1 00:00:04,400 --> 00:00:07,800 Speaker 1: Welcome to tex Stuff, a production from I Heart Radio. 2 00:00:11,840 --> 00:00:14,440 Speaker 1: This season of Smart Talks with IBM is all about 3 00:00:14,600 --> 00:00:18,480 Speaker 1: new creators, the developers, data scientists, c t o s 4 00:00:18,560 --> 00:00:23,320 Speaker 1: and other visionaries creatively applying technology in business to drive change. 5 00:00:23,800 --> 00:00:26,680 Speaker 1: They use their knowledge and creativity to develop better ways 6 00:00:26,680 --> 00:00:30,280 Speaker 1: of working, no matter the industry. Join hosts from your 7 00:00:30,320 --> 00:00:33,880 Speaker 1: favorite Pushkin Industries podcasts as they use their expertise to 8 00:00:33,960 --> 00:00:37,480 Speaker 1: deepen these conversations, and of course Malcolm Gladwell will guide 9 00:00:37,520 --> 00:00:39,839 Speaker 1: you through the season as your host and provide his 10 00:00:39,920 --> 00:00:43,000 Speaker 1: thoughts and analysis along the way. Look out for new 11 00:00:43,040 --> 00:00:45,480 Speaker 1: episodes of Smart Talks with IBM on the I Heart 12 00:00:45,560 --> 00:00:49,159 Speaker 1: Radio app, Apple Podcasts, or wherever you get your podcasts, 13 00:00:49,360 --> 00:00:55,160 Speaker 1: and learn more at IBM dot com slash smart talks. Hello, Hello, 14 00:00:55,320 --> 00:00:58,640 Speaker 1: welcome to a new season of Smart Talks with IBM, 15 00:00:58,680 --> 00:01:03,880 Speaker 1: a podcast from pushed In Industries, I Heart Radio and IBM. 16 00:01:03,920 --> 00:01:08,440 Speaker 1: I'm Malcolm Gladwell. This season we're talking to new creators, 17 00:01:08,959 --> 00:01:12,560 Speaker 1: the developers, data scientists, c t o s and other 18 00:01:12,640 --> 00:01:17,960 Speaker 1: visionaries who are creatively applying technology in business to drive change. 19 00:01:18,560 --> 00:01:22,360 Speaker 1: Channeling their knowledge and expertise, they're developing more creative and 20 00:01:22,400 --> 00:01:27,759 Speaker 1: effective solutions no matter the industry. Our guest today are 21 00:01:27,800 --> 00:01:32,759 Speaker 1: Brian Young and Stephen Better, co founders of home lending Pal. 22 00:01:33,560 --> 00:01:36,720 Speaker 1: Home lending Pal is a member of the IBM hyper 23 00:01:36,720 --> 00:01:42,160 Speaker 1: Protect Accelerator, an investment readiness and technical mentorship program that 24 00:01:42,280 --> 00:01:48,480 Speaker 1: supports impact focused startups leveraging highly sensitive data. Their story 25 00:01:48,680 --> 00:01:53,360 Speaker 1: is a perfect place to start our season. They recognized 26 00:01:53,440 --> 00:01:57,720 Speaker 1: a profound problem, the horrible process of getting a home loan, 27 00:01:58,320 --> 00:02:01,240 Speaker 1: especially if you're part of an unders serve community, a 28 00:02:01,360 --> 00:02:05,400 Speaker 1: process that, as you'll hear, is not only confusing and complex, 29 00:02:05,680 --> 00:02:10,160 Speaker 1: but often deeply unfair. So Brian and Stephen teamed up 30 00:02:10,200 --> 00:02:13,680 Speaker 1: to use technology to attack that problem in a bunch 31 00:02:13,720 --> 00:02:17,480 Speaker 1: of creative ways. You'll hear how they're tapping into blockchain 32 00:02:17,760 --> 00:02:21,079 Speaker 1: to make the home loan process more transparent and fair, 33 00:02:21,760 --> 00:02:24,880 Speaker 1: using AI to help people learn how to qualify for 34 00:02:24,919 --> 00:02:29,120 Speaker 1: a loan, and relying on IBM technology to store consumers 35 00:02:29,160 --> 00:02:34,520 Speaker 1: most sensitive information safely in the cloud. Brian and Stephen 36 00:02:34,560 --> 00:02:38,480 Speaker 1: talked with Jacob Goldstein, host of the pushkin podcast What's 37 00:02:38,480 --> 00:02:42,560 Speaker 1: Your Problem. Jacob has covered technology and business for over 38 00:02:42,600 --> 00:02:46,720 Speaker 1: a decade, first at The Wall Street Journal, then at MPR. 39 00:02:47,760 --> 00:02:58,880 Speaker 1: Now let's get into the interview. Let's start this like 40 00:02:58,919 --> 00:03:01,760 Speaker 1: a brom car How did you meet each other and 41 00:03:02,080 --> 00:03:05,040 Speaker 1: decided to start a company together. Steven was supposed to 42 00:03:05,040 --> 00:03:07,680 Speaker 1: come to a bachelor party in Miami and didn't show up, 43 00:03:07,840 --> 00:03:11,000 Speaker 1: and it broke my heart. There's more to the story 44 00:03:11,080 --> 00:03:14,600 Speaker 1: than just simply that one of my old employees introduced us. 45 00:03:15,240 --> 00:03:18,120 Speaker 1: I've just left Marcato. They've been acquired for one point 46 00:03:18,120 --> 00:03:20,560 Speaker 1: for a billion, and I am, you know, living the 47 00:03:20,600 --> 00:03:23,200 Speaker 1: Miami lifestyle. You know, I have a condo on the 48 00:03:23,240 --> 00:03:25,880 Speaker 1: water and all the nice things to go with. A 49 00:03:25,880 --> 00:03:28,760 Speaker 1: guy named Michael Ramsey had asked me, you know what, 50 00:03:28,960 --> 00:03:32,000 Speaker 1: I helped him do mortgage lead generation and I was like, 51 00:03:32,040 --> 00:03:33,840 Speaker 1: you know, sure, I'm not doing anything else? Why not? 52 00:03:34,680 --> 00:03:38,000 Speaker 1: And I meet Steve that he was in North Carolina. 53 00:03:38,560 --> 00:03:42,000 Speaker 1: I left a pretty fruitful career in banking. I was 54 00:03:42,760 --> 00:03:46,120 Speaker 1: an underwriter. Underwriting loans mean it's basically deciding who should 55 00:03:46,120 --> 00:03:48,920 Speaker 1: get a loan and at what interest rate? Right, Absolutely 56 00:03:48,960 --> 00:03:53,040 Speaker 1: due diligence, right, which is understanding whether or not this 57 00:03:53,080 --> 00:03:58,440 Speaker 1: particular individual has the worldwithal to afford the mortgage. Also 58 00:03:58,640 --> 00:04:04,720 Speaker 1: the credit risk individual presents. But there was this disconnect 59 00:04:05,280 --> 00:04:09,320 Speaker 1: in that process where you have hidden action taking place 60 00:04:09,360 --> 00:04:12,440 Speaker 1: on one side of the transaction while you have another 61 00:04:12,440 --> 00:04:15,000 Speaker 1: side of the transaction that that tends to hide information. 