1 00:00:00,040 --> 00:00:05,000 Speaker 1: Hey everyone, it's Robert and Joe here. Today we've got 2 00:00:05,040 --> 00:00:06,960 Speaker 1: something a little bit different to share with you. It 3 00:00:07,080 --> 00:00:10,680 Speaker 1: is a new edition of the Smart Talks podcast series, 4 00:00:10,720 --> 00:00:14,319 Speaker 1: which is produced in partnership with IBM. This season of 5 00:00:14,360 --> 00:00:18,640 Speaker 1: Smart Talks with IBM is all about new creators, the developers, 6 00:00:19,040 --> 00:00:22,600 Speaker 1: data scientists, c t o s, and other visionaries creatively 7 00:00:22,640 --> 00:00:27,120 Speaker 1: applying technology and business to drive change. They use their 8 00:00:27,160 --> 00:00:30,640 Speaker 1: knowledge and creativity to develop better ways of working, no 9 00:00:30,720 --> 00:00:34,840 Speaker 1: matter the industry. Join hosts from your favorite Pushkin Industries 10 00:00:34,920 --> 00:00:38,560 Speaker 1: podcast as they use their expertise to deepen these conversations. 11 00:00:39,040 --> 00:00:41,800 Speaker 1: Malcolm Gladwell will guide you through this season as your 12 00:00:41,840 --> 00:00:45,000 Speaker 1: host to provide his thoughts and analysis along the way. 13 00:00:45,320 --> 00:00:48,600 Speaker 1: Look out for new episodes of Smart Talks with IBM 14 00:00:48,680 --> 00:00:52,040 Speaker 1: every month on the I Heart Radio app, Apple Podcasts, 15 00:00:52,159 --> 00:00:55,480 Speaker 1: or wherever you get your podcasts. And learn more at 16 00:00:55,520 --> 00:01:04,920 Speaker 1: IBM dot com slash smart Talks. Hello, Hello, Welcome to 17 00:01:04,959 --> 00:01:08,480 Speaker 1: a new season of Smart Talks with IBM, a podcast 18 00:01:08,520 --> 00:01:14,040 Speaker 1: from Pushkin Industries, I Heart Radio and IBM. I'm Malcolm Gladwell. 19 00:01:15,040 --> 00:01:19,959 Speaker 1: This season we're talking to new creators, the developers, data scientists, 20 00:01:20,319 --> 00:01:24,200 Speaker 1: ct o s, and other visionaries who are creatively applying 21 00:01:24,280 --> 00:01:29,360 Speaker 1: technology in business to drive change. Channeling their knowledge and expertise, 22 00:01:29,640 --> 00:01:34,560 Speaker 1: they're developing more creative and effective solutions no matter the industry. 23 00:01:35,440 --> 00:01:39,399 Speaker 1: Our guest today are Brian Young and Stephen Better, co 24 00:01:39,560 --> 00:01:43,960 Speaker 1: founders of home lending Pal. Home lending Pal is a 25 00:01:43,959 --> 00:01:48,920 Speaker 1: member of the IBM hyper Protect Accelerator, an investment readiness 26 00:01:49,040 --> 00:01:54,280 Speaker 1: and technical mentorship program that supports impact focused startups leveraging 27 00:01:54,560 --> 00:01:59,560 Speaker 1: highly sensitive data. Their story is a perfect place to 28 00:01:59,640 --> 00:02:04,920 Speaker 1: start our season. They recognized a profound problem, the horrible 29 00:02:05,040 --> 00:02:08,480 Speaker 1: process of getting a home loan, especially if you're part 30 00:02:08,520 --> 00:02:11,839 Speaker 1: of an underserved community, a process that, as you'll hear, 31 00:02:12,280 --> 00:02:16,359 Speaker 1: is not only confusing and complex, but often deeply unfair. 