 This is our June 2023 hyperledger presentation. So, before we get started, I want to thank the financial markets special interest group and the hyperledger foundation for their ongoing support and making this meeting possible. I think we have a really good agenda for today. But before we do that, let's go ahead and take care of some what I call house cleaning. As always, please note that this meeting is being reported and it's under the umbrella of the hyperledger foundation. So we ask that everyone abide by the antitrust policy and code of contract. And the antitrust policy states that we avoid discussions of companies specific pricing products and projects. We don't make negative remarks about other companies or products, and the code of conduct means that we treat each other with respect we never discriminate, and we communicate constructively. We really support hyperledgers policy of openness, equity and inclusion. And for new participants and I think we will have quite a few today. We welcome you and if you'd like, please introduce yourself in the chat. And if you have any questions or any specific areas of interest, let us know in the chat we want to make this as interactive as possible. So our agenda for today we've already gone through the general introduction and welcome antitrust and code of conduct will speak really briefly about some of the hyperledger community information. James will walk us through an update blockchain in the mortgage industry, and then we'll have Devin daily, the chief revenue officer from true will walk us through true what they are, and their use of AI and blockchain and q amp a. For this slide in each meeting, and that's very purposeful. This is to reinforce that we're all on the same blockchain, and now AI journey. We may be at different points along that path for today's discussion in particular and we're all still learning about AI, especially with the advent of chat GPT. Today's session is part of a series on AI and blockchain, and we'll focus on education. As I mentioned in the previous slide will continue to dive deeper into this topic. As speakers come in and talk about how they're using AI and blockchain, and we discuss actual use cases. Okay, I'm just going to go through the next couple slides really quickly. These slides are intended for those people that are new to the group and would like more information. So this slide provides a link to some of the different resources that are available through the Linux Foundation hyperledger foundation. And as you can see second from the bottom the link to our subgroup wiki to access that information you'll need an L F ID and this information will help you get that. And then lastly here's just some blockchain training. I've taken this training this is how I got introduced to the technology, and it's extremely helpful I highly recommend it. So with that, I want to turn it over to James Hendrick who's going to walk us through the state of blockchain. And Marvin, thank you very much if we can move on to the next slide. All right so starting out with community by nascom. So this article provides a high level overview of blockchain and distributed ledger technology, as well as the basics of smart contracts so excuse me everybody. And Marvin mentioned if you are new to this group. This is a great article to start with. If you're not familiar with these concepts they do give a really good quick overview to help get you grounded. The article goes on to discuss how blockchain and smart contracts can improve low management through speed security and cost efficiency. Some of the topics we've talked about prevents previously. It also goes into data science and how it can prove improve low management by providing lenders with valuable insights into borrowers and their behavior. So this is information such as credit squirting fraud risk modeling customers segmentation all of these can be analyzed and addressed through blockchain. Moving on to the next article so JP Morgan Chase is developing a chat GPT like software. You know as we all know Jamie diamond is not a big fan of crypto but he is very bullish on blockchain technologies, and what they can provide the financial industry. So the company has applied for a trademark product called index GPT this last month on index GPT is touted to tap cloud computing software using artificial intelligence for analyzing and selecting security's tailored customers needs. The technology has a range of possibilities within the financial industry. Thanks, including Goldman Sachs Morgan Stanley have been testing it for internal use. And these include ways to help Goldman engineers create code or answer Morgan Stanley financial advisor queries, but JP Morgan may be the first financial institution aiming to release a GPT like product directly to its customers. So JP Morgan they've got about three years on the trademark in order to get approval. It's uncertain yet exactly how JP Morgan will deploy it but the article does close discussing how financial isers have long feared the arrival of technology. That's going to be good enough to displace their role in markets. So far those fears have largely been yet to materialize and we don't know that we'll be seeing them. The third article we've got on here is from Black Knight. So Black Knight's been evaluating the applications of artificial intelligence to such areas analytics AVMs mass customization process improvement. The article discusses how AI and ML and the mortgage industry started over a decade ago really. And Black Knight is currently using an AI driven point of sale transforming the customer experience and delivering a highly personalized assistance when and where customers need it. So Black Knight's slow walks buyers through the next step provides a self guided process for the customers. They've also got AI and correspondent lending that's assessing the completeness of the loan packages and settling conditions when information is missing in underwriting they've got a hard automation that can be used to maximize underwriter efficiency and reduce risk. And in the lead generation space by using predictive analytics to identify opportunities and deliver a highly personalized communication again with the customers. Marvin the next slide. This is probably the biggest article we have this week. Last month just a couple weeks ago MBA education hosted an event sponsored by Adventus, there's a dentist. Marvin band to again was the moderator. We have a panelist that came together talking about leveraging AI blockchain and newer