Episode Transcript
[00:00:00] Speaker A: Hello and welcome back to another episode of Mike Dub. This is specifically for the mortgage industry, really for loan officers, processors, underwriters, everybody and anybody in the industry who is unable to make conferences because maybe your lenders don't want you to be recruited out there because you're a rock star. Could be cost, could be. You haven't raised your hand yet. This show has many guests that are leaders in their space that you would typically see at the conferences. So you can get a taste, you can actually sort of get to know people before you go there. But if you are wondering what happens at the highest levels of these mortgage conferences, I believe we do a good job of bringing it to you. And just so you want to participate someday. If you're listening to us on Apple or Spotify, please subscribe. Goes a long way, but we're always live in case you want to come in, ask some comments. We do a really good job of getting it up on the screen and trying to thank you as an audience and get some answers live because it's about community that we build. And we do it every Thursday at 2pm Eastern and of course out here.
[00:01:07] Speaker B: In sunny san Diego at 11am Pacific. Always looking forward to not only our guests and also for our listeners.
[00:01:15] Speaker A: So the super bowl of mortgage banking is the MBA annual. And this is typically when new companies will spend more, get a booth, roll out their big new announcement, and you'll get to see them. There's also media appearances or LinkedIn and other ways you might see people get into the market along the way. My point is Timothy Lee is somebody that you would see at a conference and there would be a lot of excitement around. I say would because we're not at the super bowl yet. We're not here in October. And he is launching here right in January, a new solution for an industry that really does need some new blood. New in the, in how he's approaching the market. I think you're going to see a lot of history in his entrepreneurial spirit and how he was able to get into Fintech, you know, from his early route, early roots of dominating what microprocessing or technology. So you always get recruited if you actually know how to build things. And then from there he was able to say to an industry, which we'll hear about, hey, there's some things I could help you guys do better. And now it sounds like, Timothy, we are at a point where you're really stepping in the mortgage and saying, I'm, I'm back.
[00:02:37] Speaker C: Right?
[00:02:38] Speaker A: You were with Rock and A couple others here. So you have a long story. Do you want to maybe give us a brief overview and then we'll try.
[00:02:47] Speaker B: And zoom in and if we do that, Tim, can we start back into your history at just before, just after, actually just after you graduated from college. I know we're going back in time, but there's actual chain of events that have caused you to, to have kind of a, a, a drawn out meteoric rise from where you started as in, in engineering to where you are today. So I'd like to, so we can give a brief overview of how does someone go, become an in, from an engineer to all of a sudden creating technology in the financial sector and fintech that just revolutionizes banking and then taking that and becoming not only an investor but also a leader in the fintech space.
[00:03:33] Speaker C: Yeah, that's great. Mike and Michael, thank you for having me on. This is valuable time and I'm super stoked to be on this podcast. I'm here in Irvine, California, Orange County. We're about 35, 40, 40 miles away from the big fire that we are still having up there. So I want to say a few words about that and we're praying for the families out there. We certainly have a lot of families and friends that got impacted. The entire town of Altadena is basically burnt to the crisp. So for our audience, if there's a way for you to help out, I'm sure there's a lot of really well endowed organizations out there that can contribute to the fire victims. So hearts go out to those guys. But thank you Michael. Mike again. So again I'm Tim, Tim Lee. I'm founder, CEO of the Len API. My partner, my co founder is actually here in Orange county as well. So there's a lot of really cool people here in south of la, all the way down San Diego County. So you know, I, I, I could start after graduation but I just want to go back a little bit of a time. I grew up in San Francisco in the 90s, in the mid mid 90s, mid to late 90s while I was in high school actually I built a bunch of startup, this is right after personal computing became a thing that you can actually buy a computer. Right before Internet first started right there was, there was basically bulletin boards and, and little clubs you can go to here and there but there was no Internet. And in 1997, 1998 is when Internet became a thing. Started with Prodigy, AOL and later on you a decent sort of Emotum, ISD in lines, whatever and browse the Internet, you know, with Netscape and some of the Internet browsers out there. So I built a few startups with folks all over, all over the country.
We ended up taking that company IPO in 1998. There's some Wikipedia articles about that. But that's where I kind of sort of fell in love with technology. Certainly high school computers, if you will, are just sort of brand new. And I remember in high school we had our first AP Computer science class. And of course I raised my hand. I was like, I want to know what's going on. We end up programming VCRs and things like that, interfacing with some of the. So that's how I kind of got into, you know, software engineering, if you can call it that, back in the days and 1999, 2000.com crashed. I felt like I was just going to do like more Internet startups with people, but ended up going to school in the Bay Area. I went to San Jose State for undergrad in school. I built a few startups as well, or helped out with a few startups, one of which is in the travel space. Back in the days you had to call travel agents to book your tickets. This is before Travelocity Expedia. So these travel agents only had access to certain reservation systems. So we built a cross translation software to cross translate any languages you learn from these reservation systems to all of the other reservation systems.
[00:06:44] Speaker B: Was that like a competition to the Sabre system?
[00:06:47] Speaker C: We sold it to Sabre holdings for like $60 million.
[00:06:50] Speaker B: Oh yeah. So you can't beat them, have them buy you out.
[00:06:53] Speaker C: Yeah, exactly right. They. And they turn into something. But anyways, I went to grad school at USC here in Southern California. So I feel like, you know, Southern California is a home for more engineering. I did mathematical optimization in my graduate work and after that that's. I got recruited to work for Intel Corporation building supply chain optimization software to support their i86 architecture, which is their main CPU line and also communications line. The thing is so complicated out there in those plants that we had to build our own mathematical programming language to build other programs on top of it to go run the plant. So that was really fascinating.
