Cashing in on AI: Streamlining Bank Operations

0:02 You are listening to Leaders in Lending from Upstart, a podcast dedicated to helping consumer lenders grow their programs and improve their product offerings. Each week, here decision makers in the finance industry offer insights into the future of the lending industry, best practices around digital transformation, and more. Let's get into the show. Welcome everyone to another episode of Leaders in Lending. I'm met snow here with Will Robinson from and Capture. Will Welcome to the show. Hey, Matt, thanks for having me on. Absolutely Maybe let's get started by a little introduction, talk about what you do and Capture what they do, and how you got to the role of CEO there today. Yeah, it's kind of a wild journey. But in Capture we are a software company in the intelligent document processing space, so that means we have artificial intelligence that can read documents and understand what type of document is looking at and then can go find important data that in those documents and pull it out automatically. We work in a bunch of different use cases across a bank, mostly around the kind of loan origination and processing space for mortgage, commercial, consumer, indirect auto.

1:22 We do stuff in the branch as well, but essentially any workflow where there's a lot of incoming paperwork and there's important data in those documents, we can come in and really automate the manual data entry type tasks and the manual stare and compare. That is a prevalent in the back office of many financial institutions. Gotcha, Yeah, it makes sense. And I know, as we've talked a lot of similarities that we've had. You've been on both sides now in a bank and supporting banks on the service side. What's your experience been like on both sides of that you compare and contrast, like, what are the similarities and differences you've seen? Yeah, uh, you know, it's there's a lot of paperwork in banks. And it's funny. We have a famous story around our business, uh where we had a newly appointed chief digital officer of a bank, of a big bank, and we had a meeting with him and he confidently said, you know, my mandate is within the next twenty four months, we're going to have no more paper around the bank.

2:24 This thing is going to be completely digital. That conversation was twelve years ago, and needless to stay, there's still plenty of paper flying around and that chief Digital Officer no longer is in his role. It did not last very long, so uh, I think it's it's a it's a it's an interesting dynamic in our in our industry where banks are trying to become more digital, more automated, more efficient, but yet you've got legacy processes. You have a lot of regulations that drive collecting, uh collecting documents from your borrowers and your customers. Uh. And there's a lot of compliance uh that that still revolves around having a physical or even a digital copy at someone's driver's license or a bank statement or a pay staff And so how do banks kind of figure out ways to uh move into the next next, the next century, uh, but also uh satisfy these these demands on them. So it's a pretty unique. It's it's a hard thing relative to other industries. I think it's hard for banks to try to figure out how to how to navigate all this. Yeah, a lot lots we could dig into from there, for sure, especially like I said, the requirements around gathering uh documentation. I know we see that could be different to the state down to the county level.

3:39 Like how do you guys think about again, you think banking and regulation and government like being two very legacy driven, slow to change, Uh, areas like how do you manage across all that different nuance and process. Yeah, that's a good thing about about AI these days and as it's evolved. Uh, there is a lot of variance, and there's these you know, legacy regulations. Uh, every every local jurisdiction has a different way of doing things, Every bank has a different way of doing things, and a lot of the way, a lot of the policies at the bank are kind of entrenched, and uh, trying to completely overhaul that is is probably kinney, It's probably too hard for most banks and lenders. And so instead it's how do you use new technology? How can you use AI to really augment your existing workflows? You're not going to try to change the whole paradigm of the bank or the regulatory landscape under which you you operate, but can you use AI to automate some of the most kind of mundane, boring, routine tasks that that you've had to do in the past, And so, uh, it's it's interesting I hear you know, there's obviously a lot of talk around right now about AI This is something that we've been doing for years and years, so we're not we're not new to it, but we chuckle because, uh, finding like really practical use cases is still a challenge for a lot of a lot of banks as well as a lot of technology partners that want to sell AI stuff into into a bank. You have to show up with

5:13 a really proven, uh kind of uh really ROI effective use case, and that's that's hard for a lot of folks to do. Yeah, I want to dig into some of those use cases because I don't know if you've experienced this as well, and kind of getting the sense that maybe you have that when when banks talk about, you know, becoming digital or making their process digital, they're just taking that same like tired, cumbersome offline process and putting it on the internet. So they're not you know, thinking about how can we use these new technologies tools with AI to make it easier for the consumer or even internal staff, and it's just digitizing, uh you know, the same process. How do you think about are those things different or similar?