technologies and today's challenging environment. We had Leah Price from figure Mark D'Angelo PB from Finlocker myself. We started off with an overview of blockchain AI and data fabrics and what they are. The panel went on to discuss challenges or changes in the industry over the last seven years towards these technologies, and how you know CEO CTO CIOs are all evaluating these technologies and what their opinions have been. As we see them more integrated into our environment. We also talked about how companies in the mortgage industry are implementing these technologies, as well as at the recent MBA technology conference AI was a big topic of discussion there. You know one of the big focuses that groups were looking at was the risk using chat GPT proprietary data, potentially allows us chat GPT to learn from that data which can then be shared with others so there's definitely a concern about that it's really all about how it comes to deployment. Ultimately, the conversation was followed up with a discussion on data analytics. It's roughly about a 60 minute webinar it's available from both the MBA education website as well as our hyper ledger wiki. Marvin anything else you want to share about that event. I know I, other than it was, I think a very interesting panel discussion it was very interactive. Everyone on that panel was extremely knowledgeable about the different technologies. And I think what was most interesting was everyone's ability to talk about some of the challenges, and how companies can take a lightweight approach using POC's using small cases, a way of making some of these new technologies, a bit more approach. Yeah, fantastic thanks for sharing that Marvin so if you get an opportunity and you've gone our, you know, a great presentation to attend we do have a deck that went with that presentation to that has a ton of information and the appendix as well so if you guys are in need of that at all feel free to reach out. And then Marvin next slide. And then you know, last a monthly reminder this is our wiki page. Do you know take a look at the link below will drop it into the chat as well. In fact, I think I'm just dropped it in there for us. You know, save it as a favorite the LF ID that you need to set up there's instructions in the upper right again it's a free service to to set up if you're interested in any of our previous presentations over there on the left hand side. And then over the month over month presentations and there's links there to both the videos as well as the, the PowerPoint deck that went along with it. And then over on the right hand side you'll see we continue to update and add our newest articles. We're looking for a different additional information, as I've mentioned we've curated in the last year and a half well over 200 articles on blockchain. We've started to add some AI articles in there as well so feel free if you don't find anything on the, the wiki site that you're looking for. You know be a pleasure to have you reach out and we'll share what we've got. Marvin next slide. And I'll pass it back over to you. Hey, thank you James. Great information as usual. Next, I'd like to introduce everyone to Devin daily he's the chief revenue officer at true and board member of the New Jersey MBA. He's an experienced senior executive with the demonstrated history of working in the computer software industry. He's skilled and has significant expertise in banking enterprise software sales e commerce and entrepreneurship. He was also part of a panel discussion yesterday titled how AI brings trust to borrow data. This was a very informative session and I highly recommend it. We'll see if we can share a link with the rest of the team as well. So with that, let me turn it over to Devin. Hey, thanks Marvin and first let me thank everybody for joining today. We appreciate it and want to make. I want to start with, you know the concept and we're going to talk about artificial intelligence we're going to talk about blockchain we're going to talk about the two how the two can interoperate. And I think it's always important to start with the basis of both of those and the basis of both of those is data. And I start there with, I start with that one because it's the truth but at true. That is the core of our mission in helping lenders manufacture automation quality data. You know what I often say to, and I've said for years even, you know I spent the last five years at a large independent mortgage bank, where, you know, we consider robotic process automation. And it's a robotic process automation is a wonderful technology but we've got to do something about our data, because the only thing that RPA is going to do for us is help us make bad decisions faster. And so when we think about manufacturing, you know really good data. It's about leveraging machine learning to do that it's about finding documents and direct source data and being able to cross compare it. You know again when I was in the mortgage industry, not as a not as a solution provider, one of the things I was focused on was continuous QC. I was focused on that because it just seemed that we spent an inordinate amount of time at the end of the process, trying to make course corrections. And then we would incur what seemed to me to be all of these unnecessary fees might be increases in dwell time might be suspense fees, you know, whatever it equated to it just seemed unnecessary. Something I had never done before I had an opportunity to look at post closing, and my God did I get an appreciation for the mess that is post closing. And, you know, I thought I was going to be able to go in and pull reports and categorize and codify all of the various issues across all of the 10 or 12 investors and. Yeah, I was, I got an education in four hours and said my God, I have a new appreciation. And actually at the same time, I was evaluating replacing our current OCR vendor. And there were a number of reasons for that. And it mostly because, you know, the state of the current OCR technology was, there's, it's not no human in the loop, there are a lot of humans in the loop. There's a lot of humans in the loop and in order to go from a 68% per page classification accuracy to a 90 plus per page classification accuracy and by the way, automation quality data is 95% plus. Okay, that's a huge gap. And my assumption when I was a lender was only 24 hours is okay, but 24 hours is okay, and there's a lot of manual intervention so is it really any better than what I'm doing now. The basis was, I've got to get above 9095%. I've