Got recruited straight out of college, I think. We did one interview and they said, yeah, you hired. And I thought I was going to move back to Santa Clara, you know, the Bay Area, go home. And they, they sent me the offer letter and it says, oh, you got to show up in Phoenix, Arizona. So I'm like, Phoenix, I've never been outside of the State then, right? So I pack everything in my car, moved to Phoenix, showed up on the first day, met my, I guess, manager, right? Darryl Riley. I still remember his name out there in Phoenix. Hey, Daryl. He says, hey, I'm going on a vacation, right? Here's 3,000 lines of code you got to run. Like, really go on to sabbatical or whatever he was doing. So, yeah, you just drop in and jump into the pool and tell everybody how cold it is.
But I was cool with it because I did a lot of work in the technology space. You basically sat there, you know, sometimes I'll be there till like, four or five in the morning trying to compile this code because it was a Intel homegrown programming language that doesn't have a whole lot of error, you know, capturing. So you basically run into these really bizarre errors, but you have to figure out line by line what's going on. So I really cut my teeth in software engineering there, Intel. And while I was there, I got recruited to JP Morgan Chase. It was super bizarre. They keep on calling my house.
And I told my girlfriend, then wife now, that, hey, you know, we bank with Wells Fargo, I don't want to change my banks. And then she said, oh, I think they're looking for a job. They asked for a job. I'm like. And I got even more mad. I'm like, don't. You know I work in high tech? You know, back in the days, high tech was a thing, right? Now it's like, whatever, yeah. And she said, you know, the guy's been calling for forever. Just give him a call. So I called him. He says, hey, we have the same problem that. That here at Chase or JP Morgan Chase as you do at Intel. I'm like, come on, dude, what are you smoking there, right? He says, oh, you work on supply chain there at intel, right? He's like, yeah, we had the same problem. We've got seven to 8,000 ATM machines, 3,000 branches. We just merged with Bank One, and we have eight. Seven vaults that we have to manage and 300 different armored trucks running around. We don't know how to manage all these things. And to me, I'm moving wafers here, CPUs, you're moving coins and currencies over there. Just a math problem. You got to forecast, you got to optimize, and you got to figure out where everything should be, right? So. And I said, I'm not. I'm not interested, but I can tell you what to do. He's like, no, no, no, wait, you even need to tell us what to do. I don't know how to do it until he told me how much you were going to pay me flight to Ohio.
So anyways, I'm like D, I, I, you know, CPUs are great but I got to make some money, right? Try to build a family out there. So moved to Ohio. I spent a good amount of time there building just about everything that the consumer bank needed. That was right after Jamie Dimon took over Bank One and merged with Chase to become JP Morgan Chase. And at the time Chase stocked basically in the middle of the country.
So we, you know, the risk management tools, some of the applications that the banker uses, right. To manage customers, manage their in house cash flow, whatever it might be, right. Or cash supply. All that stuff needs, needs to, needs to be worked on. It was just really old technology. So I worked on a lot of stuff. And before the show, Michael, you mentioned one of the patents that I have with JP Morgan was we developed a whole system that managed every bit of ounce of currency in their entire system.
And we thought let's better patent this and maybe we can actually outsource were wide label the software for other banks to use to manage their currency. So that's what we did. That's what that is. We can talk about that later.
But I stayed there just long enough to see 2008 happening right in front of my eyes. Right? So we're in the consumer bank, we're in the small business banking. But things started to go south.
I think we had a bunch of layoffs. And I said, you know what, I've done all of the mathematical work you wanted me to do. This might be a good opportunity for me to say goodbye and go back to California, right this home. And he said no, no, you're not going anywhere. We have, we need you to talk to the risk management people, right? Because I was on retail banking operations, whatever it might be, building software. And I met with Walt Ramsey, who was the chief risk officer at JP Morgan Chase Consumer Bank. I said Walt, what's going on? What do you need me to do? He says, well we're basically the latter half of seven. Everybody see what's going on. It's like we need talent to help it with risk management.
I'm like, what? Risk management? He's like, well we lose a lot of money in this bank. So he sat me down, explained to me whatever was going on and I said, you know what, I'll do another job at Chase. Fine, I'll stay for a couple of years. So, so I was there in 08, 09, 10 and 11 and it was really bad. You know there was just stuff happening left and right on a, on a real time basis. We, I stayed up there there long enough to merge with Washington Mutual if you remember Washington Mutual. My God.
And Bear Stearns, BNY Mellons retail branches. All of those things we sort of ended up swallowing whether we liked it or whether Jamie Dimon liked it or not or not. And one of the biggest project was to merge the, the 3000 or 1500 or 1200 branches that watching neutral had. So JP Morgan Chase can go all the way to the west coast. Right. Jamie diamond said nine month would pull up the, pull down the curtain. Everything's going to be Chase.
And we did it in nine months. Nobody slept. Yeah.
[00:13:15] Speaker B: So your role then was in risk management was not about the mortgage side at that point in time. It was about the risk management of just getting accounts together and the transference of, of actual dollars, checking, savings, CDs.
[00:13:30] Speaker C: It was technology. They said Washington Mutual has their own tech, JP Morgan has, has their own tech to a certain extent. Bearish terms had their own technology.
Take pick what you need to pick and merge it all together.
They're all shitty. I mean I'm sorry, excuse me. They're all crappy technology. Including J.P. morgan. Chase's stuff. We're still trying to figure out what, what is it that we were trying to do. And if you, if you remember 0708 or even 06, that's when iPhone3 or 4 sort of came out. It had a decent enough of a camera so we were, we were actually focused on building Chase.com. chase.com didn't exist so we're trying to bring, we're trying to push everybody to online to get people to book an account, even just give us their information so we can shove them to the next steps in account originations. And then all of a sudden that stop and say well we have watching Mutual, we have bear strengths, we have all these retail technology. We got to bring it together. And the technology at the banks are essentially all risk management technology.