62 00:04:15,960 --> 00:04:19,000 Speaker 1: And just to be clear, it's the borrower who hides 63 00:04:19,080 --> 00:04:22,719 Speaker 1: information and the bank that hides the action the lender 64 00:04:23,240 --> 00:04:27,000 Speaker 1: in most cases, but this is usually both sides of 65 00:04:27,040 --> 00:04:30,800 Speaker 1: the negotia. Everybody's hiding stuff from everybody else. There's a 66 00:04:30,800 --> 00:04:33,880 Speaker 1: absolutely and it's like sort of inadvertent as well too, 67 00:04:34,120 --> 00:04:37,040 Speaker 1: right in that process, and things fall through the cracks, 68 00:04:37,080 --> 00:04:40,279 Speaker 1: and you know, falling through the cracks means weaks without 69 00:04:40,360 --> 00:04:44,320 Speaker 1: notification from a bar's perspective as to whether or not 70 00:04:44,520 --> 00:04:47,440 Speaker 1: you know this deal is moving forward. Okay, So so 71 00:04:47,480 --> 00:04:50,159 Speaker 1: the problem is a lack of information on both sides, 72 00:04:50,520 --> 00:04:52,920 Speaker 1: and that winds up leading to bad outcomes. It winds 73 00:04:52,960 --> 00:04:56,480 Speaker 1: up leading to long delays that are frustrating or scary 74 00:04:56,600 --> 00:05:00,680 Speaker 1: for the for the borrower, yes, who are of consumers 75 00:05:00,680 --> 00:05:03,840 Speaker 1: just don't have anywhere to go if you go online, 76 00:05:03,880 --> 00:05:06,280 Speaker 1: everything is too broad engineering, especially if you know you're 77 00:05:06,320 --> 00:05:08,479 Speaker 1: not ready to buy at that moment. Uh. If you 78 00:05:08,520 --> 00:05:10,440 Speaker 1: talk to a lender or relatory, if you're not ready 79 00:05:10,440 --> 00:05:12,479 Speaker 1: to buy at that moment, there they'll help you, but 80 00:05:12,560 --> 00:05:14,240 Speaker 1: it's not the same level of help. But you're not 81 00:05:14,240 --> 00:05:16,880 Speaker 1: gonna get that same level of support over months because 82 00:05:16,880 --> 00:05:18,760 Speaker 1: you know, buying houses and like buying a piece of 83 00:05:18,800 --> 00:05:21,840 Speaker 1: candy online, And so we really looked at, Okay, well, 84 00:05:21,880 --> 00:05:24,919 Speaker 1: how can we give people this safe environment to go 85 00:05:25,040 --> 00:05:28,479 Speaker 1: explore and understand when homeownership could look like for them 86 00:05:28,560 --> 00:05:30,640 Speaker 1: based on their personal information. And that's kind of when 87 00:05:30,680 --> 00:05:33,160 Speaker 1: I reach back out to Stephen around August of two 88 00:05:33,200 --> 00:05:35,680 Speaker 1: thousand seventeen and said, hey, you know, we need to 89 00:05:35,680 --> 00:05:38,960 Speaker 1: do this together. You understand the back inside from a 90 00:05:39,040 --> 00:05:43,160 Speaker 1: lender underwritish perspective, and I understand the plight of the consumers, 91 00:05:43,160 --> 00:05:44,880 Speaker 1: and if we come together, this could be something that 92 00:05:44,920 --> 00:05:48,360 Speaker 1: could be really unique. A capitalist solution to a social 93 00:05:48,400 --> 00:05:50,839 Speaker 1: challenge is probably the best way to put it. So Stephen, 94 00:05:50,880 --> 00:05:53,760 Speaker 1: you're sort of coming from the banking side, and Brian 95 00:05:53,839 --> 00:05:58,159 Speaker 1: you're sort of coming from the tech side. Absolutely, what 96 00:05:58,320 --> 00:06:00,440 Speaker 1: exactly is the problem that you've got us are trying 97 00:06:00,480 --> 00:06:03,440 Speaker 1: to solve when you start this company, and its simple 98 00:06:03,480 --> 00:06:08,279 Speaker 1: assessence is data democratization, the ability to take complex information 99 00:06:08,839 --> 00:06:11,479 Speaker 1: and simplify so that someone that isn't an expert like 100 00:06:11,560 --> 00:06:15,880 Speaker 1: Stephen can understand what's going on, and in this case specifically, 101 00:06:15,920 --> 00:06:19,880 Speaker 1: what is the data that you're trying to democratize underwriting data, 102 00:06:20,000 --> 00:06:22,800 Speaker 1: so the decision or the data that is utilized to 103 00:06:22,920 --> 00:06:26,000 Speaker 1: determine whether or not you are approved or declined for 104 00:06:26,040 --> 00:06:29,360 Speaker 1: a home loan. So right now, if I go apply 105 00:06:29,480 --> 00:06:33,000 Speaker 1: for a loan, they approve me or they decline me. 106 00:06:33,880 --> 00:06:37,159 Speaker 1: But do I know why not? Really? I mean, you 107 00:06:37,160 --> 00:06:39,560 Speaker 1: get a letter of an adverse letter, but it's still 108 00:06:39,720 --> 00:06:42,120 Speaker 1: very broad Engineeric, it doesn't really tell you what to 109 00:06:42,160 --> 00:06:44,920 Speaker 1: focus on next, but you do have a very high 110 00:06:45,000 --> 00:06:48,760 Speaker 1: level sense of why your decline. Yeah, there's no true 111 00:06:48,839 --> 00:06:53,800 Speaker 1: guidance from that point of rejection, right there's no fundamental 112 00:06:53,880 --> 00:06:57,320 Speaker 1: understanding as to what could I have done better? And 113 00:06:57,440 --> 00:07:00,160 Speaker 1: that's really what sets this platform apart and all. So 114 00:07:00,360 --> 00:07:04,359 Speaker 1: why it's important how we're sort of reframing of this 115 00:07:04,440 --> 00:07:07,120 Speaker 1: data workflow. I want to get into the details of that. 116 00:07:07,200 --> 00:07:09,960 Speaker 1: But just as we sort of understand the problem a 117 00:07:10,040 --> 00:07:11,960 Speaker 1: little bit more, I mean, one piece of it that 118 00:07:12,000 --> 00:07:16,160 Speaker 1: we haven't talked about is is race and the home 119 00:07:16,160 --> 00:07:18,480 Speaker 1: ownership gap. Can you guys talk a little bit about 120 00:07:18,520 --> 00:07:21,119 Speaker 1: that and how it fits with with what you're trying 121 00:07:21,120 --> 00:07:25,920 Speaker 1: to do. Yeah, I mean, the home ownership gap, at 122 00:07:25,960 --> 00:07:29,240 Speaker 1: least for African Americans is larger now than it was 123 00:07:29,320 --> 00:07:33,440 Speaker 1: fifty years ago and segregation was legal, which is quite saddening. 124 00:07:33,480 --> 00:07:36,080 Speaker 1: But it's not just African Americans. And when you look 125 00:07:36,120 --> 00:07:40,200 Speaker 1: at declines, whether you are a woman, whether you are 126 00:07:40,240 --> 00:07:42,720 Speaker 1: a minority, whether you're part of the l p G, 127 00:07:42,880 --> 00:07:46,040 Speaker 1: t Q plus community, it shows that there's a higher 128 00:07:46,080 --> 00:07:48,960 Speaker 1: level of declines for these communities than there are for 129 00:07:48,960 --> 00:07:51,600 Speaker 1: for or white males. So you know, in our perspective, 130 00:07:51,600 --> 00:07:53,720 Speaker 1: there has to be a lot that needs to be 131 00:07:53,800 --> 00:07:57,360 Speaker 1: done in terms of resetting, reconfiguring the system to make 132 00:07:57,400 --> 00:08:00,440 Speaker 1: it more fair and eggable for all. So, if I 133 00:08:00,520 --> 00:08:05,000 Speaker 1: understand you correctly, you're saying, basically, in the current system, 134 00:08:05,160 --> 00:08:08,680 Speaker 1: white men have an easier time getting a mortgage than 135 00:08:08,760 --> 00:08:12,040 Speaker 1: anybody else. Well, you said it, I'll just agree with it. 136 00:08:12,720 --> 00:08:14,840 Speaker 1: I think you said it. I think if I understood 137 00:08:14,880 --> 00:08:18,200 Speaker 1: you're correctly said, yeah, that that is what the data shows. 138 00:08:18,360 --> 00:08:20,320 Speaker 1: It's not just my perspect that's what the data shows. 