got to have that automation quality data extracted after you got to have great document classification because only then can you extract data, and only then can you compare across the document set, which will give you automation quality data. And what I found was, you know, one that's the basis because on top of that that's where you layer solutions. That's where you put your income analysis that's where you put your asset analyzer. That's where you put your post closing QC right, that's where you put your audits. But if you're doing all of those functions based on bad data, we're in the same position. So, you know, fast forward. That's one of the reasons why I landed it true. I was really impressed with, but frankly what I said to them was, what you're doing is so great, and so far advanced with regard to the rest of the industry and your competitors. But you don't have any solutions on top of it. And a year later, we're launching and our solutions as you'll see in a moment so you can go ahead and advance the slide Marvin. One thing about it is where we began and why we're so good at what we do. Our founder and CEO, Ari Gross is a PhD in computer vision, and has been doing this and is so passionate about the pursuit of excellence. You know when we get to 99 five we're at for one of our clients in terms of per page classification accuracy. He's the first one to say, how are we getting to 99 eight. There's no resting. There's no resting for us. And so that's where we began you know we began coming out of a research lab that was led by Ari. And as you can see we have a couple of fairly large clients up there. And we've pursued since then, you know a real focus on the mortgage industry. And so today we recognize 850 plus document classes, and we extract about 8000 plus data fields, which gives us, you know when you look toward the other end of the spectrum here when we talk about solutions. That was the basis and some of you may have seen this. I think we presented this as digital mortgage before I was part of the company. What we at the time called our audit tool where we can certify across documents that the documents are consistent. And then if there are inconsistencies we can throw those up to be manually attended to right, and then we can take those documents the authoritative source documents and compare them to date in the loan file. So we can do a really thorough audit. When you think about that for a moment we were really using that and still using that in a post close QC world. Our objective in our strategy going forward is to still start there because I think one of the tenants that we have to begin with is like any technology, especially any new technology we've got to experiment we've got to have a proof of concept as Marvin mentioned, once we prove it, that's going to help us with what the really difficult part is and the difficult part is not technology. It's the humans that use it. It's the organizational change it's the process change. And if you're not comfortable or you question the technology and or you don't have internal champions, stop. So, begin with the proof of concept and our recommendation, because it can be so powerful is start with post closing QC. Because in post closing QC now you can add a layer of intelligence coming out of there, right, on top of all of what I just mentioned in terms of that data analysis and data validation and data audit. And we do that via business rules. So we can ensure that you know the note rate is the same from the authoritative source to all the other documents and when it's not, we throw you an exception, right, and we do that via business rules. Why do I mention that because when a business rule fails that begins our quantification of where the issues are in your manufacturing process. So now within a couple of months I can give you insight you've never had before. And now we can sit with lenders we do is say listen here or where your defects are right here's where we started here's the proof of concept, and you've done the manual review and you trust the data. So now let's shift left. Now let's go into the origination process and catch these. We would treat it like a manufacturing process right. It is a manufacturing process. Let's shift left and deploy this solution to catch it at the point of entry. You know we all know the 110 100 rule. It takes a dollar to collect and correct. It takes $10 to correct it and $100 when you're done. So why spend $100 and in the mortgage industry it's more like 10101 thousand dollars right. So let's spend all that money and post closing QC, when we can identify where our defects are and where they're being injected into the process and shift left right solve the solution at the point, and that solves a couple of things, and you can advance the slide. It, it solves for the organizational change working backwards let people become comfortable with the technology and comfortable let them have assurances that they're catching this in a way and at a speed that humans never could. And we're not here to replace humans. Right. That's not what this technology about you know we talked about earlier replacing financial advisors. I think we're going to replace financial advisors. I don't think we're going to replace loan officers but what we are going to do. They're going to be the intelligent operators of AI right Leah price we talked about Leah earlier she educated me I talked for a while on this and Leah said oh yeah there's a term for that it's called prompt engineer. And those are the people with the domain knowledge that asked the really smart questions to drive the artificial intelligence. And so we're not replacing humans. We're bringing them to their highest and best use based on their knowledge, you know, no longer are we staring compare folks no longer we document classification or data entry people. We're using our higher order skills. So, what does the technology do and at true how do we specifically do it. So OCR technology has been around for years at true we have our own OCR engine but most importantly we've deployed machine learning machine learning to learn documents but machine learning also to learn from where we still have humans in the loop. You know, as I mentioned before, we have a bunch of customers when I tested the platform out of the box it was 96.7% accurate. The same. It still means that I've got to touch 3.3% of the documents. Now that's vast different than touching 100%. It's an even, you know, and a vast difference from touching, you know, 30% of the