Identity verification, credit risk modeling. Right. Overdrive limits.
You know there's so much fraud it's unbelievable. Back in the days because there's no multi factor authentication. That thing hasn't even been invented yet. Right. So we have a lot of people just give out their passwords willy nilly and people come in and just swipe out their entire account. The Chase Consumer bank for what it's worth. You know we had A budget on losses. It was like $80 million every quarter. Like if we, if we do better than the $80 million in losses on a quarterly basis, we, we're actually doing a pretty good job. Isn't that crazy?
[00:15:09] Speaker A: That is. Can I ask you about. With all this change and now you're mentioning other companies too and the financial crisis, did you have different bosses and, and how did you have to deal with CIOs of the other companies coming in and egos of their vendor relationships? Like are you, were you just that talented that, that, that all went out the window at the time?
[00:15:31] Speaker C: No, no, not at all. I was, I was a, a small fry. But you know, I, I was dumb enough to raise my hand on every single project. Hey, yeah, I'll do it, I'll do it. I'll. Let me, let me see what I can do. I'm just young guy trying to get myself into anything. Everything in.
Basically it was like almost like an office space kind of a situation where we went in there and said what do you do?
Here's your. A two week severance or what do you do? Okay, that's interesting. We're going to double your salary, but you got to stay here for 12 months.
So there was no ego. They're done. Washington Mutual bank, the savings, that bank is done. It's in receivership and nobody wanted it. I think the, the treasury chief, right, called everybody in the bank of America is the JP Morgan Chases and the Citibanks of the world on a, on a weekend and said you don't take over some of these banks, right, they're going to be all to pay. So I think Jamie Dimon was forced to take on Washing Mutual. Nobody wanted it. And I think Jamie says hey, hey, we don't have any money to buy this thing. We don't want it anyway. So I think treasury give a chase like billions of dollars of buy Washing Mutual. And Jamie diamond said if we find more stuff, especially the mortgage paper, and we know we will, you're going to have to pay us more money. And which we did, right? Every single piece of paper, it was just upside down to no ends. And the government said, oh yeah, we, that was just a verbal agreement. You can't. So we sued the government and they eventually paid us billions of dollars to offset the losses on that paper.
So when I watched these movies, I'm like, yeah, this is just cartoon version of what the hell happened? Isn't that crazy? But yeah, but what I liked about Chase talking about Bosses is that at least at the time in the mid-2000s, you know, there are people, there are career politicians that work in, work in the bank at the end of the day needs to get done, right? So who can code, who can do analytics, who can, you know, do these things, right? It was just basically a bunch of kids, a bunch of mid 20 year old running around, working to 2:00, 3:00 in the morning, you know, pressing buttons essentially to make the machine run, you know, and there's just so much shit. One of the things we did was trying to make early warning services more real time, which is one of the basis for Zelle. My God, it was cranking mainframe machines. You know, you want to. Do you want to crank mainframe twice a day, Tim? No, that's not possible. It only works once a day. So we had a hack and bridge and write a lot of different code to make it work so we can actually pretend to be somewhat of a digital bank in real time on a real time basis. After we absorb Watch Mutual, we went right back into building Chase.com.
[00:18:17] Speaker A: Is all this cranking and working helping with your entrepreneurial itch that you've had since high school or like, were you looking at ideas at the time as well?
[00:18:29] Speaker C: Yeah. So the reason why I kind of mentioned the reason why I said I'm gonna raise my hands on just everything I can get my hands on because of that curiosity, right? It wasn't premeditated. Oh, I'm gonna. It was just like, I'm just curious, right? So why early warning services? Why do we need this? What is it for? Of course, for fraud detection. Right. And we were working with other banks to figure out, hey, look, you know, this guy opened an account here and he just went over to you this afternoon. Let's figure out a way to kind of know that before he even walks in.
Right? So I was just there to solve these interesting problems. To me in the banking environment, I've already forgot how to do CPUs and it was just curiosity. And so what happened was that that same guy, the chief, left the bank in 2010.
By the way, I'm reading this book. I'm going to give a shout out to my boy here.
[00:19:22] Speaker A: Yeah, we'll put it in the notes. Is that part of the fintech cowboys or is that this just.
[00:19:28] Speaker C: This is a journalist in our space and lots of breaking news come out of Jason's. I think this. He's got a publication, he wrote his book, I bought it. I'm like a 25% way through this, I love reading it because some of that stuff, I kind of lived through that myself. But what I liked about working at, working at chase in the mid-2000s is that they would like to do anything. Oh, Tim, you want to do this? No problem. You want to file a patent on this? Perfect. Here's the four patent lawyers on deck. So I just called him, hey, we need to do a patent on this piece of software because we think we can do X, Y and Z. And the guy says, no problem, let's do it.
You know, write me a 40 page essay, give me some charts. And he will do whatever, whatever his part. And he filed it. And actually just recently I got a big packet of information from Chase. I'm like, oh, did it owe me money? It was, it was them telling me that the patent was going to be renewed for another 10 years or something like that.
[00:20:25] Speaker B: Wow.
[00:20:25] Speaker C: If they care about those long enough. So it's not going to expire until 2030 or something like that because they're going to do something with it.
But it was just a really fun place to work. And when Jamie Dimon says, oh, we're going to spend $80 billion in branch or whatever technology, I believe that because he does.
[00:20:42] Speaker B: It's interesting that you talk about that because one of the things I want to, what I'd like to talk about if we can briefly is blockchain technology.
