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The Connector Podcast - Future-Proofing Mortgages: OPER's Vision for Digital Transformation and AI Integration with CEO Geert
Are you ready to revolutionize your approach to mortgages? In our latest episode, we sit down with Geert, OPER's visionary co-founder and CEO, to uncover the future of mortgage digitization. Geert takes us on a fascinating journey from his days as a management consultant to his current role in the fintech industry. We tackle the pressing issues of borrower anxiety, outdated reliance on paper documents, and stringent compliance demands. Geert reveals how OPER is reimagining the mortgage experience with a fully digital platform that still honours the human touch, significantly easing back-office workloads through cutting-edge technology.
The conversation then shifts to the dynamic and ever-evolving European mortgage market, especially the role of Generative AI (Gen AI) and the daunting challenges of data privacy compliance. We delve into how high interest rates are pushing lenders towards more efficient, cost-effective solutions and examine the critical experimental phase of Gen AI. Geert emphasizes the importance of building robust control layers and maintaining human oversight to ensure these innovations are effective and compliant. We also get a sneak peek into the future of mortgage origination software, exploring advancements like guided sales, renovation advice, and automated underwriting decisions. Join us as we envision OPER’s ambitious expansion across 27 European countries and redefine what’s possible in the mortgage industry.
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Koen Vanderhoydonk
koen.vanderhoydonk@jointheconnector.com
#FinTech #RegTech #Scaleup #WealthTech
Welcome to the Connector podcast, an ongoing conversation connecting fintechs, banks and regulators worldwide. Join CEO and founder Cohen van der Hooydonk as you learn more about the latest available trends and solutions in the markets.
Speaker 2:Welcome to another podcast from the Connector, and today I've got with me Geert from one of the most biggest and most promising fintechs out of Belgium. It's a real honor to have you. Oper, can you tell me a little bit more about you and your company?
Speaker 3:Thank you, koen. Super amazing to be here. So I'll start with my company. So I'm personally co-founder and CEO of OPER. So I'll start with my company. So I'm personally co-founder and CEO of OPER. We are a Belgian boring B2B SaaS company and we help banks on the digitization of their mortgage journey. But we'll probably talk a bit more about that today and about myself, jef van Gieterkoven, asset co-founder, ceo, now being an entrepreneur for roughly six, seven years, and before that have always worked on the beautiful borderline of banking and technology as a management consultant at another startup on the product side and also as a consultant, independent consultant to banks. So that's a little bit on my background and a little bit on OPR.
Speaker 2:Oh, thank you very much, and I still remember the day that you switched, because that's where we got to know each other. Yep, indeed, since you guys are in the mortgages business, it's often a business that is not well understood by many, so could you tell us a bit more about what are the general challenges in the mortgages industry?
Speaker 3:Yeah, challenges per definition are not well understood. But on the other hand, I think a lot of people in their life have bought a house or have bought an apartment and I don't think many of us just could put the money on the table that just say here is 300,000 euros or 500,000 euros for a house. So everybody had to do with the mortgage process. And I mean I've been now on the digitization. I've not been on the digitization side for roughly 12 years. I've been working on this, on this thematic, and I think there are two things that happen in this journey, especially in this context to contract from you as a borrower thinking about I want to make an offer on a property, can I afford it up until you're going to a notary or a conveyance lawyer signing all the documents? And there are two main issues if you want to digitize this.
Speaker 3:The first thing is what we say borrower anxiety and actually and that's also the reason why 100% fully digital application processes don't really work, because a borrower wants to talk to somebody about trust.
Speaker 3:Not every mortgage project is the same. So I mean, if you want to bring that to a digital Uber-like experience, that's really tough. That's the first problem and a lot of banks are struggling with that. And the second one is the involvement of paper and compliance and making sure you can cover the risk. And that's what we always say as a mission statement we want to burn paper with our software, we want to reduce the amount of paper and reduce the amount of verifications, but that's also really tough. But we feel that if you, as a bank, want to say, look, we really want to bring the mortgage process to an experience, these are the two things you need to tackle. You should make sure that a client has a fully digital experience but still has the trust to talk to somebody and, at the same time, it's really alleviating your back offices by reducing the amount of data points and paper that needs to be verified, but you still want to make sure you reduce the risk of your loan.
Speaker 2:And then you guys go even a step further because you're bringing AI in this process, and I think today I'd love to go a little bit deeper on the new product that you brought out. So could you tell us a little bit more about this and what has been the inspiration of this?