documents. That's a 90% reduction. In fact, but how do we do it. Right. We don't use OCR technology the way it used to be. We use contextual classification, which means, you know, for a guy, you know, not a guy in computer PhD and computer vision that's me. It's, I explain it the way, you know, Ari explains it to me it's the way a human reads the document. We have a large corpus of mortgage related terms, we know what documents. They occur on. And so when we're reading those documents we can say this is an income statement. This is W2. This is a closing disclosure. This is an LA right and we classify accurately and then can extract the data. Right. The other thing that we're actually seeking a patent on right now is something we call provable correctness. The basis of provable correctness is for one thing you've got to prove that you're more accurate than a human is doing the same task. And that you can improve over time humans tend to, you know, reach a ramp of performance there's a lack of domain knowledge to domain specific knowledge. My performance doesn't improve with machine learning and with machine learning, my performance can dramatically improve over time and dramatic over time can be 95% accuracy to 98% accuracy right because that's still when you think about that. That's a 60% improvement from, you know, 95 to 98. And then we have our machine learning models. And one of the things that, you know, there are a lot of mortgage lenders out there, and we have some as our customers will say, I'm not necessarily, you know, I don't really want to contribute what you learn for me to everybody else in the industry. And that's a valid statement. And so what we deploy is we deploy both our global catalog what we know, and then our local learning catalog which is what we know about you and what we've learned through you. So you keep your learnings. And we have our global learning catalog and that's the way we assuage those concerns. So, let me pause here for a moment. Are there any questions about how we do it specifically. And obviously I can't go into everything but if not then let's roll them the next slide. So, Devin, I do have a question because what you said on provable correctness really stands out to me because I spoke with a bunch of OCR companies and every time they come in and make their pitch for one of our clients they their pitches, give me 10,000 samples, two months of time and $100,000 in fees and then I'll train my software for your problem. I mean, you know that quote. So what you're talking about it is something different. And I want to understand that difference and I think you're getting to it when you start to talk about the technology but I wanted to have that context. Yeah, and I think, you know, I think what makes the difference is a true we're focused on the mortgage industry. So we you're not going to incur that cost. We have a global catalog, and our global catalogs probably going to match 90% if not 95% of your needs right here and have custom docs we need to recognize of course you are. But the vast majority of the docs, you know, we're going to recognize right out of the box so there's no no learning opportunity that we need to do. Yeah, unless you have custom docs. And we do that rapidly we can actually give our local learning engine to our clients. So there's no professional services engagement, unless you say which a lot of customers say, hey, I don't really have somebody that does that and you know we're really busy closing loans over here so can you do that, but we actually enable our clients through the local learning to actually recognize the needs themselves. And over time, right. If it's their humans in the loop, right, their folks who are saying hey, you know we have a threshold, like any other AI engine or confidence level. If it doesn't meet our confidence level it goes to a human for review. When that human recategorizes that we learn from that action so we, you know we take that into account next time we make a decision. Okay, that answers that answer your question more. Yeah, absolutely thank you. Let's let's advance the next slide. So, we had a little overlay here so I think we talked about this, you know, already we have a mortgage specific AI, we are BPO free, right, but, you know I want to be really accurate here I think one of the hallmarks and, and some of our customers and prospects have said to us one of the things you know that's refreshing talking to you, you're not telling us where you're going to recognize everything and you're not going to touch anything that's just not true we're not we're not there yet with the technology. We do have customers we have one MI customer who's 99.5% accurate and granted they've been a customer for three years, but we started out about 94% accurate for them right and they deal with you know they touch a lot of loans. We do have we do have BPO partners we don't have our own BPO shop because we don't need it, quite frankly. So, 100% BPO free 100% BPO free is going to get you about 95% of the way there, you still need people to go in and do the exception processing. Hey, the threshold that you set for this CD was 75%. It came in at 74%, somebody's got to review it. Yes, it's a CD. So we'll learn from that the next time. In our documents we talked about we have really robust technology it's not just OCR we use machine learning. And, and again we've been vested five years specifically in the mortgage space. One of the sexier things about this is because our classification is so good our data extractions of really high quality and a lot there's a lot of data coming out. The challenges that our clients have and this is a good challenges. I'm getting so much data from you that I never got before. How can I leverage it. You know how can I use other forms of AI predictive analytics to understand what customers are my ideal customer profile to maybe help prioritize for my LO is what prequels they should be following up with right. And to look at our servicing couple this with our servicing data and develop models, you know, to understand risk in the portfolio. So the next challenge I think for customers when we start to deploy this is, how can I monetize all of this additional data to further improve my operation. You know the next slide. And these are just some results I do want to point out the 300% increase in underwriter, underwriter productivity was specific to MI. We've talked about one of our MI clients there. The data captured, you know, 8500 data elements, really rich sources. The other thing that