Because what you've basically done is you're, you're initially, when you're at intel, you're talking about moving chips, right?
[00:21:00] Speaker C: That's right.
[00:21:00] Speaker B: Whether it's in some kind of MRP process or whatever, it's going from one place to another. And then when you go into the banking sector, you're not, you're now talking about moving actual dollars and cents out the retail sector of checking and savings. Right now in the mortgage sector, it's, you're moving dollars and cents, except that you're moving them on a mortgage payment basis. Except not only are you moving them from the servicer, but you're also moving that information to, you know, people are collecting interest income, whether it's a reit, a mutual fund, a bond fund. There's, there's information, there's dollars.
And again, it's one big math problem.
[00:21:40] Speaker C: That's right. 100 and that non real time settlement of any kind. That's where problem arises. Right. Buffers and delays. Hey, did that payment make it on time? If there's any insufficient funds or the payment didn't make it through, then everything sort of unbuckles Right on the surface, inside, specifically, Michael. So blockchain is interesting piece of technology that's just sort of. We have to talk about distributed ledger a little bit. That's one of the tenants of blockchain is that servicing company may have a, will have a ledger, so does a bank, so does everybody else involved in that mortgage process, in that mortgage loan. Everybody has the same version of truth.
So when everybody has the same version of truth, it's really easy to figure out, okay, did that transaction make it? And if it did, does everybody believe in it, believe that transaction was made?
[00:22:35] Speaker B: So for our listeners that are out there, Tim, I want to, I want to bring some context. So you have, you have a mortgage. You know, if you're a borrower, you make your mortgage payment to whatever, whoever it's supposed to be, and it goes to that servicer, and that servicer actually has a responsibility to deliver that money to the investor. So you could be paying Mr. Cooper, Flagstar caliber, you know, whoever.
[00:22:59] Speaker A: Chase.
[00:23:00] Speaker B: Yeah, Chase, Wells Fargo, B of A, whoever. You make the payment to the servicer, but then the servicer has a responsibility to deliver that money back to the investor. And it could be a mutual fund, an etf, retirement fund, whatever. And when you're that investor, you want your interest, you want that money, that mortgage payment to come into your account, like immediately, as soon as possible. But there's a slippage in between, and that slippage in between of interest, whether it's, it could be three days, five days, 15 days, bounce checks, all these things that are happening. And that's worth literally millions of dollars to the servicer, millions of dollars to the investor, millions of dollars to, to a lot of, a lot of companies that are all in between while you as the borrower are making a mortgage payment and the investor is actually, they want their money quick as possible, but the servicer has to hold on to it until that check clears, which is why Fed now is a big deal, which is why, which is why information distribution is so critical. And so as we're talking to you, Tim, I want the listeners to be able to understand that it's not just about making the mortgage payment and delivering information. It's also about every single dollar is working at every single minute. And as we move into the technology sector of delivering this type of information, every dollar accounts at every single second because it's always working. So I want to bring it into context for our listeners that don't necessarily understand that, which is why it's such a big deal.
[00:24:28] Speaker C: Yeah, you're 100% right. That is the same with credit card companies. Same with these gigantic personal lending companies like SoFi. Everybody wants their, everybody wants instant settlement for all the reasons you talked about. And mortgage is one of the biggest piece of GDP if you will. Right. In our world. You're absolutely right. So with blockchain or something like blockchain, distributed ledger, this type of technology, if there's enough adoption from end to end, yeah, we will have close, very close to real time settlement that everybody believes in. And when you have that, then you can start accruing, then it's on your books instantaneously and is vetted. And with all the other fixing that comes with blockchain and distributed ledger, Chase tries so many years, decades maybe trying to work on that. But you know, the, it's convincing everybody else to play a part is the difficult piece. And you bet there's a lot of startups working in this space. You know, P2P payments, right. B2C styled retail, you know, lenders and things like that. They're the first adopters. It's slightly easier to implement when you have a monstrosity like the one you described, Michael, or there's literally billions of dollars being moved around every single second. And then we have, we have to go through the Fed, not Fed now, but the Fed's existing clearing houses, like my God, you know, I, I want to say, you know, figure is trying to do some of that, you know, Mike Hackney up in San Francisco, figure trying to do some of that, to eliminate some of that back and forth using blockchain to settle transactions instantaneously to take. He was estimating, I was in his office, it was estimating that he can take 20 to 200 basis points off of some of these like slippages, if you will. Right. Because he can settle things instantaneously but he still have a ways to go to kind of go all the way to wall, you know, the wall streets of the world, the investors of the world. But he's made strikes. If you go to figure.com, i feel like that's the future and certainly there's a lot of people dumping money into that.
Of course he's got the figure token, right? You got to use the token to settle in real time on his blockchain. But I think he's got a lot of mort related entities there. Especially on the, on the, on the further back end. The investor side of the house adopting this technology is living in subtle between themselves in real time.
[00:26:58] Speaker A: And adoption in mortgage is extremely difficult. And Oftentimes why a lot of the innovation is not in mortgage yet it is the how hard it is to adopt. And the perfect example pre blockchain is the simplicity of an E note and the ability to just sign electronically. And it still is not. And I think this is going to be the year that it really gets adopted and I'm starting to hear it. But it, it needs like you said, the if ameri Home or PennyMac which are investors that when your mortgage company closes it they typically try and sell some of it too if they're not accepting it. These giant mortgage companies don't want to move forward. They, they need all of their investors just in case they're, you know, just in case that day it pays more and they won't move forward. And I think that's the problem, I'm sure. And what I was going to say is I think what bigger did really well they went into like the second lien, second mortgage market. So they had a product for people to give that there's not much competition or people, there's no Fanny or Freddie out there. And so you sort of learn on training wheels and now people so hand, you know, locked in, they learn that way. So I guess segueing into so we can go maybe fast forward and then we'll go back like a Hollywood movie. Because I do want to get a chance to not only talk about lend API but your mindset coming into the the industry. There have been a lot of smart engineer types that have come in and have become frustrated with the fact that a lot of this industry is not enterprise run. They let the, the frontline people make technology decisions is one of the ways you solved it. I've seen your interface, it's amazing. Or do you have a vision in mind of doing this? I guess. Is it similar to fluid or is it going to be a different approach?
