identify as ones that they would want us to be linked to. Chances are, if there's one asking for it, then there's probably 10 more that potentially are using that same vendor and could benefit out of that. We have a team that's dedicated to doing that and we work constantly to integrate all of our tools.

Do you have a recent example of a success story that integrated tech and big data to help lenders and borrowers?

One of the things that has really ramped up is ACE, which is our automated collateral evaluation. I’ll talk a little bit about that one because that’s one of the best examples. It’s an example of where we’ve used historical appraisal data that we’ve gathered over the past. I think it’s probably [using data from] around 10 years now. And we’ve augmented that data — that’s where big data comes in — with probably five or six other sources of information about home values, as well as the quality of the actual home itself.

All of this data available out there triggered us to create a model that we felt comfortable with. [We take] some of the key transactional data that [lenders] send to us, and then as long as we know the property address, we can spit back whether or not an appraisal is really needed, and we can get them value, etc.

We have found huge utility in that, and we’ve built it into our Loan Product Advisor so that [lenders] can do that very early in the process. They can do that as soon as they’re pulling credit and getting a credit view of the customer. From a benefit standpoint to the consumer, that’s $500 on average throughout the country that they can save. That’s a big win for the borrower.

For the lender, we know now that they save between 12 and 16 days, depending on the type of appraisal. Twelve days is a huge dollar savings for the lender. We measured that, and we know now also that the lender saves about $1,900 when they get an ACE. That’s a huge benefit to lenders who had been dealing with $8,000 to $9,000 per-unit costs for the last few years.

Another one has to do with some of the fintechs that are out there. They provide asset and income data that’s leveraged by the lenders to automate the process for the borrower. The borrower gives [the lender] authorization to retrieve that [data], and they have fintechs go out and retrieve that, either from financial institutions, workplaces or banks. They’re able now to send that data to us directly and we can give them, basically, a review of that data. We can say something like, “Yeah, this income is going to work for this transaction and the asset information is going to be sufficient.”

That has, by itself, another impact on the timeline of another 10 to 12 days of improvement. That savings is another $1,300. If you have both of these things together, the overall savings — because there’s some overlap in the days between the two — is about $2,700 for the lender. That’s so meaningful.

In today’s tech-involved landscape, consumers expect automation and, in some ways, the mortgage industry has been slow to catch up. How long has Freddie been working on making sure its technology offerings are meeting borrower and lender expectations?

The journey for me started when I first started working at Freddie, which was about five years ago now at this point. That's when I had actually proposed the idea of the loan-adviser suite to the senior management team here. My background is in the primary mortgage market — I had run operations and I've been a [chief intelligence officer]. I knew enough about the process and knew that we were so far behind in this industry compared to others. And I’ll say that, since then, we’ve hired and blended in many people from the primary mortgage market that have passion around this subject. This is what we think about all the time: How do we make it better?

And the way that we know we're doing it the right way is that we're constantly talking to our lenders and partnering with them on each one of these tools as we build them. They're telling us where the pain points are and we're just trying to attack it. We feel like it's a responsibility of our system to make sure that the entire industry benefits. To your point, many of these things exist in other businesses or other lines of business that are not mortgage. And it's a little surprising how far behind we are. But it’s just time to get it fixed and slowly we’re getting it right.

What are the new pain points that lenders are mentioning? 

I think it's all about at the very beginning when the borrower first comes in. How do you make that transaction as simple as it can possibly be and really get the friction out of the process? It's just very painful to go through. So, it's about speed. How do we get the processing speed to go faster? And all that means is these tools that we're building, they need to be at a loan-officer level. They originally were built for underwriters. That's still a few days after the transaction begins and that requires a handoff from the loan officer to the underwriters.

Instead, we’d like to make sure that our tools can be used and leveraged as soon in the process as possible. I think that’s what every lender would hope to do also. They’re pushing for things like, “How do I get all these tools upfront?” And the way that we're dealing with that is by starting to take some of these bigger [suites] like Loan Product Advisor apart into components. Loan Product Advisor does all these different things, like credit-asset income, appraisal waivers or appraisal approval. We're breaking those up so that we can handle it by discrete components. If [lenders] want to know sooner in the process just about the appraisal fees or just about income, then they'll be able to do that. That gives them more capability to do things upfront, which is how they save the money.

How often does your team look to other industries for inspiration in innovation?

Frequently. We have a group that’s called our innovation lab. I have them look at other industries and look at types of technology for the future. We’re always thinking about what else you could use. For the appraisal side, for example, we’re looking at tools such as drones and satellites. We’ve talked to a couple of government agencies where there’s that capability available. I’d love to see all that be much different and leverage these things that we see elsewhere.

We’ve [gotten ideas from other industries on] things like machine learning. We applied machine learning to make our models go faster, our development go faster. We’ve been actively looking into that and artificial intelligence to evaluate our data. We can evaluate tons and tons of data at this point that used to be a manual review. Now, we can run literally millions of transactions through how we’ve set our models. And those are adjustable — we can adjust them up or down and make sure that we’re having the best credit quality possible, while also making it very clear when there’s an opportunity to add value to the process and make it less expensive.


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