5:55 What's what's yours? It's really important at that, you know, and we this a lot when we start out with the bank and we start talking about implementing our own product and capture to help streamline, you know, a specific workflow. We have to get really specific. What is it that we're actually doing at the bank, What are we actually replacing? What steps are you guys doing today that either you're not going to have to do anymore or you may have to do them different. Uh. That change management process is probably one of the biggest risk points for a bank that's trying to adopt AI, UH, because if it does, you know, for it to work, it requires you to maybe think about changing your process and you can't just necessarily always layer it on the existing way you do things. A lot of times you should try to start with that, but uh, it will require maybe a different way of thinking about a specific process or people's roles and responsibilities.

6:46 And uh, you know, we've seen this unfortunately on our side, where you know, we'll come in, we'll build a really good solution that that can save that bank a lot of time, a lot of money. Uh, But then there's so much resistance to changing a process to accommodate this this this AI that like they don't get much value out of it. And that's where we have to have these hard conversations on it's not just the technology that's going to show up and solve all your problems. You have to be willing to change. You have to be willing to adapt, and you know, we're going to try to make that as easy as possible. But it's it's very much a mindset as well as you go into a you know, kind of an AI specific project. Yeah, that's really true about the change management of all of this, because, like you said, you want to get really specific on the use cases and but not make it a complete overall of a system, because then you kind of get paralysis analysis. So you don't know where to start if you think you have to rip and replace an entire process or system. So I'm sure you know, understanding the entire landscape of a process and then digging in on those use cases is probably pretty critical to getting folks excited about starting on that journey and then keeping going. Well, and it's it's you know, it's funny too. I guess this is human nature, but people, you know, people get very comfortable with the way they do things. And we had a situation recently where we had built a solution to help a bank spread all the tax returns that they gather as part of their small business lending process. So you know, a small business lender,

8:20 a banker at the bank is going to work with a barrower and they're going to be sending in tax return documents instead of the bank. Now, instead of these credit analysts or these processors having to manually go through the tax returns and enter all the income information, we do that automatically. We can read through the documents, extract the data, we put it in the system. And we were talking to one lady and about her team, and I said, hey, how's it working. She goes, Oh, your software work's great, but nobody's using it. And I was like, well why. She said, well, they don't like skipping this step. They really like manually entering all the data because they say it quote unquote helps them understand the loan better and get to know the loan and get to know the borrower and they can kind of think as they go along. And I kind of threw my hands up and laughed. I said, I don't know if I can do anything about that, and she laughed too. She said, no, it's it's hard to get people to think about changing their behavior, especially if it's becoming grained in the way that they work, in the way that they make decisions. And so we talked about a couple of kind of practical ways to maybe let these people trust the system to do it correctly and they can go back and review later. But that's that's a hard thing, you know. And I would you know, if you're if you are kind of an exec for a lender and you're thinking about, you know, implementing new technology, whether it's AI or anything else, I think it's good to honestly ask yourself, do I have a team that is willing to change, embrace change, try things differently, or can I go find maybe a few folks that can be leaders in this area and can kind of be the advocates for the change, Because if you if you're not willing to change or really push

9:50 that and push your team to change, whatever you implement, regardless of technology, it probably is not going to work very well. Yeah, that's true, And I think that's a really good story, really specific example, but maybe highlights some things that we could dig in more broadly around AI. As you said, like being able to trust us any system really is probably key to adoption and AI for better or worse. I think it brings with it a reputation of being a quote unquote black box or something harder to understand. So maybe we could start a little bit just like getting more specific around the termin as you've used it a couple of times, like when you think about, you know, what a capture does and how you guys use AI, like what does that specifically mean, or what are some of the approaches that you're taking to make make your bags easier. It's funny, Matt, we've been using AI and specifically what we use as supervised machine learning. I can explain what that is in a second, but we've been using this for like the last four to five years, and you know, up until about a year and a half ago, we didn't even say the words AI or say machine learning because it would just scare everybody. UH people have these preconceived ideas of what AI means, what it is when it isn't what it does at the bank, and we just felt there was so much barrier, there was so much kind of pre existing UH biases against it, that we really referred ourselves more to kind of an automation technology or automation platform, and it wasn't until chat GPT about eighteen months ago kind of entered the public lexicon and everybody