[00:28:55] Speaker C: Yeah. So you know, we made this platform to be more like a do yourself, build it yourself kind of a platform. Right. It's almost like a WordPress style system where you can build anything you want. You can collect lunch money if you wanted to. So part of the things we did was we actually just took an afternoon and built a 10.03 application on our platform. So we piece all, everything together. All the questions were like 400 different questions. And we said, you know what? Why can't we let anybody to participate in just the front end of this whole mortgage? You know, 45 days of journey, right. Can we give this to realtors, mortgage bankers or whoever does sit in that the front end of it to make it super simple for them to send a link or have their customers just build out a couple of pieces of information and just to see how it goes on the front, on the back end.
It may not be me to solve every possible issue that's happening down the chain, but if we can, I hate to use this word, but I will. But if we can popularize or democratize this application process, right, as opposed to sitting through like 35 pages of the 10 or 3. But can we somehow make it easier for people to like get, get to that point and make it easier for some kind of a machinery, automation to digest all this information and then do, and then give this capability to individuals if, if that's a thing in, in the future or give it to all of the mortgage professionals out there.
This, this capability, right. They can brand it themselves and all that stuff to start shoving applications through whatever wholesale or whatever retail bank you have, you have an engagement with. That's our vision to kind of just make it easier and you know, dare I say, free to use. Right. So instead of subscribing some kind of a platform where you have to pay thousands of dollars a month to use or you have to be holden to some kind of a wholesale mortgage company that only gives you access to one, you know, lifeline, I want to make, make it sort of a one application that could, you know, that, that you can, you can take for lack of a better term like a mismal format of file and disseminate everywhere just to make it available almost like a marketplace. But we don't know where we're going to take it to yet. But we want to make a simpler free for mortgage professionals to use and, and bring, bring automation. I feel like that's where we can put some effort in solving. But some of the, you know, I guess secondary market stuff, right, that's, that may not be meet yet, but we want to, we want to make the, the first part of the experience as pleasant as possible, both for the applicant, also for some of the frontline mortgage. Mortgage professionals out there. And Mike, you've done a lot of work in this space as well.
[00:31:46] Speaker A: Yes, some say I invented the mobile app, but it, it didn't have the ability for loan officers to customize it. Right. It had to be done at the enterprise level and it certainly didn't have the sophistication that yours will have on the ability to leverage APIs. I think that's probably going to be the most exciting part of what you're offering from what I can tell is there are a lot of external fintechs out there and it's easier to connect to those APIs where you can now bring them into mortgage.
[00:32:16] Speaker C: Yes, you're, you're absolutely right. So one of the, one of the, you know, seeing our name API, so one of the tenants we have is that if you want to do a full blown manual 1003 right. Have at it, go to yourself. It's free to use but if you want to do plaid trove, argyle, whatever it might be, do automated voib to take 80% of the questions off the table. It's all there for you. Put your credentials in there. Sometimes we give you our credentials to use. It's all there. So you can build a manual version, you can build an automated version, semi automated version, you can build one that has a back and forth with a borrower to upload certain documents. All that stuff is curated. It's not one size fits all. It's, it's all size fits all, everything. So you can do all of this and it's multi tenant as well. So if you have a team of five, six guys right down for dollars in the garage, you can actually have different applications for different tenants and each tenant could be one of your team members or could be a group of people, right. And then they, they can run different underwriting, preliminary underwriting, different pricing, different plugins in the world, right. If somebody says, you know what I am my constituent, don't want to log into anything, so I'm going to use this application, right? So I'm working with a bunch of waltitude 30 year olds that very well versed in technology. They're afraid, they're not afraid to log into certain things. They want just a quick resolution. I'm going to send this version. It has all the automation built into it so you can create unlimited numbers of different type of application to get to the endpoint. Depending on the customer's needs or become and depending on how much operations folks you have on staff, it could be completely automated.
[00:33:55] Speaker A: Just where I envision you being able to use APIs which is just, we'll just call it a connection or the ability to. This isn't the technical answer, but almost go in and you could technically use somebody else's software if you wanted in this new world. And I, I. So what excites me about somebody like you that could have the resources to go do it is if you could extract information from software that people already use today.
Like oh, this integrates with ROKU So now my information is already going into the, into the point of sale from Roku or from Netflix and I can, and then I can take a little bit from another place where you know, I put in my street address. Right. And it's almost taking it that way which is a step further, further than today. You have just the ADPs and the payrolls and the, the obvious ones that are ingrained in mortgage. The first person to go outside and really just start grabbing from consumer, the most popular consumer places that our data already exists.
I've seen what you have if, if there is a ticket to get there so far, ding ding. You've entered the market and I think it's going to get very exciting.
[00:35:04] Speaker C: You're absolutely right. We, we work with a lot of embedded players for some of the folks that are selling these high end, you know, energy products. Right. So or work with a company in la. So it's all embedded and we actually get all the information out of the E commerce system. It's all pre filled, these have consent. Right. And that packet of information is basically 80% of what you need for a mortgage application because they, they need to get pull credit, they need to do income verification. Right. With that you, you kind of have a portion of that employee employment verification as well. What's really missing is that whether you. Well we know whether they have a mortgage or not for refi or whatever it might be but if they are buying solar panels they probably have a house, right. But if you're buying like appliances and things like that, they probably look for home or renting or whatever it might be. So yeah, you're right. There's a lot of gateways path to.