139 00:08:20,400 --> 00:08:24,600 Speaker 1: Is so, and so, how are you trying to help 140 00:08:24,680 --> 00:08:30,040 Speaker 1: fix that problem by turning everybody into corn? By turning 141 00:08:30,040 --> 00:08:32,680 Speaker 1: everybody into corn? I like it what do you mean 142 00:08:32,720 --> 00:08:36,000 Speaker 1: by that? Through the power of math, right, cryptography specifically, 143 00:08:36,520 --> 00:08:38,760 Speaker 1: we are able to make everyone look the same and 144 00:08:39,200 --> 00:08:44,880 Speaker 1: the underwriter just simply understands the fundamental attributes that ought 145 00:08:44,920 --> 00:08:48,480 Speaker 1: to drive that approval disapproval decision. Right, in order to 146 00:08:48,559 --> 00:08:52,240 Speaker 1: help us and also to help our government understand where 147 00:08:52,280 --> 00:08:56,880 Speaker 1: these biases are coming from, our lenders are required to 148 00:08:56,960 --> 00:09:00,200 Speaker 1: ask you what your raise, what your sex, even your age, 149 00:09:00,280 --> 00:09:03,880 Speaker 1: right Like, all of this comes with with this application scenario. 150 00:09:04,559 --> 00:09:09,640 Speaker 1: But does this information inadvertingly create the bias? Can we 151 00:09:09,800 --> 00:09:13,480 Speaker 1: make everyone look the same and start to remove or 152 00:09:14,400 --> 00:09:18,400 Speaker 1: better identify where these issues are sort of coming from. 153 00:09:18,720 --> 00:09:22,400 Speaker 1: So you're trying to use technology to blind all the 154 00:09:22,440 --> 00:09:26,440 Speaker 1: decision makers in the home loan process to race, ethnicity, 155 00:09:26,559 --> 00:09:32,200 Speaker 1: genre specifically blockchain. There are three big tech ideas behind 156 00:09:32,600 --> 00:09:35,520 Speaker 1: home lending pal at least three of that we're going 157 00:09:35,559 --> 00:09:38,640 Speaker 1: to talk about today on the show, and blockchain is 158 00:09:38,840 --> 00:09:41,640 Speaker 1: big tech idea Number one. You may have heard of 159 00:09:41,679 --> 00:09:46,640 Speaker 1: blockchain because it's the key idea behind cryptocurrency, but the 160 00:09:46,679 --> 00:09:50,280 Speaker 1: idea of blockchain is bigger than just digital money and 161 00:09:50,400 --> 00:09:53,000 Speaker 1: much more than just a new way to store information 162 00:09:53,040 --> 00:09:58,160 Speaker 1: on the Internet. Blockchain is a shared immutable ledger that 163 00:09:58,240 --> 00:10:03,320 Speaker 1: facilitates the process of recording transactions and tracking assets in 164 00:10:03,400 --> 00:10:07,880 Speaker 1: a business network. Brian and Stephen want to use blockchain 165 00:10:07,960 --> 00:10:10,680 Speaker 1: to gather up the information on race and gender that's 166 00:10:10,720 --> 00:10:14,319 Speaker 1: required by law without showing it to the lenders making 167 00:10:14,320 --> 00:10:18,880 Speaker 1: the decisions about who gets alone. Our argument or our 168 00:10:19,480 --> 00:10:23,000 Speaker 1: thesis is that with the leverage of a mutable ledger 169 00:10:23,040 --> 00:10:25,480 Speaker 1: such as blockchain, you're able to still collect the information 170 00:10:25,480 --> 00:10:28,640 Speaker 1: that is necessary for the Home Mortgage Disclosure Act or 171 00:10:28,720 --> 00:10:31,840 Speaker 1: HUMMED as Stephen was referring to. But then with a 172 00:10:31,920 --> 00:10:34,360 Speaker 1: smart contract, you don't have to release that information, so 173 00:10:34,520 --> 00:10:37,360 Speaker 1: after the decision, the approval of decline is made for 174 00:10:37,400 --> 00:10:40,280 Speaker 1: the consumer. So you have this big idea for what 175 00:10:40,360 --> 00:10:41,679 Speaker 1: you want to do as a business, which you want 176 00:10:41,679 --> 00:10:45,120 Speaker 1: to do socially, but how do you make creative use 177 00:10:45,160 --> 00:10:47,760 Speaker 1: of technology to do the thing you want to do 178 00:10:47,840 --> 00:10:50,520 Speaker 1: to make it real? You know, we're trying to build 179 00:10:50,520 --> 00:10:53,240 Speaker 1: something that hasn't been done in the mortgage industry, especially 180 00:10:53,240 --> 00:10:56,040 Speaker 1: when talking about artificial intelligence and a virtual assistant. Most 181 00:10:56,040 --> 00:10:58,760 Speaker 1: people think of that it's just a one way street. 182 00:10:59,240 --> 00:11:01,280 Speaker 1: You know, we are trying to build this human like 183 00:11:01,440 --> 00:11:04,960 Speaker 1: interaction where it is able to not only understand, but 184 00:11:05,080 --> 00:11:08,840 Speaker 1: to respond, and then to leverage those responses and create 185 00:11:08,840 --> 00:11:12,199 Speaker 1: a road map towards allowing you to achieve your goals, 186 00:11:12,280 --> 00:11:15,000 Speaker 1: which is probably one of the most creative things that 187 00:11:15,120 --> 00:11:18,400 Speaker 1: I've ever done personally. But it also requires you to 188 00:11:18,480 --> 00:11:21,199 Speaker 1: be willing to accept constructive criticism from the people that 189 00:11:21,240 --> 00:11:24,200 Speaker 1: are going to be using it up front, and a 190 00:11:24,280 --> 00:11:26,000 Speaker 1: lot of what we're doing is really trying to find 191 00:11:26,000 --> 00:11:28,559 Speaker 1: creative ways just to get them involved in that conversation, 192 00:11:28,600 --> 00:11:30,720 Speaker 1: to say that, hey, you know, we are trying to 193 00:11:30,760 --> 00:11:33,440 Speaker 1: build this to help you. Right now, there's about twenty 194 00:11:33,440 --> 00:11:36,840 Speaker 1: one million mortgage ready millennials today that are qualified to 195 00:11:36,840 --> 00:11:39,960 Speaker 1: buy alone, even though they're not trying. They just don't know. 196 00:11:40,280 --> 00:11:44,199 Speaker 1: We're trying to bring greater trust and transparency to this process. Yeah, 197 00:11:44,400 --> 00:11:48,000 Speaker 1: I guess from my perspective, beyond just simply understanding the 198 00:11:48,080 --> 00:11:51,800 Speaker 1: technology and what it's able to do, I think it 199 00:11:51,840 --> 00:11:55,280 Speaker 1: takes the will to go ahead and take on that 200 00:11:55,400 --> 00:12:00,959 Speaker 1: complexity to try something new. We were child is constantly 201 00:12:01,240 --> 00:12:05,640 Speaker 1: with why not a simpler solution? Right? But in reality 202 00:12:05,679 --> 00:12:10,040 Speaker 1: the problem is much more complicated than the simplicity these 203 00:12:10,080 --> 00:12:13,560 Speaker 1: forces wanted to bring it into the table. You have 204 00:12:13,640 --> 00:12:16,040 Speaker 1: to have vision, you have to have a desire to 205 00:12:16,120 --> 00:12:20,080 Speaker 1: want to make fundamental change. Yeah, new tech built on 206 00:12:20,080 --> 00:12:23,400 Speaker 1: the old, broken processes doesn't allow for systemic change. You know, 207 00:12:23,440 --> 00:12:27,280 Speaker 1: you have to try to find ways to not only 208 00:12:27,320 --> 00:12:29,600 Speaker 1: just to make it easier for people to connect to lenders, 209 00:12:29,640 --> 00:12:32,000 Speaker 1: but at the core of what we were trying to build, 210 00:12:32,000 --> 00:12:34,640 Speaker 1: we really wanted to address the systemic issues in the 211 00:12:34,679 --> 00:12:37,280 Speaker 1: home buying process, and that required us to try something 212 00:12:37,320 --> 00:12:39,800 Speaker 1: different basically, and I think that's the most creative thing 213 00:12:39,840 --> 00:12:42,840 Speaker 1: you can do in an industry that ticularly Stephen mentioned 214 00:12:42,840 --> 00:12:45,559 Speaker 1: and wanted us to do is simpler. Yeah. So one 215 00:12:45,559 --> 00:12:50,200 Speaker 1: of the ideas you guys have is that transparency can 216 00:12:50,200 --> 00:12:54,600 Speaker 1: help reduce bias. So in what we are, you're using 217 00:12:54,679 --> 00:12:59,520 Speaker 1: technology to bring more transparency to the home buying process. 