11:24 kind of got comfortable talking about AI and realized, hey, for better or for worse, I'm gonna have to figure out a way to use some flavor of AI going forward. That's really allowed us to speak I think a lot more candidly about it. And so, uh, you know, I won't get in all the weeds and all the types of the AI that are out there, but you know, there's there are several different uh strategies and techniques of using artificial intelligence. That's a really really broad term. Uh. You know what we do around supervised machine learning is we are it's almost like we have you know this this for that almost behave someone like like a fresh college grad that's maybe in their very first job, and they're smart, they're willing to learn, but you really have to teach them what the job is and what they're expected to do. And so in our use case, you know, we have trained our machine learning models to you know, recognize certain types of documents. So we'll feed it samples of documents, and we'll feed it a bunch of different driver's licenses and say hey, these are driver's licenses.

12:24 And then we'll feed a bunch of samples of bank statements and say hey, these are bank statements, and all the while the system is looking at these documents and looking at maybe some of the similarities and some of the differences between the document types, so that you know, one day I can show up with kind of a mystery document, give it to the system, and they can look at it and say, Okay, is this a bank statement, is this a driver's license, or is this something else? And you know, the more that you could train them and teach them and give them hints, give them rules, give them reminders, or reinforce kind of positive decision making the better it is at kind of I would say, guessing the right the right outcome in terms of what documents looking at or the data it's looking for. So that's a really kind of simplified version of how our our machine learning works, our AI. That's actually very different than what chat GPT is as you as you guys have heard around the generative AI. That's a that's a different type of AI than instead of kind of guessing an answer uh to a specific question based on previous training, this AI is more about creating new content and trying to figure out what's the next word that I should say in the Senate so that the sentence is complete and makes sense. Relative the question that was asked, so use cases there for generative AI are are are a bit different than kind of the AI we use for reading documents. And I think it's important for you know, someone who's looking to bring on AI to understand, Hey, tell me how your AI works. Tell me how much of a black box it is, because you know, in our case, we have kind of intentionally designed ra AI to where it's less of a black

13:54 box. In some ways. You would say, well, maybe it's not as effective. I would argue, well, we at least know how it's making the decisions it's making, and so every time it makes it a decision, we can point to kind of the way it went about that versus it being some just magical thing that comes up with an answer and we don't know how it gets there. So that's a big you know, that's a big part of it. And then the other thing is, especially for banks and lenders credit unions, how are you know, where are you using AI in your organization? It's one thing if you're using AI like ours, which is just really kind of automating these Monday tasks, you know, doing replacing manual data entry. It's different if you're trying to use AI to make a credit decision, or you're using AI to have a conversation with a customer or provide kind of a more native kind of conversational onboarding experience for a customer. Those all have kind of their own unique risks and challenges. And again for lenders specifically, there's a lot of regulation that would say, hey, if you're going to put AI in to make a decision on the loan, you better have a lot of confidence that you understand how that system is making that decision so that you can prove it's not discriminating against certain types of borrowers. Hey there, former host Jeff Kelviner here to let you know about an exciting opportunity to strengthen your understanding of artificial intelligence and financial services. If you want to better understand the fundamentals of how AI models work and how they will impact the financial services industry, your in Luck Upstart is launched an online course and certification designed to do just that. This course will give you the knowledge you need

15:26 to lead the conversation about how your organization can best leverage and manage this revolutionary technology. If that sounds interesting, check out the course as it's currently free for a limited time. You can learn more at upstart dot com slash AI Certification. Again, that's upstart dot Com slash AI Certification. Thanks and now back to the show. Yeah, totally. I think every application, we keep coming back to the idea of use cases, but defining those use cases, understanding the risk and trade offs are definitely important. I think when you're embarking on this kind of a journey or deciding if an AI application is right for a certain use case and really understanding uh, the benefits you get there And you didn't you mentioned compliance too. I think sometimes you know, folks maybe view that as a more adversarial relationship, or different levels of government will come out with their perspective or recommendations on use of AI. But we talked before about you know, how how can you use AI more of a proactive way or being more compliant or helping uh, you know, stay within regulation, regulation and guidelines. How have you guys thought about that and in ways that you can actually use this technology to help you maybe show up better in those cases. Yeah, it's it's funny. There has been this growth in UH regulations for banks and lenders and how you know, UH, there's more and more data that has to be reported to the government to make sure that you're not discriminating in your lending practices. There's recently been a revamp of you