[00:35:58] Speaker B: All this stuff all retail. Whether I'm a mortgage broker or a retail loan officer.
[00:36:03] Speaker C: Right.
[00:36:03] Speaker B: And if I'm, whether I'm using Encompass Point, lending Pad arrive, whatever the loan origination system is, I have to go through some kind of pricing module whether it's optimal blue or whether it's H2O or whatever.
[00:36:17] Speaker C: Right? Yeah.
[00:36:18] Speaker A: So possible sponsors, we won't mention any names till they, they reach out to us.
[00:36:23] Speaker C: Yes.
[00:36:23] Speaker B: I'm not mentioning specific, any one specific company but what I'm looking at is I'm sitting here going because once I've taken the application now I'm sitting here know I've got the credit, I got the, the basically a 1003 and I've got the credit score, I've got the address, I've got the loan amount and what you're saying. Because if I have to run. If I have to go to a pricing engine, I won't say the company. If I have to go to pricing engine then you know that's going to, you know, I input that takes me like let's say five minutes. Okay. Or even 10 minutes because now oh, I got to do this other thing. I'm changing up from a 30 year fix to a 5 year ARM FHA MI factors. Da Da da da da. So what you're saying is that you can now regardless of the system and the loan origination software you're integrating, you're basically taking for every mortgage loan that are there that either the mortgage broker or the retail loan officer has and you're going to save on that team anywhere between 10 to 45 minutes of mortgage loan base time, creating 45 minutes of, of, of other, other opportunity cost to do something else.
[00:37:34] Speaker C: Yeah. And is and do yourself. So you mentioned a couple of those P PPEs. We have our pricing engine as well. Right. If you want to just, if you're just selling to three wholesale you can do that. You can actually put all the pricings in, in, in our pricing engine you don't have to dial out another ppe. So everything's self contained and then it will just spit out a mismal format file and you can jam into. Where I want to jam into Automation is what ultimately our goal is as as much as possible. Even if somebody wants to upload pay stubs, 10, you know, 1099 W2s or you know, or, or you know, or their pay, pay stub, whatever it might be, we can scan through all of that. Right. Get to the 80, 90% of the way there. Right. Without having to go back and forth with the people. Even if there is back and forth, the system does it. The system recognizes the deficiencies and it will send out emails or alerts to the applicant and say please upload, please log in. Right. We want no intervention. At the end of the day what we want is a pretty well done Nismo whatever format that these bank needs and it's do yourself the applicant essentially interacting with the system with a lot of automation. Lots of intelligence if you will. Right. There might be a banker of sorts sitting there watching this whole thing happen. But it's all automated. It has the intelligence to direct traffic.
[00:39:00] Speaker B: Is it also reading the the data that's on for example, someone has uploaded bank statements, W2s, 1099, whatever, tick K1 1040s. Is it also reading those documents as the PDF or we have OCR technology.
[00:39:15] Speaker C: That can read off all that stuff and makes make decisions based on whatever you upload. And if you don't upload certain things, we ask you to upload certain things. For the. My co founder and I were all engineers background for us, you know, if we squint our eyes a little bit, there's a, there is a, there is a pretty massive fishbone but there is a, there is a happy path. 60, 90, 60, 90% of could be happy path if that's even a word. And there's enough technology out there now. We have decisioning engine that drives workflow interaction with the customers. We have decision engine that drives the credit decision as well. That's income ratio, loans of value. All that stuff is calculated on the fly. You know, the debt is collected from different sources, the income is collected from different sources. It may not be 99% but it's good enough so you can, you can siphon through, you know, 80% of the garbage and only focus on the 20%.
I worked at places where, you know, the loan depots and quick loans in the world, my God, they have like 500 people here, you know, trying to go through all this information and if you look at it it's like wow, that's. This stuff could just be done through a code. It's not a hundred percent but we can get rid of 80% of noise.
[00:40:27] Speaker A: That's what I was curious about and I'd like to maybe re. So that's lend API. You guys will put the website in the notes. It is exciting and actually this whole interview has been very thrilling for me and love to have you back on and just break down some of the other mortgage tech and what, what you think of it like a company like Mesmesa or Mesa out there bringing the first debit card. Because I know that that was your world too where you can help people sign up for credit cards. And so that was my question. So here's where I see the landscape and then just kind of your comment with your experience from Loan Depot and Rocket or Quicken at the time. In an ideal world, the perfect technology for the perfect customer experience is driven at these enterprise marketing data type companies that think this way and happen to do mortgages and are really awesome at it. So people keep coming back happy path.
Reality is as you start to learn more, refinances are a little bit dried up right now compared to normal. But the purchase market, 6.5 million loans a lot 80, 90% of that should be purchased Purchase is driven still on the streets with loan officers and Real estate agents, which especially with their age don't adopt the mortgage technology tools because they get going and in the sales and they forget to use it the way they're supposed to. So with all that said, I think if this model keeps up the, the IMBs that rely on that retail in the streets are going to crush it and crush it and almost overnight wake up one day and wonder what happened while somebody over in the data marketing world has figured out how to flip the switch and really vacuum up all of that. I don't know when that happens, but yeah, how would you.
