What does AI in mortgage really look like in action? Rebecca Seward joins Digeo Sanchez to explain how AI isn’t there to replace underwritters or loan officers — it’s here to amplify them. She shares insights on how AI can streamline processes, reduce repetitive tasks and helo mortgage professionals focus on higher-value work.
Rebecca also explores how AI can drive long-terrm industry stability by distrupting the traditional boom-and-bust workforce cycle and supporting a more sustainable staffing model. This conversation breaks down practical AI strategy, showing how the right tools can surgace better insights and help teams work smarter — not harder.
Rebecca also explores how AI can drive long-terrm industry stability by distrupting the traditional boom-and-bust workforce cycle and supporting a more sustainable staffing model. This conversation breaks down practical AI strategy, showing how the right tools can surgace better insights and help teams work smarter — not harder.
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00:00We are live from Las Vegas.
00:08I'm Diego Sanchez, President of HousingWire,
00:11and I'm joined today by Rebecca Seward of Ocrelis.
00:15Rebecca, it's so great to talk with you today.
00:17Likewise. Thank you.
00:18So before we jump in,
00:20could you briefly introduce yourself and Ocrelis?
00:23Of course. My name is Rebecca Seward.
00:25I am Head of Mortgage Products at Ocrelis.
00:28We are an AI-powered data analytics company,
00:32is what we like to say.
00:33Of course, we do document automation.
00:36We leverage AI, ML, OCR technology,
00:40plus human-in-the-loop,
00:42to extract data and generate AI-empowered data analytics
00:48to drive faster, better decisions for lenders
00:51across multiple industries, including small business,
00:56but also, of course, mortgage.
00:58So we are here to embed ourselves
01:01into the digital lending ecosystems
01:03to help make faster, better decisions.
01:06Yeah, that's great.
01:07And AI is definitely a buzzword
01:09that you're hearing in the hallways here.
01:12How do you think about AI in practice
01:16as opposed to it just being a buzzword?
01:19Yeah, of course.
01:20So for us, we believe that AI is essentially
01:24an augmentation tool that enables lenders
01:27to make faster, better decisions.
01:29We do not see ourselves as a replacement
01:31for the human decision-makers.
01:33We believe that we have a part to play
01:36to enable them to be more efficient.
01:39So when we see the cyclical and seasonal nature
01:45of this market, of course,
01:46I've been working in this for 12 years,
01:48the ebbs and flows,
01:49we believe that you should utilize AI
01:53to enable your teams to scale more efficiently.
01:57And so I know there's this big buzzword around AI.
02:02It's not a random tool
02:06that just has all these different,
02:08I guess, pulls and levers, right?
02:12It's a human-empowered tool.
02:15The AI is only as good
02:17as the information that you give it, right?
02:22So Oculus, for example,
02:24is leveraging off-the-shelf tools
02:28like Google Textract and Amazon AWS,
02:32of course, or Amazon,
02:34but we leverage human validators
02:37to constantly empower our systems
02:40to get smarter and better over time.
02:42We feel the same way about the underwriters
02:45that are embedded in our workflow.
02:46So we actually read the information
02:49that they're giving us
02:50to enable us to get better over our analytics.
02:53So I think that humans have a big part to play
02:56in terms of making AI stronger and better
02:59and evolving it over time.
03:00Yeah, I mean, that's a big misperception, I think,
03:04that AI is going to replace humans
03:07as opposed to enhance humans.
03:09And it sounds like you're building product
03:12in that spirit.
03:13Exactly right.
03:14So I've been working in this industry for a long time.
03:17I understand the nuance of a mortgage underwrite.
03:20It is never as easy as just a simple person
03:24coming in with a wage earner income situation.
03:27There are now a lot of different ways
03:30of making income, right?
03:31There's Uber, there's TikTok, for example.
03:35And so we understand that there's always a part to play
03:39for the human underwriter or the human processor
03:41to inform the decision.
03:43So we are just here to make their job that much easier.
03:46We try to get them 90% of the way,
03:49present them with a decision-ready data point,
03:52and then enable them to make the necessary decisions
03:57to get to that credit decision,
03:59whether it was approval or denial or whatever else.
04:02So let's get really tangible.
04:05How do you help a lender either improve their processes,
04:11win more business, ultimately win market share?
04:14Of course.
04:15So we have a suite of products, essentially.
04:18I mean, we're an AI-first company,
04:20so we extract and classify documents,
04:241,700 different documents.
04:27So customers can, of course, leverage that
04:30for any data-driven decision point.
04:33However, our suite that is embedded within Compass
04:37does classification, essentially.
04:39So we are reading documents in from file manager
04:42or a listening folder within the e-folder.
04:44We classify those documents,
04:46and we place them into e-folder locations
04:49depending on the customer's unique taxonomy.
04:52So a human processor doesn't have to do that themselves, right?