17:02 know, the CRA rules, which have been around for fifty years, but those have expanded. There's this upcoming you know Dodd frankedin seventy one rule. And I realize I'm saying a lot of acronyms here, Matt, but I'm sure your listeners will know what these are. There's just this growing body of reporting data to the government. And you know, what we've found for our use case is there's actually a really there's a really good solution here. A lot of times these compliance teams are having to go review you know, loan packages to make sure that the data they've pulled out of their system that they're about to report to the regulator is accurate and complete, and so they spend a lot of time hunting and pecking through big loan package PDFs, you know, double checking various data points against the system. It's very time intensive and kind of cumbersome process. Most teams can't even check every loan. They don't have the staff or the time to do that. So we'll just sample a few loans. And that's a great use case for AI. And again we're not you know, it's it's it's not something where we're manipulating data and reporting without your you know, it's not this black box scenario. It's simply saying, okay, AI, I want you to go through all of my loan packages and I want you to pull out the data that I need to report.

18:12 I want you to compare it to my system, and I want you to flag any discrepancies. And you know what we found is ninety to ninety five percent of the data points that are being reported, they're accurate, they're consistent, and you don't need to do anything with them. But maybe it's five or ten percent of your data points are they're missing data fields or it's a different name. You know, it should say Will Robinson, but instead it says Matt Snow and you need to go figure out why that is. And so it cuts down a lot of this kind of mundane data scrubbing activity that these compliance teams do. So I think that's kind of an interesting thing about you know, you hear about compliance and banking and regulations going to restrict AI. Ironically, banks are using AI to facilitate their obligations under the regulations.

18:57 So, you know, I think that's kind it's kind of been an ironic twist of AI. AI helping banks even though it's going to get cracked down on I think more and more. Yeah, I think you're right. I think that's a good example too of where, especially with human resources that are very expensive and limited, like, can you use a combination of these two tools, right the AI that can help you focus and prioritize or find higher risk signals. I think we see that a lot in terms of you know, verification in underwriting as well, Like you're seeing tens of thousands of applications in any day, which ones do you focus on, where do you put the human capital against that? And where should you focus on? Types of documents or types of fraud? And being able to partner those two together, each with their unique skills and either handling volume or you know, novelty in the data I think can be a great win. Yeah, you know, We get asked this question a lot, and you know, it's like every conference you go to, that's a big question, how do I use AI my bank? And I kind of chuckle because I had not heard many people show up on these panels and give like three or five or seven or even you know, ten good answers, Like usually there's kind of one use case or two. And I think it's because sometimes you hear about all the potential of AI and it's hard to figure out where to start, and there's so much hype around what it could do that I think people miss like what

20:29 are some really really practical things that can do today? So we always just encourage people, and again this is unique to our business a little bit, and the fact that we you know, we process, we process documents. But go look through your existing workflows and look at where people are spending a lot of time doing repetitive tasks that are that are highly rules based. Right, you probably have a rule or a process because in those situations you can use technology to automate that. It doesn't even have to be AI. So much of what a load origination system is, which doesn't been around forever, is really just making sure you're applying your bank policies and bank rules around a process and around an underwriting set of decision making. So that and there's no AI in that. So I do think it's an It's important you know that you first take stock of do we have tight, repetitive processes that can be automated. The flip side of that is, don't try to go use technology or AI until you've got that part down. We struggled with this a lot with some of our customers. We will go they will be so excited about using AI. Oh, you know, you're going to automate our loan origination process and you're going to help, you know, make sure that we're following all these rules and we're collecting all the right documents. And we're like, yeah, that's great. Tell us your process, tell us your rules so we can make sure the AI is following your rules. And then quickly you realize they don't really have clean rules. They don't have consistency on what the rules are. We did. This was a couple of years ago, but we rolled out a solution based on what the project team at the bank had said. These are our rules and this is how we process loans, and so we we started testing it with actual users and they revolted. They were