32 00:02:17,320 --> 00:02:20,600 Speaker 1: So Brian and Stephen teamed up to use technology to 33 00:02:20,680 --> 00:02:24,680 Speaker 1: attack that problem in a bunch of creative ways. You'll 34 00:02:24,720 --> 00:02:27,560 Speaker 1: hear how they're tapping into blockchain to make the home 35 00:02:27,600 --> 00:02:32,280 Speaker 1: loan process more transparent and fair, using AI to help 36 00:02:32,320 --> 00:02:35,400 Speaker 1: people learn how to qualify for a loan, and relying 37 00:02:35,440 --> 00:02:40,840 Speaker 1: on IBM technology to store consumers most sensitive information safely 38 00:02:41,120 --> 00:02:44,919 Speaker 1: in the cloud. Brian and Stephen talked with Jacob Goldstein, 39 00:02:45,280 --> 00:02:49,400 Speaker 1: host of the Pushkin podcast What's your problem. Jacob has 40 00:02:49,400 --> 00:02:53,200 Speaker 1: covered technology and business for over a decade, first at 41 00:02:53,200 --> 00:02:57,760 Speaker 1: The Wall Street Journal, then at MPR. Now let's get 42 00:02:57,760 --> 00:03:09,480 Speaker 1: into the interview. Let's start this like a rom com. 43 00:03:09,480 --> 00:03:11,800 Speaker 1: How did you meet each other and decided to start 44 00:03:11,800 --> 00:03:14,480 Speaker 1: a company together. Stephen was supposed to come to a 45 00:03:14,520 --> 00:03:17,120 Speaker 1: bachelor party in Miami and didn't show up, and it 46 00:03:17,200 --> 00:03:20,840 Speaker 1: broke my heart. There's more to the story than pensimbly 47 00:03:20,919 --> 00:03:24,640 Speaker 1: that one of my old employees introduced us. I've just 48 00:03:24,840 --> 00:03:27,840 Speaker 1: left Marcato. They've been acquired for one point for billion, 49 00:03:27,880 --> 00:03:30,880 Speaker 1: and I am, you know, living the Miami lifestyle. You know, 50 00:03:31,040 --> 00:03:33,120 Speaker 1: I have a condo on the water and all the 51 00:03:33,320 --> 00:03:35,840 Speaker 1: nice things that go with it. A guy named Michael 52 00:03:35,960 --> 00:03:39,160 Speaker 1: Ramsey had asked me, you know, would I help him 53 00:03:39,200 --> 00:03:41,240 Speaker 1: do mortgage lead generation? And I was like, you know, 54 00:03:41,240 --> 00:03:43,840 Speaker 1: I'm sure I'm not doing anything else? Why not? And 55 00:03:43,960 --> 00:03:48,120 Speaker 1: I meet Stephen. He was in North Carolina. I left 56 00:03:48,120 --> 00:03:52,520 Speaker 1: a pretty fruitful career in banking. I was an underwriter. 57 00:03:52,800 --> 00:03:55,440 Speaker 1: Underwriting loans mean it's basically deciding who should get a 58 00:03:55,440 --> 00:03:58,880 Speaker 1: loan and at what interest rate? Right, absolutely due diligence, right, 59 00:03:59,280 --> 00:04:03,680 Speaker 1: which is understanding whether or not this particular individual has 60 00:04:04,040 --> 00:04:08,920 Speaker 1: the wherewithal to afford the mortgage, also the credit risks 61 00:04:08,960 --> 00:04:14,680 Speaker 1: that individual presents. But there was this disconnect in that 62 00:04:14,720 --> 00:04:18,720 Speaker 1: process where you have hidden action taking place on one 63 00:04:18,720 --> 00:04:21,800 Speaker 1: side of the transaction, while you have another side of 64 00:04:21,839 --> 00:04:25,360 Speaker 1: the transaction that that tends to hide information. And just 65 00:04:25,440 --> 00:04:29,000 Speaker 1: to be clear, it's the borrower who hides information and 66 00:04:29,040 --> 00:04:33,279 Speaker 1: the bank that hides the action the lender in most cases, 67 00:04:33,440 --> 00:04:37,599 Speaker 1: but it's usually both sides of the negotia. Everybody's hiding 68 00:04:37,640 --> 00:04:41,520 Speaker 1: stuff from everybody else's absolutely, and it's like sort of 69 00:04:41,560 --> 00:04:45,160 Speaker 1: inadvertent as well too. Right in that process, certain things 70 00:04:45,160 --> 00:04:47,719 Speaker 1: fall through the cracks, and you know, falling through the 71 00:04:47,720 --> 00:04:52,400 Speaker 1: cracks means weaks without notification from a bar's perspective as 72 00:04:52,400 --> 00:04:56,119 Speaker 1: to whether or not you know this deal is moving forward. Okay, 73 00:04:56,160 --> 00:04:58,520 Speaker 1: So so the problem is a lack of information on 74 00:04:58,560 --> 00:05:01,600 Speaker 1: both sides, and that winds up leading to bad outcomes. 