04:55We standardize that across all of the documents
04:58that we receive.
04:59So every single time we see a bank statement,
05:02for example, we label that bank statement
05:04or we label an asset document, whatever else,
05:07depending on the customer's unique taxonomy.
05:09All the while, we've been extracting data
05:12off of those documents.
05:13And then we leverage that data to empower
05:17income calculations.
05:18So when a customer comes in,
05:20they see all the income calculations,
05:23whether it's wage earner, self-employed, rental,
05:26variable income, such as unemployment
05:30or retirement, for example.
05:33And then we also have a product that's in beta
05:36called Inspect, which does a data comparison
05:38of the Encompass 1003 to the document data
05:41that we process.
05:42So we can help expose things like undisclosed REO
05:46or a missing document for your initial needs list.
05:51So the history of mortgage workforce
05:57is one of boom and bust, right?
05:59We saw it in 21 and 22,
06:03big boom in hiring,
06:05and then 23 and 24.
06:07Unfortunately, big bust in hiring
06:10and a lot of people were let go.
06:11How do you think these tools
06:13that you're bringing to market
06:15help lenders not have such an accordion of hiring?
06:19Of course, it enables you to scale
06:21so much more efficiently, right?
06:23Every single time you see that refi boom
06:26or the surge in volume,
06:28a lot of lenders will handle that
06:31by hiring a ton of human collateral, essentially.
06:35So we believe that if you embed technology
06:38like Oculus or AI-empowered tools,
06:40it enables you to scale so much more efficiently.
06:43You essentially train your team
06:45to be 3x, 4x times more efficient
06:49to be able to process that many more loans,
06:51to be able to handle decisions
06:53that much more accurately and efficiently.
06:55And so you can essentially handle the surge in volume
06:58with the same amount of humans and human labor,
07:02but just enabling them to do it
07:04more efficiently through AI.
07:06You know, it seems to me that
07:08it's really important for lenders,
07:12especially the lenders,
07:13most lenders don't have huge tech teams.
07:16And so it seems like right now
07:18when we're still in the early days of AI,
07:19it's important to stay flexible with your AI strategy.
07:23How do you help your clients
07:26stay flexible with that AI strategy?
07:29Yeah, of course.
07:30So like I said, we involve our humans
07:32in our decision point.
07:33We're not here to present the decision
07:35and just allow you to accept it.
07:37We are here to present decision-ready data,
07:40but enable the customer to
07:43be flexible with that decision.
07:45So we, like I said,
07:49have a lot of humans in the loop,
07:53and we say that, a human in the loop.
07:55So every single time that we're seeing documents,
07:58we are learning from that.
08:00So we have a human flywheel, essentially,
08:03where we are seeing new documents come in
08:06and we see the human validation
08:08that's coming in from our verifiers,
08:10and it enables us to get that much better
08:13and smarter about our AI models.
08:16We feel the same about the customer involvement.
08:19So we actually read the different edits
08:21that they're making,
08:22the different ways that they're using
08:24our income calculation,
08:26and we get smarter about that over time.
08:29In terms of the human involvement
08:31and the customer involvement,
08:33we go out on site.
08:35We train our teams.
08:36We have AI-empowered underwriter courses
08:39that we teach these underwriters
08:42about how they should enable their teams with AI
08:45and, again, not replacing you,
08:49here to make you better, smarter.
08:51And so in terms of flexibility,
08:53I think we are just trying to teach them
08:56of all the different ways
08:57that they should be embedding this
08:59into their workflow
09:00in terms of training teams,
09:02making it more consistent
09:05across all of the processing,
09:07the underwriters, the loan officers,
09:09so that when you're bringing in new talent,
09:11especially, I feel like the mortgage industry
09:13is, I've been saying all week,
09:16quite incestuous.
09:17It's a lot of people that have been here
09:19for a long time.
09:20So if we want to attract new talent,
09:22like the Gen Zers and the millennials,
09:23for example, that are fresh out of college,
09:26it's using these tools
09:28to actually create consistent learning
09:30throughout all of your new talent.
09:32Absolutely.
09:33And if you look back at that boom
09:36that we saw in 21 and 22,
09:38there was a real shortage
09:40of underwriting talent
09:41during that time.
09:42So maybe this helps with that.
09:43Yes, absolutely.
09:45And I think that you will find
09:48with all this younger generation
09:51coming on board,
09:52they will be looking for companies
09:53that are more innovative
09:55and are open to leveraging technology
09:57like Oculus and other tech vendors.
10:00Because nowadays, I mean,
10:02imagine the kids in high school
10:04using ChatGBT, for example.
10:07They are going to want to leverage technology
10:09like ours to enable
10:12more efficient decisions.
10:14Well, what an interesting conversation.
10:16Love talking about AI.
10:18Thank you so much for joining me today.
10:19Of course, thank you for having me.