22:03 like, this thing is terrible, and we said, well, why is it terrible? And then we uncovered it's because they could not agree internally on what even the rules were or the standard processes were, and so they thought that, you know, it was AI's fault. And we were like, hey, we we can make this do anything you want, but you have to have consistent standards at the bank that you can follow. Otherwise this is not going to help at all. So I think starting with kind of, you know, making sure you have clear process, making sure that if you're going to be using data, your own data to help with an AI enabled project, making sure that data is clean and well structured is really important. Again, this happens a lot with us. We look at we look at documents, and so a lot of times we'll go into a bank and say, hey, pull us samples of these documents so we can make sure our machine learning model can read it. Well. They don't have their documents safe down very well, and they don't know where they are, or they have a miss misclassified or misindexed, and so we start with a really dirty sample set and it just doesn't go well, So, uh, there's a there's a lot to do here on the AI front. But I think just taking a step back and looking at hey, what are the what are the most mundane, routine, repetitive things that my team's doing every day that's taking a lot of time and maybe it's not very much fun and how can I go automate that? And they'll typically be a good use case there. Yeah, that makes sense. So as if you were advising somewhere, you know, at a bank or a credit union, thinking about automation use of AI, how do you think about the you know, bill versus buy or what are the different dimensions? Is it like, like you said, the type of data that I have, or maybe for a third party would have access to

23:36 the skills I have natively, you know, and evaluating each of these different use cases that might exist. How would you advise someone to think about, you know, is this something we should try to tackle on our own or start looking for help outside. I think it's really a function of how big is your bank, how big is your budget, and what are you trying to do. I do think the biggest banks, you know, the JP Morgan's of the world, they have the budget, in the in the appetite to kind of build as much as they can in house. At the same time, we work with some of those those biggest names, and what we found is the problem that we're solving for them is really important to the business, but from like a from an IT or product perspective inside the bank, it's not very it's not very important. So the bank's not going to get to it even though they could build it there, it's going to take them a year or two to get there, and so the business is like, hey, I need this solve now. So you know, I would say, if you're like a line of business leader at a lender, I think just being honest with yourself and with your team on maybe my bank says they want to build it, or they say they can, or they say they're working on it, but when's it really going to be ready? Versus if I can find a vendor that can solve this, and what's the cost of that and what's the timeline up getting that in, I think you'll you'll realize even if the long term plan is for the bank to build it themselves, I think you can probably get pretty far with the vendor up early on.

24:59 So in terms of by most banks, I mean just cutting to the chase, most banks are ill equipped to build technology internally. Just most banks are. They're just not set up for that. It's not in their DNA, it's not their culture. They don't they don't have success with it. So again, most banks, it's better to partner with vendors who've done this for a long time, and again, do do really good diligence on your on your vendors. Ask for referrals, Uh, make sure you know that that they've got a long body of evidence of this being successful. Ask for a lot of details around their process and how their technology works. A good vendor, who's who's been doing this a while, we'll have no problem sharing that, and we'll have good documentation around that. If you feel like you're working with someone who's maybe trying to figure this out alongside you, that's okay, But just be aware of that that they're trying to figure it out alongside you, and it may not. It may not be go as well as you as you're hearing in the sales process. So you know, I think there's gonna be more regulation around this as well. We were talking about that earlier, Matt, kind of you know where where's regulation going around this? The state of Colorado just passed a law requiring organizations that use AI and these high they call it, these kind of these high risk or high impact areas to have a lot better governance around what is the AI doing, what data is it touching, what is it doing with that data? How is it making decisions? How is it impacting the outcome of the bank, is it discriminating?

26:27 Is it being biased towards certain things? So I think more and more regulation is going to come out on that. And again, vendors like ourselves, we you know, we we have answers for all that, and so making sure that your vendor understands what's going on, I think we'll give a lot of confidence. Yeah, the transparency and trust I think is key. And bringing back to what you said earlier about you know, maybe the LMS and chat GPT bringing more awareness to the space in general. Have you seen your partners, you know, asking harder questions, getting into deeper diligence or are you still having to do a lot of education on you know what what some of the terminology is, the mechanisms you're using in this space to help demystify some of that. You know, it's a it's a wide spectrum.