helps us do those 8500 data elements is to a really fine grained audit across the documents and across the documents to the data in the loan file, and we do that really rapidly. Next slide. So we talked about, you know, the use cases we talked about, you know, starting in post closing QC and then shifting left. We're finding a lot of use cases obviously in MI, but also with servicing loan onboarding as well as correspondent lenders. You can go to the next slide. And here are the solutions that we're working on. Right so I talked to you, you know, we're going to launch our income analysis engine next month we've got our post closing QC solution. We're going to do a partnership with clear capital will work on our collateral analysis and asset analysis, and then later this year we're also going to have a fraud detection. I'll say it again, you know, we're really fortunate to have been founded by Ari Gross, who's one of the world leading experts on font detection. This is such a specialty, but that is so important in fraud detection, right, not only how the documents and images line up but the font and the minute differences between the font so we're going to have a significant advantage when it comes to our fraud detection solution that comes out later this year. So, you know, we're looking at the solutions the loan verification the compliance suite compliance or true quality automation. And we talked about our audit and QC here so I think what's on the next slide Marvin is kind of where we go next and this may be, you know what, you know, where we started kind of overall discussion about AI and blockchain if that makes sense. All of this information really ties together well from an OCR AI and blockchain perspective. I was actually in an MBA technology forum meeting about three or four months ago, where they said that the IRS had actually come to them and they were working on an OCR problem. They were trying to trying to digitize a lot of the tax information that they received, and the problem that they're grappling with is their accuracy threshold was about 6566% and then the people within the MBA we're saying that from an industry perspective. It's more 9095%. So, what do you think are some of the key steps that a company or an entity like the IRS needs to go through in order to get their accuracy from 6065% initially to what we're seeing as an industry. That's that's a great question and it's, you know, while true has focused on the mortgage industry, we do have tax solutions, you know we have really large tax tax practices that use our, you know, use our technology so we're familiar with that space. I think it's about a combination of technologies right. It's tough to start from a generic platform. It's not where you don't have any industry knowledge, but the IRS is certainly in a great position because they have volumes of data, right that they can rely on and back test on. I think it's really just about, you know, deploying the right resources, I mean, you know, years ago, I got in introduced to unfortunate to live fairly close to Princeton University. And I got introduced to several and you know if you know anything about the applied mathematics field Princeton University has the best program in the world. And I got introduced to 12 PhDs were focused on applied mathematics, and these are the guys that solve problems, you know it's the PhDs and applied mathematics that can solve those problems, you know, and, and by the way, going from 65 to 95% is a mammoth task. Believe it or not going from 95 to 98 is an even, it's probably an order of magnitude even larger than the 65 to 95 so they're probably going down the wrong, going down the right track I should say forgive me if any from the IRS is listening. It's a big problem and it's going to be time consuming and I think it's, you know, it's about bringing in people with domain knowledge. You know the right domain knowledge to get that done. It is a mammoth task. And, you know, perhaps, you can refer me to those folks after Marvin. I'll see if I can dig up their information. One of the limitations that they were facing is that they could not use any type of BPO services because what the people in the group thought was okay if you're trying to go from 60 to 65 to 290. You will need not necessarily an army, but people to actually support that step. But that's not something that I think the IRS as a government agency can can utilize so I think that was one of the problems they were grappling. And you know what, here's what I'll tell you about that. We face that problem and we face that problem currently, as we think about expansion. And I'll just say maybe expansion a little bit north. There are laws stricter than those in the US with regard to privacy information and data can't be moved outside of the country. So what true has developed because we have this large purpose of knowledge about words and phrases used on us residential mortgage documents, we've started this automatic redaction process. So, potentially you could use a BPO process if it's 100% automatically redacted. And then you have to determine right if it can't be 100% automatically redacted. So what's the nature of the information that I'm giving out and is it coupled on this document or across documents with any other so I think that's a that's a problem that can be solved pretty readily. I think we'll have that solved in the next three months. Great, that's a very encouraging to hear. And you mentioned using word recognition, because going back to the MBA webinar. One of the things that we'd mentioned with the MBA audience is from a chat GPT perspective. It's built on I think 100, 176 billion tokens. Those are letter combination word pairs so in the word recognition approach that true has. How does that compare in contrast to the token approach that chat GPT uses. Are they compatible or why misunderstand the whole process. You don't misunderstand it all you, as a matter of fact you've nailed it and those tokens can be applied and associated with multiple documents. And then it's about the relative use that that one token compared with the other token to make, you know, the likelihood that this is an income statement versus a pay stub, right. Yes. And so you're 100% right in drawing an analogy to it it's just that the, you know, the body of knowledge the language model is a lot smaller for us. And quite frankly that's why we just did a test against Google and AWS with regard to pay stubs and w twos and our results were stellar. And it's because of that contextual