Speaker 3:Yeah, so the inspiration is well, of course, when ChatGPT got released by OpenAI, we were, of course, curious. Everybody was jumping on it. So we started going a bit of a level deeper because, I mean, my team and I have a bit of experience really in machine learning as well. So we kind of knew what could we do with machine learning, what can't we do? And what we quite quickly found out is, if you look at LLMs and if you look at GenAI, it is really good in predicting in context what is the next word going to be, good in predicting in context what is the next word going to be? Like it can, and I mean there's also the visualization part, but especially around predicting text in context, um, it is extremely powerful and it is plug and play because the foundation models basically provide you with a pre-trained set.
Speaker 3:And then, if we looked at the problems we were facing, helping loan advisors give better advice for a mortgage is just a textual problem. Basically, I will upload 20 documents, five salary slips, a tax, I mean processing the data out of that and basically saying, hey, this is the loan application of Kuhn. Actually, kuhn's profile looks really well. It benchmarks very well against our risk policy. Again, it's a textual problem. So we got very excited about the fact that a lot of problems we had could be solved by LLMs but of course, need to be solved in a compliant blah blah blah way. I can go a bit deeper into implementing it for real, but that got us really excited and we just started brainstorming. We developed 20 use cases, started to meet our clients, discussing them, got like a top three and then just started doing the work.
Speaker 2:And can you shed a little bit on those examples that eventually made it?
Speaker 3:Yeah, sure, the first one that's now productized is our data and document verificator. It's a very simple use case, but it's an incredibly powerful one. So it basically takes all the documents and data points that you will submit as a borrower and basically starts cross-checking all the data. Example you said I make €3,000 a month and at the end of the year I have a bonus. Well, what we would do is we would take all the documents that you have provided to the bank, we would process all of them and we would say well, actually Kuhn makes €2,500, and this bonus hasn't arrived on his account. So we would push a warning sign to the loan advisor, but also to the mid-office, to say, hey, something wrong is here, or employer address of the employer isn't correct.
Speaker 3:So this is a very basic use case, but you wouldn't know how much lenders still do this verification Like bank advisors would do this verification and they would do it in the back office, and so we are now like in a full cockpit, fully automating that, and the results are pretty amazing actually.
Speaker 2:What intrigued me here is that you were set. You said that you're contacting clients to find use cases. Have you done the other way around? Have you already got feedback from clients?
Speaker 3:yeah, so we have now trying three clients that are in the pilot program with us. Um, there two have given their goal to put it into production and the largest hurdle actually was to convince compliance about. You know how are we using LLMs is data leaking, et cetera so that we got covered. But they really see a like the processing time for especially a loan advisor to prepare for a loan conversation or to submit the loan application. We actually see that. You know we can reduce four to six hours of the verification time right now and this is really a pilot case. You know. I think when we get this into production, we will be able to share much better metrics.
Speaker 2:And can you share a little bit about, because I think it's a very valid point that the big topic was data privacy compliancy. Can you share a little bit about that and maybe general also, what is sort of the trend within the European mortgages market and how does that fit?
Speaker 3:Yeah. So I think in the mortgage business, what happened in the last two I mean two years ago was the worst, I mean in terms of volumes, mortgage markets went back to 15, 16 years ago. So what you see is that the narrative has very much changed. At, let's say, 80% of the lenders are more talking about efficiency. And you know, how can we, even in an increased interest rate environment, how can we, you know, do more with less? As opposed to we wanted to go out and sell 5,000 different mortgages, etc. So it became very much a cost case. So that, of course, means that Gen AI and using that as a lever for efficiency is a very hot topic right now. But I mean, everybody's experimenting now. I think we're still in the cowboy days. You have a lot of consultants, you have a lot of consultants, you have a lot of internal experiments, but what I think is a key challenge now is like how are you going to translate an experiment that yields results into something that you can implement into your business processes to really yield that efficiency case? And when I look at Gen AI, okay, building the models, it's contextualizing the models. I think if you have smart people around, that's, I'm not going to say easy, but it's doable.
Speaker 3:But then there are two other big challenges and one is compliance. We kind of went I mean, the cloud providers there are quite helpful, but still it is a lot of navigating about. You know how do you host models locally? Do you do it in a separate instance, et cetera. So there is some infrastructure work that needs to be done and also that's why we build up our own control layer to make sure that also the model doesn't hallucinate.
