- 7 months ago
- #mortgagetech
- #aiinfinance
- #tavant
In this conversation with HousingWire’s Allison LaForgia, Dr. Olav Laudy, Tavant’s head of AI, discussed his path into mortgage technology and how Tavant differentiates its approach to AI in an industry saturated with hype.
“I am a traditionally machine learning guy,” Laudy said. “Then, naturally, you get into AI right when machine learning transferred into the AI domain. I joined Tavant, and Tavant has a mortgage arm. And naturally I was curious on how the things were already automated and how I could help automate that more using AI.”
#MortgageTech #AIinFinance #Tavant
“I am a traditionally machine learning guy,” Laudy said. “Then, naturally, you get into AI right when machine learning transferred into the AI domain. I joined Tavant, and Tavant has a mortgage arm. And naturally I was curious on how the things were already automated and how I could help automate that more using AI.”
#MortgageTech #AIinFinance #Tavant
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NewsTranscript
00:00I'm Alison LaBorgia with HousingWire and today I'm so excited to talk to Dr.
00:08Olav Laudy from Tevant. How are you today?
00:10Very well, thank you. Thank you very much.
00:13So I am excited to talk to you and let's start with your journey into mortgage technology and
00:21how you became focused on AI driven solutions for mortgage.
00:25Right. So I think I am a traditionally machine learning guy. So then naturally you get into AI,
00:36right? When machine learning transferred into the AI domain. I joined Tevant and Tevant has a mortgage
00:43arm. And naturally I was curious on how the things were already automated and how I could help
00:51automate that more using AI. Now, when it comes to AI and mortgage specifically,
00:57our industry has a lot of hype. So I'm really interested in hearing how it's different for
01:04Tevant and their approach to AI implementation and how Tevant has strategic solutions to solve
01:14real friction points when looking at AI technology. Right. So I think the key is
01:21understand the mortgage process as professionals act, right? And we deploy a lot of actual mortgage
01:30professionals, people with business backgrounds in mortgage, and they understand what the friction
01:35points are. So when I propose something, they can say yay or nay to it. And that allows me to develop in
01:41the right direction. But other than that, from an AI point of view, we're never deploying AI for the
01:49sake of AI. You need to deploy AI because it solves indeed what you correctly mentioned, a friction point.
01:56Anything that takes a lot of time for someone can be potentially enhanced by an AI feature. We're not
02:03looking to replace underwriters or lenders in entirety. We're looking to enhance the process so that those
02:14people can do their work better, faster, smarter. That is very interesting. So it sounds like there's
02:20really a meeting of the minds in Tevant's approach to AI. You have a full understanding of the process,
02:27and then you have a subject matter expert like you who says, okay, where can we alleviate some
02:33of these pain points to make the people better? So I'm really excited to dig into that a little bit
02:38more. Can you share an example where AI streamlines tasks for the loan officers or for the underwriters
02:46in this process? Yeah. So here's a very simple example, right? An underwriter typically is asked by a
02:54loan officer, hey, can you tell me the status of the loan, right? So that's a very common question.
03:00So what does the loan officer, what does the underwriter need to do? They go into the loan,
03:04they kind of look what is missing, what is the status, where we are, how much still need to be done,
03:13and then that is one task. And then they have to kind of all write that up and send it to the loan
03:20officer. So a simple email like that will take, if done properly, 20 minutes. If you take three
03:26minutes for it, you're basically just saying like, okay, it's fine, don't worry about it, right? But
03:32that is also not fair to the loan officer. So an AI, an agent that is aware of the loan can do that
03:40searching, right? So we have built this, and it is an AI set that's called chat with loan, right? And you
03:50you go in there and say like, write a note for the loan officer. The AI reviews all the elements in
03:58the loan, what is missing, what's the status, what is the time to close, etc. And then writes that email
04:05as an update for the loan officer, the underwriter, and this is a very important point, looks through
04:12that email, says like, this is right, or this is not right, this is what I do want to say or not,
04:17it has the ability to update something. So this is the human in the loop. And once done says click send.
04:24And that process now in its entirety will take three minutes instead of the 20 minutes. But in those
04:31three minutes, a complete email is actually being compiled as something that is relevant
04:38and useful for the loan officer. And that's just across one file, which is fantastic. So if you can
04:43save just, I mean, what sounds like upwards of you're cutting 75% of the time out of it for multiple
04:53files, it sounds like a massive time saving, which is fantastic for everybody who's involved in
04:59processing that loan. So I'm really curious if you could tell me about how this extends over to the
05:06borrower experience, especially for consumers who we know are now remote, time constraint, or even
05:13managing a complex financial situation. Right. So we have our mortgage avatar called Maya. And Maya
05:22basically accompanies you from the start of your loan all the way till the end and into the servicing
05:31end of it. So when you start your mortgage application, Maya greets you and will basically introduce
05:39herself, letting you know what she and what she can do. Right. What she can do is very clear. She's not allowed to
05:47say like, Oh, I can give you this mortgage for X percent of interest rate. But what she can do
05:53is help you fill out forms, answer questions along the way, and do the important checks. And so for
06:01example, if you need to upload a 1040 tax document, and you upload a different tax document or a different
06:09year or something else, right, it is immediate immediately noticed. And then that gets fed back
06:17to you. So like, well, this is not the right document. Now, if you compare this to the traditional way,
06:23normally, you would upload the document. And then that goes to a loan officer, either the loan officer
06:29sees that or not goes to the underwriter, then that goes, the underwriter says, yeah, but this is not
06:34what I need. And now this whole hop needs to hop back. And that gets two, three days down back to
06:40the borrower, the borrower lives in anxiety thinking like, Oh, well, I get my loan or not. Or even worse,
06:46has a parallel track with another lender is exploring that. And maybe if that goes faster, he will drop you.