classification. Great. Are there any questions from the audience. I have a ton of questions and I'm really excited about this topic and and I saw the presentation that you guys did yesterday but I don't want to monopolize this discussion since we do have Devin here so if there's anyone else that has any questions by please feel free to type it into the chat or just to log into the discussion. Okay, if there aren't any other questions from the rest of the team I did want to get your thoughts Devin on how blockchain ties into AI and what you guys are doing around true I when I was speaking with Devin at the MBA tech conference, he says that blockchain can be a basis for that common understanding of all of this mortgage information if you start with and this is going to be a crossover simplification. So if you start off with correct data and a common understanding and that forms the basis of the blockchain, and then apply AI to that, you have a robust technical solution. Yeah, yeah. I'll say a little bit differently and I'll draw a direct analogy to what my objective when I was still, you know, on the lending side of this was, you know I talked about continuous QC. When you think about continuous QC right if I can take my continuous QC efforts and somehow guarantee and certify the actions that I've taken. And create a fast way to audit it, aren't my loans, you know I used to say, you know, maybe flippantly aren't my loans worth more, you know, on the secondary market, right. And so you begin to ask that question and say, Well, how really could I engineer that and I think I think the answer is in in Ari's answer, and the way I thought about it was in the way I think about blockchain. You know I've heard a lot of in you and I talked about this Marvin, I've heard a lot of people say I'm going to build an application on top of blockchain and and to me that's the antithesis of blockchain right you don't want to build an app keep keep your keep your applications built on the proper technology and and for for me. The continuous QC and that rapid audit capability was the ideal use of blockchain and here's what I mean by that. We're using documents, we do intake of direct source data. Right, maybe I'm using form free maybe I'm using plaid I'm getting or I'm getting income data from a document. We do a fraud check on that document projects fine. Right, we hash that document, we take that hash, and we store it in our system of record but we also put it on the blockchain. We extract data from that document. We take that data we put a hash on it, we store it in both places, and we create a link between the document and the data. We make a decision and run a calculation. We do the same thing that same process step three create hashes store them separately. And now we've created is this auditable trail. Right, we make a decision based on those calculations do the same thing. So, at the end of the process, what we have is really a completely auditable and certifiable process that says, I got this document or this data from this place at this time the borrower uploaded at the borrower, you know signed into form free and got the data this one, however we obtained the data, we certify that process we store it. We extract data from it, maybe it's just an XML file or JSON file or we've extracted it from a document. We run calculations on it, and that whole process is certified. So if you think about it, you know, you think about eliminating costs in the back end of the process, and there are a lot of costs there there's a QC cost whether your QC is internal or your QC is external. When you begin to, you know, use TPR firms, there can be a significant cost there. And so, is there a way to say to the TPR firm, I've done a lot of the work for you, or this third party has done a lot of work for you, is there a way to lower that overall cost, not only lower the overall cost but make this a lot faster. Right. So at the end of the day, the TPR firm is lowering their labor cost. The lender has, you know, certified this loan and perhaps can get, you know, to cash flow a lot quicker and start producing a lot higher quality loans that are potentially worth more on the secondary market. If they're worth more, I'm certainly getting, I'm certainly speeding up my cash flow. And so that's the way I think about, you know, and obviously that's the inner play of AI and blockchain because we're using AI and machine learning to do all of that data certification and data on it. So it makes sense. Absolutely. And Devin, the way you just described that the interaction of blockchain and AI, the use of hash, the how to expedite processing, and I can't help but think that you must have been sitting in on our design discussions, what we were building out our EOC, because you pretty much articulated the problems we faced and then the approach we decided to take. So I think that's what a lot of people, at least those working within the blockchain industry are realizing that that's the best way to utilize blockchain is to minimize that processing cost, use that technology in a more efficient fashion. So yeah, that was great. Yeah, great architecture. It was, you know, the reasoning behind that it was, you know, I used to say you go to, you know, you go out to Silicon Valley and you make a pitch and this is 10 years ago you just use the word disintermediation, just one one deck, and they'll give you a check for $10 million. You know, three years ago was hey I'm building something on blockchain and here's a check for $10 million. Yeah, yeah, I think people have just driven a lot by that even though it was probably the wrong decision. Yeah, and now we're trying to use that same type of approach with AI. I've been pitching AI I haven't gotten the check for five or 10 million yet but I'll let you know how that goes. All right. Yeah, let's have one question. Don't laugh at this but I've always wondered about this. I joined sorry half hour late. But looking at this today and sticking to mortgages, why can't I get a mortgage today in two minutes and close in three minutes. What stops us considering considering I have a, I do have a credit score like most people do. So, I don't today understand why I can't get a mortgage, not that I'm in that market, but why couldn't I get a mortgage in two minutes and then bring in a new technology like AI chat to GPT with all its flaws that I've been looking at. Is that the expectation in the future I mean what is it vested interest around that closing cost of $57,000 or what prevents that from happening, because