218 00:13:00,600 --> 00:13:04,199 Speaker 1: When we speak of transparency, when we speak of trust, 219 00:13:04,600 --> 00:13:07,320 Speaker 1: where we're really talking about it is just the natural 220 00:13:07,960 --> 00:13:13,319 Speaker 1: features of the blockchain. Right. It's transparent because all participants 221 00:13:13,760 --> 00:13:18,280 Speaker 1: within this framework have access to this decentralized ledger, So 222 00:13:18,559 --> 00:13:23,240 Speaker 1: we are all seeing how these pieces are sort of moving. Right, 223 00:13:23,280 --> 00:13:27,520 Speaker 1: we're playing poker with our cards facing up when we're 224 00:13:27,559 --> 00:13:32,199 Speaker 1: speaking to trust, right, we're speaking of the mutability of 225 00:13:32,240 --> 00:13:35,760 Speaker 1: this information, knowing that if an action is taken, it's 226 00:13:36,120 --> 00:13:39,080 Speaker 1: there on the ledger and we can't just simply remove it. 227 00:13:39,679 --> 00:13:47,000 Speaker 1: So these features lead to this forceful curing of certain 228 00:13:47,040 --> 00:13:51,800 Speaker 1: biases that tends to form within certain systems. Um, we're 229 00:13:51,800 --> 00:13:55,360 Speaker 1: not saying that we're going to remove all bias, but 230 00:13:55,440 --> 00:13:58,080 Speaker 1: what we're saying is that we feel very confident that 231 00:13:58,120 --> 00:14:02,960 Speaker 1: we'll be able to reduce it said nificantly without regulatory 232 00:14:03,080 --> 00:14:07,280 Speaker 1: reinforcement by the simple nature of this technology stack that 233 00:14:07,320 --> 00:14:11,880 Speaker 1: we're developing. So was there some moment when you guys 234 00:14:11,960 --> 00:14:15,520 Speaker 1: had the like light bulb, the high idea that you 235 00:14:15,559 --> 00:14:19,200 Speaker 1: could do this. The moment that made me realize that 236 00:14:19,280 --> 00:14:22,120 Speaker 1: this was doable was when our first group of lenders invested. 237 00:14:22,160 --> 00:14:24,800 Speaker 1: There was a group called the Mortgage Collaborative. They are 238 00:14:24,840 --> 00:14:28,680 Speaker 1: a collection of about three hundred five lenders I believe 239 00:14:28,720 --> 00:14:32,840 Speaker 1: across the country. They represent about the overall originations that 240 00:14:32,880 --> 00:14:35,520 Speaker 1: happened in the US. When they kind of stepped in 241 00:14:35,600 --> 00:14:37,480 Speaker 1: and we're like, hey, you know, we're gonna lead your 242 00:14:38,200 --> 00:14:40,400 Speaker 1: your development before your Series A, We're going to try 243 00:14:40,440 --> 00:14:41,880 Speaker 1: to help you there. I think that was the moment 244 00:14:41,960 --> 00:14:44,560 Speaker 1: for for me and then we had shortly after that. 245 00:14:44,680 --> 00:14:46,880 Speaker 1: Joining that round was a group called Quo Mutual or 246 00:14:46,880 --> 00:14:50,320 Speaker 1: CMfg Adventures their discovery fund, and they are the one 247 00:14:50,360 --> 00:14:52,960 Speaker 1: of the largest collections of credit unions in the industry. So, 248 00:14:53,280 --> 00:14:55,640 Speaker 1: you know, typically you have an issue where you know, 249 00:14:55,720 --> 00:14:58,640 Speaker 1: consumers feel like there's a problem that's not truly being solved. 250 00:14:58,640 --> 00:15:00,840 Speaker 1: But to see that lenders were looking to try to 251 00:15:00,880 --> 00:15:03,320 Speaker 1: find solutions like ours, I think that was the moment 252 00:15:03,360 --> 00:15:05,400 Speaker 1: for me. They said, hey, you know this could be 253 00:15:05,440 --> 00:15:07,840 Speaker 1: feasible for us, that the people who will actually have 254 00:15:08,000 --> 00:15:11,400 Speaker 1: to work with you want to help you. Like, that's 255 00:15:11,440 --> 00:15:17,440 Speaker 1: exactly great, but just tell me how will it work? Like, 256 00:15:17,520 --> 00:15:20,440 Speaker 1: walk me through. I'm an ordinary person. I want to 257 00:15:20,480 --> 00:15:24,240 Speaker 1: get alone. I come to home lending Pal. What happens 258 00:15:24,560 --> 00:15:26,920 Speaker 1: when when you're fully you know, fully up and running. 259 00:15:26,920 --> 00:15:30,600 Speaker 1: How's it gonna work? Yes, So you will spend about 260 00:15:30,640 --> 00:15:33,720 Speaker 1: five minutes going through our onboarding process where you're connecting 261 00:15:33,720 --> 00:15:36,960 Speaker 1: your online bank accounts, you're authorizing and soft FCO. Cool. 262 00:15:37,480 --> 00:15:40,640 Speaker 1: There's a credit report basically a credit report. You're here. 263 00:15:40,680 --> 00:15:43,200 Speaker 1: Most people don't realize so so lenders are utilizing your 264 00:15:43,200 --> 00:15:46,040 Speaker 1: FICO scores and most of the places online that you're 265 00:15:46,080 --> 00:15:47,960 Speaker 1: able to go to your showing a vantage score. So 266 00:15:47,960 --> 00:15:50,160 Speaker 1: that's kind of the first level of disconnect and so 267 00:15:50,200 --> 00:15:53,240 Speaker 1: we're solving for that first. So you go to that 268 00:15:53,320 --> 00:15:58,000 Speaker 1: process and then after you signed up, our virtual assistant 269 00:15:58,120 --> 00:16:02,960 Speaker 1: keV begins doing his he's analyzing your profile. Uh. He's 270 00:16:03,000 --> 00:16:06,520 Speaker 1: really a geared towards helping you understand really three or 271 00:16:06,560 --> 00:16:10,400 Speaker 1: four critical elements. You know, one your likelihood for success 272 00:16:10,480 --> 00:16:14,480 Speaker 1: or approval to some financial modeling and forecasts and give 273 00:16:14,520 --> 00:16:17,280 Speaker 1: you a better understanding of when you should begin the 274 00:16:17,360 --> 00:16:19,480 Speaker 1: process to apply for for a home. So how long 275 00:16:19,480 --> 00:16:21,920 Speaker 1: will it take you to become a homeowner or to 276 00:16:21,920 --> 00:16:25,280 Speaker 1: close on the the home? Three the best loan product for you, 277 00:16:25,960 --> 00:16:28,320 Speaker 1: and then for the lenders within our ecosystem, they present 278 00:16:28,400 --> 00:16:31,120 Speaker 1: the best chance of success with them as well. So, 279 00:16:31,320 --> 00:16:37,160 Speaker 1: so you mentioned a virtual advisor, keV virtual meaning it's 280 00:16:37,200 --> 00:16:41,720 Speaker 1: not a guy named keV, right, it's it's named after 281 00:16:41,760 --> 00:16:44,320 Speaker 1: one of my my good friends from