75 00:05:01,640 --> 00:05:04,760 Speaker 1: It winds up leading to long delays that are frustrating 76 00:05:04,880 --> 00:05:09,040 Speaker 1: or scary for the for the borrower. Yes, a lot 77 00:05:09,040 --> 00:05:12,320 Speaker 1: of consumers just don't have anywhere to go. If you 78 00:05:12,360 --> 00:05:15,080 Speaker 1: go online, everything is too broad engineering, especially if you 79 00:05:15,120 --> 00:05:17,360 Speaker 1: know you're not ready to buy at that moment. Uh, 80 00:05:17,360 --> 00:05:19,160 Speaker 1: if you talk to a lender or relatory, if you're 81 00:05:19,200 --> 00:05:21,440 Speaker 1: not ready to buy at that moment, there they'll help you, 82 00:05:21,480 --> 00:05:23,240 Speaker 1: but it's not the same level of help. But you're 83 00:05:23,240 --> 00:05:25,720 Speaker 1: not going to get that same level of support over months, 84 00:05:25,720 --> 00:05:27,839 Speaker 1: because you know, buying housesn't like buying a piece of 85 00:05:27,880 --> 00:05:30,920 Speaker 1: candy online. And so we really looked at, Okay, well, 86 00:05:30,960 --> 00:05:34,000 Speaker 1: how can we give people this safe environment to go 87 00:05:34,120 --> 00:05:37,560 Speaker 1: explore and understand when homeownership could look like for them 88 00:05:37,600 --> 00:05:39,720 Speaker 1: based on their personal information. And that's kind of when 89 00:05:39,760 --> 00:05:42,280 Speaker 1: I reach back out to Stephen around August of two 90 00:05:42,279 --> 00:05:44,760 Speaker 1: thousand seventeen and said, hey, you know, we need to 91 00:05:44,760 --> 00:05:48,040 Speaker 1: do this together. You understand the back inside from a 92 00:05:48,120 --> 00:05:52,240 Speaker 1: lender underwritish perspective, and I understand the plight of the consumers, 93 00:05:52,240 --> 00:05:53,960 Speaker 1: and if we come together, this could be something that 94 00:05:54,000 --> 00:05:57,440 Speaker 1: could be really unique. A capitalist solution to a social 95 00:05:57,480 --> 00:05:59,920 Speaker 1: challenge is probably the best way to put it. So Stephen, 96 00:06:00,000 --> 00:06:02,839 Speaker 1: you're sort of coming from the banking side, and Brian 97 00:06:02,920 --> 00:06:07,240 Speaker 1: you're sort of coming from the tech side. Absolutely, what 98 00:06:07,400 --> 00:06:09,599 Speaker 1: exactly is the problem that you guys are trying to 99 00:06:09,640 --> 00:06:13,360 Speaker 1: solve when you start this company and its simple ast essences, 100 00:06:13,480 --> 00:06:18,680 Speaker 1: data democratization, the ability to take complex information and simplify 101 00:06:18,800 --> 00:06:21,600 Speaker 1: so that someone that isn't an expert like Stephen can 102 00:06:21,720 --> 00:06:25,160 Speaker 1: understand what's going on. And in this case specifically, what 103 00:06:25,360 --> 00:06:28,960 Speaker 1: is the data that you're trying to democratize underwriting data, 104 00:06:29,080 --> 00:06:31,919 Speaker 1: so the decision or the data that is utilized to 105 00:06:32,000 --> 00:06:35,080 Speaker 1: determine whether or not you are