06:52Right. So the here you see an avatar that is truly functional in doing things for you at the moment that
07:01you need it. Another example is the clearing of conditions after they get access to your your bank
07:08transactions. For example, this is a very common hurdle for people, they need to write a gift letter,
07:15or they need to explain some transactions. Well, if an avatar kind of scans your bank transactions,
07:21and says like, hey, already explain me this because I because I know that we're going to get answers
07:27or questions around this, you just save valuable time for both the borrower as well as the underwriter.
07:34That sounds like some really fantastic improvements that are now enabled by utilizing AI in the borrower
07:41experience. However, there the industry at large has some concerns around compliance, safety and
07:48usability. How would you address those concerns? Right. So I think the compliance is
07:54if you compare, if you would replace the word AI by computer, right, and you would kind of realize how
08:08people are freaking out over all compliance, what what happens if you have an AI do this, right? What
08:14happens if you have a computer do this, as compared to the previous process where people would write
08:20everything out. So obviously, it is not the computer who decides who gets alone or not, but the computer
08:28makes the whole process very smooth, and frictionless. And so with AI in place, that is an even further
08:38enhancement of that process. So you always have a human in the loop. And you always have things tested,
08:46and make sure that nothing goes wrong here. So I think the human in the loop, as a final approver,
08:56is key to this whole process, and not thinking that you just hand it off to a big machine, and then
09:03you hope for the best. The other point is obviously, like, you do deep user research, right, experiments to
09:13make sure we're actually conducting one at the moment, to see if people would like to see an avatar on
09:20screen, or they look like would like to see a speech avatar. So you kind of see some some waves on the
09:28screen, if the avatar speaking, and then how that functions, if they do have questions for this avatar,
09:35if they feel that the avatar can can help them in that process or not. Or would they, for example,
09:42rather type something in, right, it can well be that for for certain groups of users or borrowers,
09:51they would rather kind of ask a question and not have a like a presence on the screen. Personally,
09:58when when I looked at this, I find an avatar at the screen, and filling out forms with me and for me
10:06is lovely, right? So I if I can just talk to my computer, I say, Okay, can you fill this out? Can
10:11you fill this out? Or if I am not entirely sure what I need to fill out, I can ask questions, or I can
10:18describe what I want. And the avatar smart enough to take from what I described, and then fill the form for
10:24me, I think that is the next level of interaction. But we're testing that out as we speak, to see what
10:30is the best way and even let people choose their their mode of interaction with this process.
10:38You just mentioned a couple features that you're looking at implementing and testing. How do you see
10:45AI continuing to transform mortgage operations and borrower engagement? And what innovations are you most
10:52excited about? Well, we're currently, apart from the voice interaction, which just matured, I guess,
11:00in the last half year, we're exploring something that we refer to as policy as code. And so traditionally,
11:08you would have, like all your guidelines. And if you were to automate that, you would have to translate
11:14them into a computer readable rule set. And that rule set can be used in a scoring or a rule engine to test
11:24against, like the actual mortgage details of a borrower. When you have something like policy as code,
11:36you're just kind of omitting that complete step, right? You have the borrower's details, and you have
11:43your guidelines, and you have an AI that very smartly goes through this. Now, obviously, you cannot,
11:52if you try to kind of think about this, it is not that you have like a chat GPT, and you provide your complete,
12:01say, guideline document or guidelines documents, right, into chat GPT. And you add all your borrower
12:09information, and you ask the AI, please figure this out. That's not how it works. You still need to kind of
12:16cut things up in manageable bite size chunks, and then ask that to the AI. But you'd be surprised how strong
12:24AIs are today, to take very complex rules, and then apply certain conditions to that, and data to that,
12:33to see if things work out or not. In fact, so much so, that if I try this out, and I'm working with those
12:43complex rule sets, that I will need to really sit down and five minutes kind of compare details to
12:49understand the complex conditions, whether they apply or not. And you provide that to an agent,
12:55and the agent says, okay, here you are, this is the outcome. And so I think that will lead to
13:03much faster processing, as well as much more concise compliance.
13:09Dr. Elaudi, thank you so much for walking us through Tevant's approach to strategic AI
13:16implementation in mortgage. It sounds like there's so much that you guys have accomplished in such a
13:22short period of time. I'm so excited to see what the future brings. Thank you for joining me today.
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