I can get a car loan that fast but I can't get a mortgage. Yeah, yeah. Mortgage has got a backed asset the card to drive it off. And a car's mobile, right. I can smash it in five minutes. So, why is what prevents that from happening today. So, I don't think the objective is wrong. I think, you know, the two minutes or even two days is going to be, you know, quite some time before we get there. There are still parts of the mortgage industry and I know you're going to find this surprising that are pretty archaic. You know, we've got a lot of counties. And Marvin sent me a document on that that I read through and I saw that on there about the paper. Just wondering, sorry for interrupting but yeah. No worries. I was just going to say we've got 3400 counties. The first challenge would be to digitize all of them. You know, and even I think the top 500 counties are responsible for 93% of the mortgage transaction so maybe let's just start with 500. And I think that's the ground title and I think, you know, the appraisal process I know the GSEs are really laser focused on on the appraisal process. Appraisal automation and firms like Clear Capital that I mentioned before, you know that we're going to partner with are focused on expediting that as well. I think we've got several years but I think we've got, you know, and everybody uses that that analogy about, you know, getting the car loan. And is legal, the legal system locked into this also to because obviously the attorneys involved in a lot of the stuff and closing costs whether it's commercial or residential are they do they somehow under the evidence that they are sticking a wrench into this. I don't know if I'm going to get in trouble for saying this but there's a lot of people with their fingers, you know that drive the cost up under the ages of let's protect the consumer. There's a lot of people that in the middle of all of these transactions, particularly at the back end and you know, not just particularly at the back end but during you know during the process as well. Right. Interesting. And Jeff, if I could take a stab at answering that question. And I'm going to go back to part of the discussion that you guys had. I think that a significant portion of the origination costs is due to compensating the sales people. So my personal theory and I'm probably going to get into a lot of trouble with this is a lot of what's preventing a two minute or even more one minute loan closing process is the social inertia that's built into the current system. If I'm a mortgage broker and I'm getting and I'm going to just make up a number $1,000 per loan. I'm going to invest in my best interest to make sure that I'm a part of that process, and continue to work with you for better or for worse, to get that loan to where I wanted to go, as opposed to something that's completely automated. Let me give you an example about 10 years ago, I was working on an online loan application for a bank that I can't mention the bank, but it was for high net worth individuals to get loans, unsecured loan that you have a credit score of 820. You're going to pay back your loan. Okay, if you're a high net worth individual, I as your banker and incentivize to give you a loan for up to $20,000 to buy a racehorse to buy a car to buy whatever because at a credit score of 820, you're going to pay back that loan period. And so we built that application, and that was up and running and still is up and running. But the problems associated with a mortgage are several magnitudes more complex, because of the social inertia of the loan brokers of the different parties that are part of the process, and honestly, a lot of the risk associated with it. And if there's any type of fraud, losing 20,000 is significantly less impactful than losing 500 or average cost of a house now 350,000 400,000. That's why I think we're still not seeing the two minute loan. Honestly, I don't think we're ever going to see that I would be happy with the 24 hour long. James anything you want to add or shoot me later on. No, no, actually I would tend to agree with you Marvin, you know getting down to a two minute loan I think would be fantastic. The reality is I see the process being shortened down to two and three days over the next you know several years. But I don't know that until you know, much like I think Devin brought up until you get all these title companies and others, you know integrated into a, you know similar platform or connection methodology. I just, I don't see that we get to a point where you've got pretty much instant instantaneous approval. Yeah. And one last thing that I want to add in this, we actually build a proof of concept for a loan origination process, where we could get through and this was simplified through the entire loan process within a two hour window, using integrations using APIs. So, technically, it's feasible. It's not the technology that's the problem. It's the process and the regulation. And for the amounts that we're talking about maybe it's a good thing that it's slow. I mean, I'm really impatient. I mean, I'm a tiktok user anything that's three minutes long, I can't pay attention to that so I need a loan, I need a loan quickly. But I think right now it's still a good thing that it does take at least a week or a couple days. Yeah Marvin you may want to move on if you do that's fine if not, I can add a comment to this if. Yeah, definitely add a comment. Alright, so Jeff I'm an old mortgage technology writer I've been writing about the industry since 97. And we sometimes forget that this is a risk based process. So, Devin, what did you say you guys are like 97% accuracy on those docs now. Yes, that's awesome. That's awesome. So that means only 3% of the time you're going to miss and Marvin's right. The loan is about $350,000 so if you do 100 loans. Three of them are going to be bad now when you make a good loan is a lender you're going to make what two grand well in today's market the profitability is actually very close to zero, but on a bad loan, you're going to pay back the entire amount. On those 3% you've now lost a million dollars, how many loans do you have to make to make that up. You're not going to make it right. So remember this is about. This is about risk averse business people trying to make sure they don't lose money and so it's never going to be a two minute loan. It might be a two day loan is more likely going to be a week. So in today's paper world, I interject. What's the, what's the miss rate on, I've heard horror stories are even around