college that passed 282 00:16:44,320 --> 00:16:47,080 Speaker 1: from a rare form of germ sale cancer. He's probably 283 00:16:47,080 --> 00:16:49,240 Speaker 1: one of the most helpful, friendly people that you've ever met, 284 00:16:49,240 --> 00:16:51,720 Speaker 1: and it didn't matter who you were, So we really 285 00:16:51,720 --> 00:16:57,560 Speaker 1: wanted to encompass his personality into the solution itself. But yes, keV, 286 00:16:59,040 --> 00:17:01,440 Speaker 1: it becomes a friend pal. You know, so even if 287 00:17:01,440 --> 00:17:04,000 Speaker 1: you're not ready to buy, he just doesn't pass you 288 00:17:04,040 --> 00:17:06,120 Speaker 1: off and say hey, I'm not going to help. It 289 00:17:06,160 --> 00:17:09,040 Speaker 1: really analyzes your ber profile and begins to create a 290 00:17:09,080 --> 00:17:13,720 Speaker 1: path that you can follow to become a homeowner. We 291 00:17:13,760 --> 00:17:17,720 Speaker 1: have arrived at big tech idea number two. keV the 292 00:17:17,840 --> 00:17:23,560 Speaker 1: Virtual Assistant is built using powerful artificial intelligence tools. The 293 00:17:23,720 --> 00:17:27,960 Speaker 1: AI takes the potential homebuyers information and runs it through 294 00:17:28,000 --> 00:17:31,280 Speaker 1: algorithms that tell you things like how likely you are 295 00:17:31,320 --> 00:17:33,960 Speaker 1: to get alan, and what loan makes the most sense 296 00:17:34,000 --> 00:17:36,840 Speaker 1: for you, and how long the whole process is likely 297 00:17:36,880 --> 00:17:40,320 Speaker 1: to take. You can ask keV questions and it will 298 00:17:40,359 --> 00:17:43,520 Speaker 1: give you answers. But keV is more than your average 299 00:17:43,560 --> 00:17:48,400 Speaker 1: responder chatbot. It speaks conversationally, It knows who you are, 300 00:17:48,680 --> 00:17:52,800 Speaker 1: understands your needs, and helps beyond just providing a frequently 301 00:17:52,840 --> 00:17:55,960 Speaker 1: asked questions link. Brian says he thinks a lot of 302 00:17:55,960 --> 00:17:58,880 Speaker 1: people might be more comfortable talking with an AI powered 303 00:17:58,960 --> 00:18:02,639 Speaker 1: virtual assistant then with a human loan officer at a bank. 304 00:18:03,680 --> 00:18:06,439 Speaker 1: I think it really solves a cultural problem there. There 305 00:18:06,440 --> 00:18:10,639 Speaker 1: are cultural barriers that prevent different segments from becoming homeowners 306 00:18:10,720 --> 00:18:13,560 Speaker 1: or at least impact they're buying decisions in terms of 307 00:18:13,560 --> 00:18:16,640 Speaker 1: how they explore homeownership. So the first part is to 308 00:18:16,680 --> 00:18:18,800 Speaker 1: try to use this virtual assistant just to make them 309 00:18:18,800 --> 00:18:22,400 Speaker 1: feel comfortable getting into the process of what homeownership could 310 00:18:22,440 --> 00:18:25,400 Speaker 1: look like. And then from there it is about preparing them, 311 00:18:25,400 --> 00:18:28,640 Speaker 1: getting them better qualify so that once they are ready 312 00:18:28,680 --> 00:18:30,959 Speaker 1: to say, hey, I want to come home owner, I 313 00:18:30,960 --> 00:18:34,800 Speaker 1: found the house that I love, allowing that transaction, that 314 00:18:35,200 --> 00:18:37,840 Speaker 1: process to be a lot smoother and easier through the 315 00:18:37,880 --> 00:18:41,040 Speaker 1: use of blockchain. Basically, so when you say cultural, I 316 00:18:41,040 --> 00:18:45,520 Speaker 1: mean does that include in part race and ethnicity, people 317 00:18:45,560 --> 00:18:50,720 Speaker 1: who have traditionally been excluded from the banking sector from housing. 318 00:18:51,320 --> 00:18:54,040 Speaker 1: Is the dream that sort of AI can help people 319 00:18:54,080 --> 00:18:58,360 Speaker 1: who have been excluded become more included. Yes, most white 320 00:18:58,359 --> 00:19:02,520 Speaker 1: people have resources. They have other friends and family who 321 00:19:02,520 --> 00:19:06,359 Speaker 1: have gone through this process successfully multiple times as opposed 322 00:19:06,359 --> 00:19:09,560 Speaker 1: to just the one time. Within our communities, is difficult 323 00:19:09,600 --> 00:19:12,840 Speaker 1: just to find the one person that you can discuss 324 00:19:12,920 --> 00:19:16,040 Speaker 1: this process with, and most of the time that one 325 00:19:16,080 --> 00:19:19,720 Speaker 1: person has gone through a negative experience in that right. 326 00:19:20,000 --> 00:19:23,159 Speaker 1: Brian's parents have experienced difficulty in this instry. My parents 327 00:19:23,400 --> 00:19:27,200 Speaker 1: have experienced difficulty in this process too. Isn't until you 328 00:19:27,240 --> 00:19:30,879 Speaker 1: get to our generation where you have family members that 329 00:19:30,920 --> 00:19:34,800 Speaker 1: have gone through this process multiple times and have been successful. 330 00:19:35,200 --> 00:19:39,760 Speaker 1: So when we speak to keV being culturally relevant, it's 331 00:19:39,880 --> 00:19:44,880 Speaker 1: because keV is there to provide you accurate support that 332 00:19:45,080 --> 00:19:49,720 Speaker 1: historically hasn't been available to these marginalized groups. Stephen, you 333 00:19:49,840 --> 00:19:54,440 Speaker 1: mentioned your own families, your and Brian's famili's experience with 334 00:19:54,960 --> 00:19:57,400 Speaker 1: getting home loans with the banking system. Do you guys 335 00:19:57,400 --> 00:20:00,080 Speaker 1: mind us talking about that specifically? What have been in 336 00:20:00,119 --> 00:20:06,240 Speaker 1: your family's experiences with getting loan? Yeah? Yeah, I mean 337 00:20:06,600 --> 00:20:09,159 Speaker 1: back in the subprime mortgage crisis, and you know, my 338 00:20:09,200 --> 00:20:11,119 Speaker 1: mom nearly lost her dream home that I bought for 339 00:20:11,800 --> 00:20:13,960 Speaker 1: those primarily because we were in an arm even though 340 00:20:13,960 --> 00:20:15,680 Speaker 1: we should have been an av A loan because she 341 00:20:15,720 --> 00:20:18,719 Speaker 1: has a military veteran and an armed an adjustable rate 342 00:20:18,760 --> 00:20:22,240 Speaker 1: loans that was way worse than the mortgage. It was 343 00:20:22,240 --> 00:20:24,120 Speaker 1: way worse. I mean, you know, it started out better 344 00:20:24,200 --> 00:20:26,560 Speaker 1: just because you pay less, but once that interest rate flips, 345 00:20:26,600 --> 00:20:28,919 Speaker 1: it becomes way worse if you're not prepared for it. 346 00:20:28,960 --> 00:20:31,680 Speaker 1: And I think you know, again, when when we talk 347 00:20:31,720 --> 00:20:34,600 Speaker 1: about these cultural factors, there's really five that you deal with. 348 00:20:34,640 --> 00:20:38,080 Speaker 1: There's there's cultural itself. So things like the subprime mortage 349 00:20:38,119 --> 00:20:41,440 Speaker 1: crisis where African Americans are hurt the most coming out 350 00:20:41,440 --> 00:20:44,440 Speaker 1: of that, you have red lining, reverse rate lining, etcetera. 