I'm interested now. And then when we have you back on a year later as you're talking to more of these, these retail in the streets. But how would you give advice to them from your rocket days like that in Loan Depot that really look at data and then I do see maybe they could win if they flank them with starting to offer loyalty programs or credit cards or other ways, ancillary ways to sign up, which you could probably also use your software or at least your brain to, to onboard that. Where do you see all of that like interest? That's the royal rumble who ends up the last one in the ring.
[00:43:02] Speaker C: You know, mortgages, refi or purchase, right? That's a big decision for a lot of people, right? That might be the biggest decision you make in your life or in your spouse. So there has got to be the new phrase is human, human in the loop and you're going to have all the A in the world, right? It is, it needs to have somebody psychologically speaking, right. And that person may be a real person, maybe a carbon based or maybe it's Joe Bayes person to kind of help you guide through this whole experience. It is scary because it'll ask you some detailed questions about your life and you know all that stuff, right. It feels like you're you, you have to give up your entire life's worth of information to get this mortgage. So it's a scary process.
So for us to present a cold multi page 1003, that's one thing, right? For us, that for us to overcome that psychological barrier of somebody that just got a job, mid-20s, early-30s, wants to build American dream. That's the thing anymore.
We, we have to build other things to hug around these, these applicants.
But it may not need a person per se. You know, like I, I overheard my wife, you know, calling, texting Amazon, she says oh I'm texting their AI. She knows, but it's really good, right? So that technology and I've experienced that. That all needs to come here, right? If Amazon has it, we should have it, you know, at the end of the day, banking, fintech, whatever it might be, right? We claim we have the most amount of technology, but I don't see it everywhere yet. Right? So that's why I built an API to say, well, we want to give all the, all these technology to everybody, the applicants, the mortgage brokers, whoever that's touching all this stuff. Leverage technology to make it easier, to make it, to spend more time hugging around your customers, to make it more comfortable going through this whole process, right? So it could be AI driven, it could be automation, it could be that, hey, all that technology is going to take care of the basics. Let me walk you through how this thing really transpires as opposed to a very transactional activity 10 years ago, right? If you more it's like, hey, can you upload this? Can you send me as opposed to having a conversation with me about, hey, what happens after you have a home? You know, what are some of the things that you ought to look at, right, to, to be a homeowner, right. To make it a little bit more humane or human.
[00:45:27] Speaker A: Who do you think should ideally build that out? The CEO, the chief Marketing officer, the CIO with three, three people and a couple people offshore. Let each compliance and, and the branch manage sales managers each take a turn as a SME.
[00:45:44] Speaker C: If.
[00:45:45] Speaker A: Has anybody really figured this part out yet?
[00:45:47] Speaker C: Yeah, no chance, no chance somebody, CIO from whatever big bank or no, no offense to anybody else rocket launcher is going to come up with, they just hire AI guy, right? All, all due respect to those guys, but it's going to be startups, it's going to be another figure. It's going to be another, you know, we have better, right? We have some of the other folks out there trying to really think about this whole process not in a mechanical way, but in a human way, right? Why, why is this done this way? Of course we, we have to conform. At the end of the day, everybody understands what a Metro 2 is. Everybody understand what a mismo format is. Everybody understand these things that needs to be delivered. But there's so many meanings to that end, right? It's going to be you and I, Mike, Michael, Tim, whatever, right? To, to say, you know what, this needs to be done a little bit differently. And you know, I still believe in market adoption, right? If you present a good solution, a wealth curated experience, people are going to come to it and hopefully these other bigger players in this, in this Industry will start adopting that. It's going to require generational change.
My kids or, you know, maybe just, you know, post Millennials, right, they experience technology completely different than we do. Their attention span is 3 to 7 seconds. Our attention span might be 10 to 12 seconds. You know what I'm saying? So how do we build technology to bring them to this type of product?
The products are product. The Jenny's and Freddy's and all the Andes of the world. It's not going to move in the next 50 years. But how do we. There's so many again, there's so many means. To that end, it'll be somebody on this podcast or somebody in our audience to, to drive that change.
Just like this is a little selfish of me.
[00:47:29] Speaker A: I have a question that's been. I hope I don't ruin the podcast with a question, but maybe this off subject, but I saw a post where somebody was saying the threats of AI and honestly, Andrew De Good posted this. I hope I got the right person credit. And somebody was talking about how the a, you know, the atom bomb wasn't a threat because it couldn't make its own other bombs like humans had to use it. And then it was done this AI and it told the story about how there was a capta, which are those pictures to kind of prevent bots. It, it was smart. The chat GPT was smart enough to go out to task Rabbit and then hire somebody to do it. The person said, is this a bot? And it was actually smart enough to deceive and lie and say, no, I'm impaired. Fast forward to my question. Because there's no skill game. They don't go to jail. Right. Like they can just do it. If this world is all going to point of sale, which it already is, and then you're bringing a human version of that, does that make it easier for somebody, you know, a year from now when it gets really good to just have bots fill these out and take out mortgages?
[00:48:39] Speaker C: Yeah. When I, when I say human, it doesn't have to be a carbon based human, right.
[00:48:42] Speaker B: It.
[00:48:42] Speaker C: It just have to be humane enough to, to as a aid, you know, interesting enough, I read the other article yesterday not to derail this is that sometimes they find some of these LLMs think about problems in Chinese. You know, of course it was based on all of the training models that they've adopted. Right. Most of the, you know, a third of the world or a quarter of the world speaks in that language. So it makes sense that these models think, think that, think about themselves in that language. But it's, it just, it's, it's a guide, you know, it's a, it's a guide. Like for example, when I, when I worked at UIC teaching there in the engineering school, we had a hell of a time hiring instructors, part time lecturers to teach classes. So one of the research project we did was, was build a digital professor. SC has tons of data, right? So we basically build a, build a, a digital twin if you will, of all of us, right. So the students, you know, could do office hours there. It does a hell of a job. I mean does a better job than I, I could ever right do. Because I'm verbose, right? The, the, the digital professor was, would give the right answer right off the bat instantaneously. And it's free to use for students, right? So why can't that be everywhere? You know, especially with mortgages, right? So that compedium information is key to, to everything. So when you go Everybody else on LinkedIn, go to Settings, the choice for your data to be used to feed Microsoft's LinkedIn's LM is on by default. Go look at it right now. So you can turn off if you want to, but it's using all of this information right now to come up with a model that can explain, that can think on its own.