Speaker 3:It sits behind a safe user interface so it's not some chatbot that can go wild. So there's a lot of safeguards we build in, actually from when you use the software in the user interface up until where the data and the models are being stored in the infrastructure layer. So we really did that fully vertically. I think that is one part, and the second thing is we provide it in our software. So we're a P2P SaaS, meaning that it doesn't have to be integrated into a user interface or a mainframe application. But I think that's a bit our vision on it. But I can tell you, the compliance part and the let's say, the real getting into real implementation part is challenging.
Speaker 2:I think that the biggest challenge you often hear is traceability. How is that then being tackled within the application?
Speaker 3:Yeah, that's why we simplified the process. So I think that's why we build our own control layer. We really simplified the use cases and we bring the human in the loop. So it's a very good point. What you bring there is that. That's also why I personally that's maybe a provocative statement I don't really believe in the chatbot use cases, Like sometimes we have clients that come to us and they say, Ah, Oper, can't you do a chatbot for a borrower to be advised on a loan? We'll get there, I'm sure.
Speaker 3:But today soon getting it unhallucinated, unhallucinating, talking to a client about your mortgage portfolio.
Speaker 2:Um, I wish you good luck getting that past the regulator, with the a, with the eua no, no, I agree, and and there's also a misconception about a lot of people when you say llm, they immediately make the link with chat gpt. But I what I hear from you it's it's a dedicated system.
Speaker 3:It's been trained for one single purpose, which also eliminates a lot of the hallucination, per definition already exactly the metaphor that I like to use and I don't know if it's accurate, but I mean I know ms dos, back in the days in ms you were also typing scripts etc.
Speaker 3:And you know that's indeed so. That's that's the first implementation. You, you basically chat or you basically talk commands to get something back, and I feel prompt engineering and chat gpt is very it's very similar in that regard, but at a certain point in time, windows came and windows basically built a ui layer on top of ms-dos, so it was actually easier to use, it was more correct, you didn't have to know any script language, et cetera, et cetera. And my prediction is that with vertically integrated AI, we're going to see the same. We're going to see user interface being built on top of the foundation models to make it much more controllable, much more traceable and basically make it much more error prone.
Speaker 2:Well, talking about how the future looks, could you give us a glimpse a little bit about what's next in oprah after ai?
Speaker 3:yeah. So I mean, apart from ai, I think that's that's a very important pillar for uh, for the product development. So I feel, around the I mean our software is really built around mortgage origination so we help the whole sales trajectory. So you'll see much more features on guided sales, renovation advice, much more complex loan cases. I think that's something on the product. You'll see quite some very neat features popping up. And then also on the underwriting part, I think I mean now we have the whole document completion and document analysis. We now already have also a prototype that basically prepares an underwriting decision. So it basically writes like a human hey, I would approve. I wouldn't approve Kuhn's loan for this reason.
Speaker 2:Oh God, there goes my house.
Speaker 3:Yeah, and then today, I mean we have a European focus. So we're currently working with 15 financial institutions in six european countries, um, and we're actually preparing now to to do to get into 27 countries, uh, as of next year.
Speaker 2:so we are fully ramping up for that uh, to be, let's say, make the solution fully eu agnostic, and that we can serve any bank that wants to do digital mortgages better it's an amazing story and I'm glad that this is sort of the pinnacle of the conversation, because I started with one of the biggest fintechs in Belgium and the fact that you came up with those figures I think it was just a demonstration of my definition in the beginning. I'm sure there's a lot of people that would like to hear more. How does that work? How do they get best in contact with you guys?
Speaker 3:Oh, you can always send an email to sales at opercreatorscom. It sounds like a very generic mailbox but it's very effective to get to us Because if you go directly to LinkedIn you can follow me, but getting a response is going to be slow. I get a bit too much spamming there. So sales at opacreatescom, I think, is the most direct channel, but also feel free to follow us on LinkedIn we're all cooking up or just go to our website, because again, that also gets. I mean, our contact form is also getting followed up quite nicely.
Speaker 2:Thank you very much for sharing your story today in our podcast and, yeah, looking for more news in the future.
Speaker 3:And thank you for joining us. Thank you, koen, it was great.
Speaker 2:Have a nice day. Thank you also to the listeners. Thank you and stay tuned for more FinTech news on this channel.
Speaker 1:Thank you. Thanks for listening to another episode of the Connector Podcast. To connect and keep up to date with all the latest, head over to wwwjointhaconnectorcom or hit subscribe via your podcast streaming platform.