351 00:20:45,160 --> 00:20:49,000 Speaker 1: Red Lining is basically the history of lenders not making 352 00:20:49,080 --> 00:20:53,280 Speaker 1: loans to people in predominantly black neighborhoods. Essential exactly, we're 353 00:20:53,280 --> 00:20:56,960 Speaker 1: picking in which areas they will lend to specific groups. Yes, 354 00:20:57,000 --> 00:21:00,159 Speaker 1: and those areas were predominantly white historically hamper dom wa 355 00:21:00,200 --> 00:21:03,439 Speaker 1: why Yes. So you have those elements. You have the 356 00:21:03,440 --> 00:21:07,080 Speaker 1: economic elements where there's this concept of its just being 357 00:21:07,240 --> 00:21:11,560 Speaker 1: unattainable for us. You have the psychological elements of being 358 00:21:11,760 --> 00:21:14,080 Speaker 1: misunderstood thinking that the only way I can buy a 359 00:21:14,119 --> 00:21:17,040 Speaker 1: home is having down to put down towards of down payment, 360 00:21:17,440 --> 00:21:21,320 Speaker 1: and that's just not true. So our ultimate objective is 361 00:21:21,359 --> 00:21:24,040 Speaker 1: just really to make that more attainable for for everyone. 362 00:21:24,080 --> 00:21:26,120 Speaker 1: And it's really for all load of monitor income borrowers 363 00:21:26,240 --> 00:21:29,560 Speaker 1: these days, just because with rates increasing, with the supply 364 00:21:29,640 --> 00:21:32,399 Speaker 1: shortages that we have, you know, homeownership is really going 365 00:21:32,400 --> 00:21:35,240 Speaker 1: to become a lot more difficult for a lot of people, 366 00:21:35,280 --> 00:21:38,879 Speaker 1: regardless of their age, sex, and race. So you have 367 00:21:38,960 --> 00:21:41,880 Speaker 1: this industry that suffers from a lack of transparency, from 368 00:21:42,000 --> 00:21:45,200 Speaker 1: historical bias in terms of race and gender. You start 369 00:21:45,280 --> 00:21:47,960 Speaker 1: this technology driven company to try and fix those things. 370 00:21:48,280 --> 00:21:50,000 Speaker 1: As you're building the company, how do you come to 371 00:21:50,040 --> 00:21:55,480 Speaker 1: work with IBM um our need for data protection and security? 372 00:21:55,640 --> 00:22:00,919 Speaker 1: So you're talking about digitizing documents, digitizing information to allow 373 00:22:01,200 --> 00:22:06,400 Speaker 1: greater access to underserved underrepresented groups. And IBM had their 374 00:22:06,480 --> 00:22:10,480 Speaker 1: hyper Protect Accelerator which was entirely focused on that, taking 375 00:22:10,520 --> 00:22:14,560 Speaker 1: small startups like ours and allowing them to basically run 376 00:22:14,600 --> 00:22:17,680 Speaker 1: the palace that we ran without having to worry about 377 00:22:17,680 --> 00:22:21,320 Speaker 1: people's information getting stolen in essence, and then Steve and 378 00:22:21,359 --> 00:22:23,919 Speaker 1: I were just very aggressive in terms of just reaching 379 00:22:23,920 --> 00:22:28,160 Speaker 1: out to different vps, different executives at IBM, kind of saying, 380 00:22:28,200 --> 00:22:29,560 Speaker 1: you know, here's what we want to do, here's what 381 00:22:29,600 --> 00:22:32,959 Speaker 1: we need, will you help us? And being in an 382 00:22:33,000 --> 00:22:36,760 Speaker 1: industry that is so regulated, it helped us really get 383 00:22:36,760 --> 00:22:39,560 Speaker 1: to that door, just because you know, every bank has 384 00:22:39,800 --> 00:22:43,600 Speaker 1: a vendor on boarding process that requires a very high 385 00:22:43,680 --> 00:22:46,480 Speaker 1: level of data security to even work with them. In in 386 00:22:46,400 --> 00:22:52,000 Speaker 1: in essence, here's the third big tech idea in the 387 00:22:52,080 --> 00:22:57,080 Speaker 1: home Lending Pal story protecting data in the cloud. Think 388 00:22:57,080 --> 00:22:59,919 Speaker 1: about the problem this one is solving. Brian and Stephen 389 00:23:00,119 --> 00:23:03,720 Speaker 1: have this little startup. They need to collect supersensitive data 390 00:23:03,800 --> 00:23:06,640 Speaker 1: from people. Everything you have to show the bank when 391 00:23:06,640 --> 00:23:09,800 Speaker 1: you want to get a mortgage, This data has to 392 00:23:09,840 --> 00:23:15,040 Speaker 1: be secure. IBMS hyper Protect Accelerator enables small businesses to 393 00:23:15,119 --> 00:23:18,960 Speaker 1: store sensitive data in the cloud and keep that data secure. 394 00:23:19,800 --> 00:23:22,959 Speaker 1: Brian says, it lets home lending Pal do something they 395 00:23:23,000 --> 00:23:27,280 Speaker 1: would never do on their own. From a technical perspective, 396 00:23:27,320 --> 00:23:29,840 Speaker 1: you have different compliance checks that you have to meet 397 00:23:29,880 --> 00:23:33,320 Speaker 1: to work with banking institutions or financial institutions. So it 398 00:23:33,359 --> 00:23:37,000 Speaker 1: allows a small startup like home Lending Pal to still 399 00:23:37,040 --> 00:23:40,240 Speaker 1: be able to meet those checks and balances to bring 400 00:23:40,320 --> 00:23:44,040 Speaker 1: an innovative solution to the table for a financial institution, 401 00:23:44,119 --> 00:23:46,439 Speaker 1: where more than likely as a startup, you're not going 402 00:23:46,480 --> 00:23:47,960 Speaker 1: to have the ability to do that on your own, 403 00:23:48,000 --> 00:23:51,000 Speaker 1: just because it is so expensive to either have internal 404 00:23:51,040 --> 00:23:52,680 Speaker 1: servers or to try to do it on your own 405 00:23:52,680 --> 00:23:55,639 Speaker 1: as well. So so people have to trust you to 406 00:23:56,000 --> 00:23:58,520 Speaker 1: use home landing Piller right Like, I'm giving you everything, 407 00:23:59,080 --> 00:24:01,280 Speaker 1: how do you can into me? How do you convince 408 00:24:01,320 --> 00:24:06,240 Speaker 1: customers that you're going to keep their data safe absolutely. Um. 409 00:24:06,440 --> 00:24:08,919 Speaker 1: Part of it is doing stuff like this where we're 410 00:24:08,960 --> 00:24:12,040 Speaker 1: acknowledging and making the consumers aware of our relationship with 411 00:24:12,119 --> 00:24:14,879 Speaker 1: IBM and how IBM is handling our storage of the 412 00:24:14,960 --> 00:24:18,960 Speaker 1: data and the sensitive data itself. Technically, the IBM description 413 00:24:19,000 --> 00:24:22,480 Speaker 1: of it is their confidential computing services or cloud services, 414 00:24:22,520 --> 00:24:24,840 Speaker 1: and it's basically saying that even though the information is 415 00:24:24,880 --> 00:24:27,399 Speaker 1: stored in the cloud, IBM is going to do a 416 00:24:27,400 --> 00:24:30,600 Speaker 1: lot to help Home Lending Pal protect this sensitive data. 417 00:24:31,160 --> 00:24:33,080 Speaker 1: Part of it is being able to show IBM s 418 00:24:33,119 --> 00:24:36,000 Speaker 1: logo on our website. You'll you'll be surprised how much 419 00:24:36,000 --> 00:24:39,960 Speaker 1: logo recognition helps people understand that this is a legit business, 420 