For what it's worth, I wrote this in my 2025 FinTech prediction article is that we, we will be free to do whatever we need to do and my digital twin will be the person that's making a living for me. Why shouldn't it be that way, right? So this thing could be 24 7, whatever, you know, thoughts you have, right? That could just work 24 7.
And I will free up myself to think about whatever I, I need to think about. You know that, that's, that I would love that feature. Make 10 of these. And it's happening right now, right? So it's happening right now because you can create your digital twin and the digital twin can create content, blog, whatever it might be, right. And make money for you. And you will think about, I don't know, maybe orchestrate something else, I don't know, or build something else. Interesting.
[00:51:18] Speaker A: That's fascinating.
[00:51:20] Speaker C: Yeah.
[00:51:20] Speaker B: You know, in the Philip K. Dick wrote a short story, iRobot part of it. And, and in that one of the characters is asking the question, they're asking questions to this AI model of a thing and he's going, oh, this isn't the right, that's not the right question. That's not the right question. That's not the right question. And at the end of the story he goes, that's the right question.
[00:51:42] Speaker C: Right?
[00:51:43] Speaker B: And it's not a fear. But the concern that I have as a consumer, just for me as a consumer is, I don't know, to ask the right questions and. But yet somewhere in the programming of either my, my, my cell phone or my computer, because of the way I think it, it's putting out information or to ask the right question. But I personally am not asking the right question.
[00:52:06] Speaker C: That's right. And the closest analogy that I can think of is that you go to the doctor's office, right? You sit there, they probe everything about you, including a blood test, because you don't know how to ask the right question as a patient.
So that's what AI is doing to us right now. They're examining everything we say, how we behave. Every single speaker is reading information to preempt you.
So another movie will be Minority Report. You walk through the mall, you already know what you need before you even arrive. And that's all happening right now. There's some AI bot technologies just sounds just like a person. You can't tell the difference. We release it and it's customers calling. And in the middle of the conversation we can change the accent to Australian from an American accent.
[00:52:55] Speaker A: You know, it's, it's NFL football season. This reminds me of what some of the fintech in the real thought leadership world said stole the show for best commercial last super bowl was the Discover commercial where the woman from that HBO show says how do I prove I'm not a bot?
[00:53:17] Speaker C: Right?
[00:53:18] Speaker A: She's calling Discover and they're, they're joking about how they know like you don't have to speak to a bot. And then she says, wait a minute, how do I prove I'm not a bot? And not too many people picked up on it. The commercial ends. But there were some articles after saying how would you. And like what will be that mechanism where you can actually say something that you can prove that an AI agent couldn't say and, and what that will be in the future? And I don't, I don't know if anybody really had the answer. But for that reason it won the fintech minds.
[00:53:47] Speaker C: Yeah, that's right. So now there's a bit of issue where, how do we know the model accuracy? Right. So do you have to do this ensembling approach where you run 10 different LLM models or whatever industry that is. Right. And check on each other? Right. If 8 out of 10 LLM says yes, then that might be the right answer. Nobody knows. But some of these concepts are written in the 1960s, which is now we just have more CPU computing power to do this.
[00:54:13] Speaker A: That's, that's all that is. Is that like what you think it'll be like, like some sort of like those trick questions we had, like riddles, I guess, growing up. Like somebody goes up the ski. Three people go up the ski mountain. How many people come down?
[00:54:24] Speaker C: Yeah.
[00:54:24] Speaker A: If they can do your voice and they can hack in and get all your information on the, on the dark web. So it sounds like Timothy, you've answered all my questions. How will you prove I'm actually talking to Timothy?
[00:54:35] Speaker C: I just, just had this conversation with my mother. Right. We didn't have a safe word.
Yeah, right. Because you know, the elderly is our first, first batch of people is going to get defrauded by this. Right. So who knows, man? You know, I'm, I'm GLAD I'm in 2025. Michael.
[00:54:53] Speaker A: Well, thank you for, thank you for having one of the most thought provoking shows we've had and it's great that we have it right out of the gates here.
We will put in the notes and you will be seeing a lot of you here on our LinkedIn feeds and hopefully that results in a huge growth for Lend API because the industry certainly needs you in our industry to move it forward and bring in a lot of your knowledge from the outside world.
Thank you again. Any final thoughts, Timothy and Michael?
[00:55:25] Speaker B: Timothy, thanks for coming on to the show. Twenty years ago it was take a loan application, put it into your loan origination software. It didn't matter what company you work for, whether you're retail or broker, and then, and then run du and then submit it out. Back then we had Transbox. Now we can actually do it electronically. But technology has launched us forward in such a meteoric rise in the way that data is being transferred and the type of transference today is not only for the originator but for the consumer to have a better experience as a result.
So, Tim, thanks for, thanks for coming to today. We could probably have another hour just on the transference of information, but we are on the precipice for this for the mortgage industry, for everybody in the Los Angeles area, our prayers are out for you. And Tim, thanks so much for coming aboard to.
[00:56:20] Speaker C: Thank you very much, Michael and Mike as well. Thanks for the invitation. I thoroughly enjoyed it. Thank you.