title clearance and a closing when somebody goes back later says you couldn't do that. How often does that happen. Every time. That's what you know that's why what Devin's talking about today is so exciting because in a paper based world we have errors all through the process and as Devin pointed out, the longer it takes to solve it the more expensive it becomes. And if you look at JD powers surveys of borrowers, the thing that makes them the angriest is being getting to the closing table thinking it's all done, and it's not done. But the changes in trade change that a lot because now you've got to read disclose everything and wait another three days, and that punched a hole in that problem, but it gave borrowers another reason to be upset. The point of it all is is we're not lending against cars, we're lending against a very expensive very massive asset, and it's not the lender's money that there's given away right at somebody else's money. It's a public system that's never going to be like a car loan, but with software like what we've heard about today from Devin and with other blockchain applications. It'll get a lot better than this. Right, right. Thanks. That was great commentary. I'm so impatient at technology I press great people every time I'm going to a star like why do you have to do this, why do you have to do this, try for me can't do this. I don't understand this. The younger people and they're like, I mean, like, it's this old guy doing complaining about, you know, you know what a blockchain is folks. I went into my Verizon. I was just in the office and I was just setting the phone up like why can't he pay through crypto. I don't get it. Yeah, but you must be a tech company. Why don't you have a black chain. There's rise of a black chain. What's a black chain. And I just, what's with you people you want. I can give you a credit card. You're like, get him, get him out of here. Jeff, we need to get you out in front of everybody. Yeah, exactly. You're a great advocate for technology. May I, may I add something else because Rick was saying that it's a risk averse industry. I also would say it's a lazy industry in the sense that usually they, unless the, unless fanny or Freddie push or bring up bring out anything that says that they should do this. They tend to just keep on doing things the same way. And then they're not so prone to innovate. So, only if they're forced to move because in during the pandemic during COVID, they were they had to update how they were closing. They could have done that way before because the technology existed way before but they never did it. They didn't do it until they were forced to because people couldn't go to sign. So if they couldn't go to sign they couldn't close business. And only then did they implement that part and now they're working with it but they would never have done it before because no one was forcing them to do it. There's also a bit of that in the mix right. That it's an industry. It's not the regulations. I think it's, it's, they use that sometimes they use that as an excuse. I think they don't, they have an aversion to innovate also. This is going to be maybe a first for me. I'm usually not very sensitive, Miriam. I think we're of like minds of this I'd probably use a different word or a different phrase I'd say that the mortgage industry is filled with fast followers, not necessarily and I can't, I can't take credit it was a former boss of mine who said described as firm as as fast and I think the mortgage industry by and large with the exception of a few are fast followers and because they are risk averse. They really don't want to do anything that Fannie and Freddie or all the GSBs haven't proved as of yet right. It's, it's very risky to do that. I like Maria is the lazy term. Yeah, yeah. I'm just trying to be nice. I think those are some great points and everyone this, this was a great discussion I think those of us and I think it's almost all of us that have been in the industry for a while we understand that that challenges of working within the mortgage industry and that's why we're here hopefully that we can help change the industry and make some money from it so I want to get back to you Devin was there anything else you wanted to cover on the presentation. Okay. Were there any other questions for Devin or from AI or to perspective. Okay, I want to go ahead and because we just have a couple minutes left. We're going to talk about what we're going to try and cover in our next in our July meeting and I'm trying to get to the right slide excuse me in our July meeting we're going to have a, another company come in, and this is going to be I'm trying to share the screen here. So we're going to have, excuse me while it's loading, Sanjay Kumar Nishant he's the chief operating officer from in pain, and in pain provides a blockchain platform using hyper ledger fabric that brings the different parties together on to a blockchain, a single blockchain platform. And let me get to a brief description really quickly here. So in pain is a blockchain enabled structured finance platform that gets lenders issuers investors and other parties, like services trustees and rating agencies onto a shared data platform for these transactions that uses artificial intelligence and blockchain for a seamless flow of data with zero reconciliation, complete transparency and provenance of asset and data. So, you know that that's a big claim. I've spoken with Sunday a little bit. It sounds like they have a real interesting solution so hopefully people on this call can join us for that one as we continue to delve further into AI and blockchain. And with that we're at the top of our hour but does anyone have any other questions or comments before we all sign off. David, we get your slide back. Absolutely. Thank you. Presentation today. Thanks for coming and sharing with the group. Thanks for having me Marvin we appreciate I appreciate the invite and everybody for that extend that forever one of true as well. Yeah, that that thank you Devin this meeting will be recorded Jeff and it will be posted on to the hyper ledger wiki. So all of that information will be there and will post his, his, excuse me, Devin's presentation there as well. Well, it's actually my wedding anniversary today so I snuck in here for half an hour while she's out 200 hours. Okay. It's not that that joking. I better get back. Okay, well happy anniversary. Thank you Jeff. And thanks everyone for attending I appreciate it. Thank you. Thank you Devin it was great presentation.