00:24:40,000 --> 00:24:42,960 Speaker 1: a legit company, if you will. And then there's also 421 00:24:43,000 --> 00:24:46,440 Speaker 1: stuff like you know, people seeing the address of the business, 422 00:24:46,520 --> 00:24:49,240 Speaker 1: contact information for the business, like all this stuff factors 423 00:24:49,280 --> 00:24:51,800 Speaker 1: into why people will be willing to give us their data. 424 00:24:52,119 --> 00:24:54,000 Speaker 1: But a lot of that is very contingental, just people 425 00:24:54,040 --> 00:24:56,159 Speaker 1: seeing the IBM logo and saying that, hey, you know, 426 00:24:56,240 --> 00:24:58,480 Speaker 1: we can if we don't trust Home Lending Path, we 427 00:24:58,480 --> 00:25:01,639 Speaker 1: definitely trust IBM with this expect of the business. So 428 00:25:01,680 --> 00:25:04,439 Speaker 1: what is the sort of story of of working with 429 00:25:04,520 --> 00:25:07,200 Speaker 1: IBM on this. I mean, did you just figure out 430 00:25:07,240 --> 00:25:09,160 Speaker 1: that they had the thing you need or did they 431 00:25:09,200 --> 00:25:11,239 Speaker 1: sort of work with you to to build the thing 432 00:25:11,280 --> 00:25:16,720 Speaker 1: you need? We told them what we wanted. I think 433 00:25:16,760 --> 00:25:21,160 Speaker 1: there's a certain special relationship that we have with IBM. 434 00:25:21,240 --> 00:25:24,320 Speaker 1: As I mentioned, Steve and I are are very aggressive 435 00:25:24,400 --> 00:25:27,240 Speaker 1: of internally and externally in terms of getting things change 436 00:25:27,280 --> 00:25:30,320 Speaker 1: in this industry, especially when talk about systemic change, and 437 00:25:30,400 --> 00:25:33,879 Speaker 1: sometimes that requires you to make very big ask, you know, 438 00:25:33,960 --> 00:25:37,640 Speaker 1: swing for the fences and see what happens. And as 439 00:25:37,640 --> 00:25:40,159 Speaker 1: we found out more, as we we hired better talent, 440 00:25:40,200 --> 00:25:42,280 Speaker 1: as we understood more of what we were trying to do, 441 00:25:43,000 --> 00:25:44,640 Speaker 1: it made it a lot easier for us to really 442 00:25:44,720 --> 00:25:48,560 Speaker 1: share this vision with IBM. And then now they're able 443 00:25:48,600 --> 00:25:51,040 Speaker 1: to recommend products to say, we see you're trying to 444 00:25:51,080 --> 00:25:52,879 Speaker 1: do it this way, but maybe you want to use 445 00:25:52,880 --> 00:25:55,159 Speaker 1: our internal product and do it with this instead, And 446 00:25:55,160 --> 00:25:56,679 Speaker 1: so that makes it a lot easier for us to 447 00:25:56,680 --> 00:26:00,000 Speaker 1: try to bring artificial intelligence and blockchain to an industry 448 00:25:59,880 --> 00:26:05,399 Speaker 1: that hasn't historically accepted new technology that well. So where 449 00:26:05,440 --> 00:26:08,119 Speaker 1: are you in your journey as a company. I know 450 00:26:08,200 --> 00:26:11,280 Speaker 1: you're still sort of working on it. What can customers 451 00:26:11,320 --> 00:26:16,800 Speaker 1: do now with your product? They can get recommendations right now. 452 00:26:17,560 --> 00:26:23,400 Speaker 1: We're fully licensed Colorado, Florida and in North Carolina, so 453 00:26:23,600 --> 00:26:26,119 Speaker 1: right now, customers from those days can expect to be 454 00:26:26,119 --> 00:26:31,240 Speaker 1: connected with the lender with full guidance as to what 455 00:26:31,400 --> 00:26:35,440 Speaker 1: exactly they're getting into and what pricing expectations they ought 456 00:26:35,480 --> 00:26:39,160 Speaker 1: to be presented with. Have you heard back? I mean, 457 00:26:39,200 --> 00:26:41,879 Speaker 1: I know that this is kind of a weird question, 458 00:26:41,880 --> 00:26:45,200 Speaker 1: given that the whole point is that people can be anonymized, 459 00:26:45,280 --> 00:26:48,000 Speaker 1: but are you able to talk to your customers? Have 460 00:26:48,000 --> 00:26:50,800 Speaker 1: Have any of your customers told you about how it's 461 00:26:50,800 --> 00:26:54,760 Speaker 1: helped them. Surprisingly, a lot of our customers will reach 462 00:26:54,760 --> 00:26:56,840 Speaker 1: out to us and give us use cases we've had 463 00:26:56,920 --> 00:26:59,680 Speaker 1: local TV interviews, what they've interviewed them. Without those success 464 00:26:59,680 --> 00:27:02,600 Speaker 1: stores will have customers that will reach out to us 465 00:27:02,600 --> 00:27:04,480 Speaker 1: what challenges that they're having and hoping that we can 466 00:27:04,480 --> 00:27:07,400 Speaker 1: help them through those, even if we have to manually 467 00:27:07,560 --> 00:27:09,879 Speaker 1: connect the borrower to a lender and a state that 468 00:27:09,880 --> 00:27:12,080 Speaker 1: we don't operate, and we're more than happy to do that. 469 00:27:12,560 --> 00:27:15,080 Speaker 1: In exchange for that, they're basically helping us build out 470 00:27:15,119 --> 00:27:18,159 Speaker 1: this new process, and so that's kind of the beauty 471 00:27:18,160 --> 00:27:20,240 Speaker 1: of the system is that you know, customers are coming 472 00:27:20,280 --> 00:27:23,159 Speaker 1: in at all stages of the buying cycle. You have 473 00:27:23,320 --> 00:27:26,399 Speaker 1: some that are still renting at that day dreaming phase 474 00:27:26,400 --> 00:27:28,560 Speaker 1: where they're really trying to understand, you know, is home 475 00:27:28,640 --> 00:27:31,560 Speaker 1: ownership a feasible option for me? And you have some 476 00:27:31,640 --> 00:27:34,120 Speaker 1: that are you know, trying to test out new features 477 00:27:34,119 --> 00:27:36,919 Speaker 1: like optimal character recognition software where they're able to upload 478 00:27:36,960 --> 00:27:39,960 Speaker 1: documents and see how those documents transferred to lenders. So 479 00:27:40,320 --> 00:27:42,640 Speaker 1: I really think that is the beauty about what we're 480 00:27:42,640 --> 00:27:45,040 Speaker 1: building is that the people have helped us build it 481 00:27:45,160 --> 00:27:49,080 Speaker 1: so far. Are there any particular stories you've heard from 482 00:27:49,080 --> 00:27:53,520 Speaker 1: customers that have stayed with you? Um? Honestly, I think 483 00:27:53,560 --> 00:27:56,000 Speaker 1: the one that's most relevant to me, that sticks closest 484 00:27:56,000 --> 00:27:57,680 Speaker 1: to my heart is my mom. You know, she was 485 00:27:57,720 --> 00:28:00,560 Speaker 1: looking to try to buy another house. We were able 486 00:28:00,600 --> 00:28:02,880 Speaker 1: to get her approved for a little bit over six 487 00:28:03,200 --> 00:28:05,480 Speaker 1: D fifty thousand, which was about fifty thousand more than 488 00:28:05,520 --> 00:28:08,760 Speaker 1: what she had heard from anyone else in the area. Uh, So, 489 00:28:08,920 --> 00:28:10,439 Speaker 1: you know, we've really been excited, at least I had 490 00:28:10,480 --> 00:28:13,000 Speaker 1: really been excited about that one. That's great, you know, 491 00:28:13,240 --> 00:28:16,560 Speaker 1: to do better