27:11 Most I will say this, most banks are doing a good job that when they hear AI kind of their the red flag goes up and they ask more questions, like, this is not just a routine. I'm not just buying you know more more you know, telephones for my office desks at my you know, at my company. This is this is something I need to have a better understanding up. And so we do a lot of education around that. And again, AI is such a broad term. There's so many different techniques that, like, what I tell you about how our stuff works does not mean every other AI vendor out there is going to work the same way. In fact, most of them won. And so getting a good understanding, making sure that your vendor can explain it crisply and simply, because you're gonna have to go turn around and explain it to your bosses. What is it this we're putting in, How does it work? How do we make sure it won't you know, run a foul of any of our fair lind regulations? How am I gonna get people to buy into it? And so I think if you can understand how it works simply, then you'll have more confidence that will work well. Yeah, I agree. And one of the things I've found is while it's certainly important, you know, pre contract and as a one time event, it's also an ongoing exercise because you know, companies like ours, I think, are constantly trying to find the next new innovative approach or like you said, AI is so broad that the way we're leveraging those types of technology is changing day by day. So it's really a part of the ongoing relationship. I think if you're using a third party to stay in touch with them and how they're evolving their thinking and kind of it's

28:44 like ongoing diligence of the relationship. Absolutely, I mean it's a it's a never ending If you find the right vendor, there's there's typically a good chance they can do more than just one thing for you. And if they can do one more than one thing, that means they're gonna be sticking around for a while and they want to do more than one thing. So I think it's important when you're not just looking at the technology or the specific use case, but talking about vendor and saying, hey is this a type of partner. Have they been around a while, are they going to be around? Are they really committed to my industry, MySpace, my use cases? Do they want to do more with me? Or am I going to be kind of a one trick pony where they just show up, do their thing and then leave and they never hear from me and collect a connector collector renewal check.

29:25 I think getting a good sense of that's important because you can probably go find, you know, just a handful or less of good vendors that can do a lot for you across the bank, and you'll make a lot more progress sticking with those lenders and are sticking with those vendors and going deep than trying to find thirty different vendors who kind of each do one thing for you. Gotcha? Well, maybe it's a good time to transition to a closing question for you. As I was thinking about our conversation today, I was thinking back to a book I read around this, Stuart Russell's Human Compatible. I'm not sure if you've read that, but always trying to find whether it's a podcast a thought leader book. I thought that one was really interesting just around the idea setting up controls and transparency around human and AI interactions. And while it was enlightening around AI and some of the topics we've talked about, it helped me think about even you know, human decision making and maybe processes we take for granted or that are more automatic. So I highly recommend that if you haven't read it, but curious if there's anyone you follow in this space to again kind of stay relevant and keep your thoughts sharp in this this area, whether it's a podcast or you know, maybe a writer that you follow. That's a good question, Matt. I would if I mean very transparent. I feel like I'm following twenty or thirty people right now and I'm still trying to figure out like who is who is real and legit in this because it's and I also think kind of the diversity of thought in this phase of AI. It's so early. You have well, I think I'll give

30:57 you an example. I think more Andresen he's a prominent venture capitalist. He wrote a really good kind of long form essay on AI and how we should be thinking about it, wrote it several months ago, so I do encourage people to read that. But he does talk about the concept of the Baptists and the bootleggers. Relating this back to the prohibition, that there are people who are going to really call for the the regulation of AI because they feel like in their hearts that's the right thing for the world. But then there are people who are going to call for the regulation of AI because they're the ones who are going to benefit the most from it. And so I think in this early phase of AI and how we use it, I think diversity of thought is really important so you can hear kind of the different sides and then feel confident, you know, making your own conclusions on what does AI mean for us as a as a world, What does it mean for me in my business, what does it mean for you know, my own professional development going forward. I did read an interesting quote recently, and I can't remember who said it, but it is essentially, you shouldn't worry about AI replacing your job. You should worry about a person who knows how to use AI replacing your job. And so I think, I think there's sometimes this fear that AI is going to replace everybody, and I think there will be pockets where AI comes in and can automate some of the things that people do, but I don't think those are the most interesting jobs. And I think annially is going to create a lot more opportunity for a lot more people and be a net benefit, just as we've seen with every major technological advancement over

32:30 the history of the history of me and kind. So maybe a conversation for another day we can have, Matt, but I encourage you read, read diverse thoughts and continue to kind of keep an up in mind on where this all goes. Yeah. I couldn't agree more on that, will. I think that's really it's a really exciting time to be in industry as these tools are coming on board, and I think, like you said, the transparency, that openness in the dialogue. Let's find ways to test and see what works and be open about the pros and cons and see where this goes. So yeah, I'd love to follow up with a conversation on that and appreciate your time with me today and thanks again, Well, yeah, thanks so much for having me. Matt really enjoyed it. Upstart partners with banks and credit unions to grow households and expand consumer lending through its leading AI lending platform.

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