than your mom, right, that's the whole 492 00:28:16,600 --> 00:28:18,679 Speaker 1: reason why I built the system, so you know, So 493 00:28:18,760 --> 00:28:21,520 Speaker 1: that one really sticks closest to me is because, uh, 494 00:28:21,720 --> 00:28:23,680 Speaker 1: we've asked some users that have gone through the entire 495 00:28:23,760 --> 00:28:27,000 Speaker 1: process and have helped us go from our initial phase 496 00:28:27,040 --> 00:28:30,280 Speaker 1: and we've really been launching in phases where at first 497 00:28:30,320 --> 00:28:33,000 Speaker 1: it was more show just showing the affordability amount, like 498 00:28:33,040 --> 00:28:34,359 Speaker 1: you know, what was the amount of home that you 499 00:28:34,400 --> 00:28:37,280 Speaker 1: could afford. Now, as Steven mentioned, we're getting into this 500 00:28:37,400 --> 00:28:42,160 Speaker 1: much more interactive, uh, conversational dialogue where consumers are not 501 00:28:42,240 --> 00:28:44,360 Speaker 1: only showing kind of what they want to buy, but 502 00:28:44,480 --> 00:28:47,040 Speaker 1: also getting into kind of what their feelings are, what 503 00:28:47,320 --> 00:28:49,680 Speaker 1: is what are their sentiments that they're looking for in 504 00:28:49,800 --> 00:28:52,560 Speaker 1: a potential relationship with they lender. Uh. So we're really 505 00:28:52,560 --> 00:28:55,600 Speaker 1: excited when consumers come in and they test new features 506 00:28:55,600 --> 00:28:58,800 Speaker 1: and they say, hey, this is working, great, this isn't working, Uh, 507 00:28:58,840 --> 00:29:00,640 Speaker 1: you know what about this? And we think that's really 508 00:29:00,680 --> 00:29:03,360 Speaker 1: going to lead into our series A raise here in 509 00:29:03,360 --> 00:29:04,720 Speaker 1: the next couple of months, where we'll go out and 510 00:29:04,800 --> 00:29:07,760 Speaker 1: raise hopefully eight thiggers or more to really flush out 511 00:29:07,800 --> 00:29:10,000 Speaker 1: the features that consumers have said they wanted the most. 512 00:29:10,360 --> 00:29:14,520 Speaker 1: Is really what we're most excited about. What's your dream 513 00:29:14,560 --> 00:29:16,600 Speaker 1: for home landing Pale If you think whatever, I don't know. 514 00:29:16,680 --> 00:29:18,719 Speaker 1: Five years in the future, ten years in the future, 515 00:29:19,160 --> 00:29:21,120 Speaker 1: where are you. I want to see at least a 516 00:29:21,160 --> 00:29:24,600 Speaker 1: million people, hopefully on a million minorities, become homeowners by 517 00:29:24,680 --> 00:29:27,840 Speaker 1: utilizing our product. You know, we operate in an industry 518 00:29:27,880 --> 00:29:31,440 Speaker 1: that's very lucrative for a lot of people. Having supported 519 00:29:31,520 --> 00:29:34,120 Speaker 1: IBM will hopefully help us from a business perspective, But 520 00:29:34,840 --> 00:29:36,560 Speaker 1: I don't want us to lose sight of our social 521 00:29:36,560 --> 00:29:39,400 Speaker 1: impact goals and the things that we're really set out before, 522 00:29:39,440 --> 00:29:41,760 Speaker 1: which was to make the process more agguiable for everyone. 523 00:29:41,800 --> 00:29:44,520 Speaker 1: You know, I think if we were to be acquired 524 00:29:44,600 --> 00:29:47,040 Speaker 1: or to to do an initial public offering in five 525 00:29:47,120 --> 00:29:48,920 Speaker 1: years and we're not doing that, then for me it 526 00:29:48,920 --> 00:29:51,240 Speaker 1: would not be as as sweet as if it were 527 00:29:51,320 --> 00:29:54,000 Speaker 1: to ensure that we're actually doing stuff to close the 528 00:29:54,040 --> 00:29:57,720 Speaker 1: gap for people. Thank you guys so much for your time. 529 00:29:57,760 --> 00:30:00,520 Speaker 1: I really it was great to talk with you. Pleasure. 530 00:30:01,120 --> 00:30:11,320 Speaker 1: Thank you absolutely. Malcolm glave all here to end today's show, 531 00:30:11,560 --> 00:30:13,960 Speaker 1: I want to talk about someone who we didn't hear 532 00:30:14,000 --> 00:30:18,960 Speaker 1: from in the interview, but who we heard about, Brian's mom, 533 00:30:19,000 --> 00:30:22,200 Speaker 1: because her story really is the story of Home Lending Pall. 534 00:30:22,960 --> 00:30:25,440 Speaker 1: Remember how Brian told us that back in the odds, 535 00:30:25,760 --> 00:30:28,560 Speaker 1: his mom got that crappy mortgage, the one that left 536 00:30:28,560 --> 00:30:31,680 Speaker 1: her paying higher interest rates than she should have been paying. 537 00:30:32,440 --> 00:30:35,680 Speaker 1: That happened to a lot of people, particularly people of color. 538 00:30:36,400 --> 00:30:39,600 Speaker 1: It was that story and others like it that really 539 00:30:39,680 --> 00:30:43,240 Speaker 1: inspired Brian to team up with Stephen to build Home 540 00:30:43,320 --> 00:30:47,040 Speaker 1: Lending Pall. They wanted to fix a home lending system 541 00:30:47,320 --> 00:30:52,080 Speaker 1: that had been opaque and unfair basically forever. Most people 542 00:30:52,120 --> 00:30:55,800 Speaker 1: applying for mortgages aren't thinking about the technology that's behind 543 00:30:55,800 --> 00:30:58,840 Speaker 1: the scenes. We all just want a good mortgage with 544 00:30:58,920 --> 00:31:03,400 Speaker 1: fair terms. And because Brian and Stephen made creative use 545 00:31:03,480 --> 00:31:09,160 Speaker 1: of IBM technology using AI, blockchain and cloud to rethink 546 00:31:09,240 --> 00:31:13,240 Speaker 1: the home loan process, that is now possible for all 547 00:31:13,320 --> 00:31:19,720 Speaker 1: of us. On the next episode of Smart Talks with IBM, 548 00:31:19,760 --> 00:31:23,120 Speaker 1: as AI becomes more widespread, how do we ensure that 549 00:31:23,200 --> 00:31:27,880 Speaker 1: it is built and deployed responsibly, We talked with Pedra 550 00:31:27,960 --> 00:31:35,680 Speaker 1: Bonadira's trustworthy AI practice leader within IBM Consulting. Smart Talks 551 00:31:35,680 --> 00:31:40,360 Speaker 1: with IBM is produced by Molly Sosha, Alexandra Garraton, Royston 552 00:31:40,440 --> 00:31:45,080 Speaker 1: Preserve and Edith Rousselo with Jacob Goldstein. We're edited by 553 00:31:45,160 --> 00:31:49,560 Speaker 1: Jan Guerra. Our engineers are Jason Gambrel, Sarah Brogare and 554 00:31:49,680 --> 00:31:54,880 Speaker 1: Ben Holliday. Theme song by Gramoscope. Special thanks to Colly 555 00:31:54,960 --> 00:31:59,000 Speaker 1: Migliari and Kelly Kathy Callaghan and the eight Bar and 556 00:31:59,080 --> 00:32:03,720 Speaker 1: IBM teams, as well as the Pushkin marketing team. Smart 557 00:32:03,760 --> 00:32:06,960 Speaker 1: Talks with IBM is a production of Pushkin Industries and 558 00:32:07,120 --> 00:32:11,000 Speaker 1: I Heart Media. To find more Pushkin podcasts, listen on 559 00:32:11,040 --> 00:32:14,920 Speaker 1: the i Heart Radio app, Apple Podcasts, or wherever you 560 00:32:15,000 --> 00:32:19,720 Speaker 1: listen to podcasts. I'm Malcolm Gladwell. This is a paid 561 00:32:19,800 --> 00:32:30,960 Speaker 1: advertisement from IBM.