approved or declined for 106 00:06:35,120 --> 00:06:38,440 Speaker 1: a home loan. So right now, if I go apply 107 00:06:38,560 --> 00:06:42,080 Speaker 1: for a loan, they approve me or they decline me. 108 00:06:42,960 --> 00:06:46,200 Speaker 1: But do I know why not? Really? I mean, you 109 00:06:46,240 --> 00:06:48,680 Speaker 1: get a letter of an adverse letter, but it's still 110 00:06:48,800 --> 00:06:51,000 Speaker 1: very broad and generic. It doesn't really tell you what 111 00:06:51,080 --> 00:06:53,760 Speaker 1: to focus on next, but you do have a very 112 00:06:53,800 --> 00:06:57,440 Speaker 1: high level sense of why your decline. Yeah, there's no 113 00:06:57,520 --> 00:07:01,840 Speaker 1: true guidance from that point of rejection, right there's no 114 00:07:02,279 --> 00:07:05,559 Speaker 1: fundamental understanding as to what could I have done better? 115 00:07:06,240 --> 00:07:09,200 Speaker 1: And that's really what sets this platform apart, and also 116 00:07:09,400 --> 00:07:14,320 Speaker 1: why it's important how we're sort of reframing this data workflow. 117 00:07:14,720 --> 00:07:16,360 Speaker 1: I want to get into the details of that. But 118 00:07:16,440 --> 00:07:19,400 Speaker 1: just as we sort of understand the problem a little 119 00:07:19,400 --> 00:07:21,200 Speaker 1: bit more, I mean, one piece of it that we 120 00:07:21,280 --> 00:07:26,040 Speaker 1: haven't talked about is is race and the home ownership gap. 121 00:07:26,120 --> 00:07:27,840 Speaker 1: Can you guys talk a little bit about that and 122 00:07:27,880 --> 00:07:32,400 Speaker 1: how it fits with with what you're trying to do. Yeah, 123 00:07:32,440 --> 00:07:35,640 Speaker 1: I mean, the home ownership gap, at least for African 124 00:07:35,720 --> 00:07:39,160 Speaker 1: Americans is larger now than it was fifty years ago 125 00:07:39,200 --> 00:07:42,960 Speaker 1: and segregation was legal, which is quite saddening. But it's 126 00:07:42,960 --> 00:07:45,960 Speaker 1: not just African Americans. And when you look at declines, 127 00:07:46,520 --> 00:07:50,000 Speaker 1: whether you are a woman, whether you are a minority, 128 00:07:50,440 --> 00:07:52,040 Speaker 1: whether you're a part of the l p G t 129 00:07:52,160 --> 00:07:55,360 Speaker 1: Q plus community, it shows that there's a higher level 130 00:07:55,440 --> 00:07:58,280 Speaker 1: of declines for these communities than there are for ford 131 00:07:58,320 --> 00:08:00,880 Speaker 1: or white males. So, you know, and our perspective, there 132 00:08:00,880 --> 00:08:03,040 Speaker 1: has to be a lot that needs to be done 133 00:08:03,040 --> 00:08:06,560 Speaker 1: in terms of resetting reconfiguring the system to make it 134 00:08:06,600 --> 00:08:09,520 Speaker 1: more fair and equitable for all. So, So, if I 135 00:08:09,600 --> 00:08:14,080 Speaker 1: understand you correctly, you're saying, basically, in the current system, 136 00:08:14,240 --> 00:08:17,760 Speaker 1: white men have an easier time getting a mortgage than 137 00:08:17,840 --> 00:08:21,119 Speaker 1: anybody else. Well, you said it, I'll just agree with it. 138 00:08:21,800 --> 00:08:23,920 Speaker 1: I think you said it. I think if I understood 139 00:08:23,920 --> 00:08:27,240 Speaker 1: you're correctly, yeah, that that is what the data shows us, 140 00:08:27,280 --> 00:08:29,080 Speaker 1: and it's not just my perspective. That's what the data 141 00:08:29,120 --> 00:08:32,880 Speaker 1: shows is So and so how are you trying to 142 00:08:33,440 --> 00:08:37,360 Speaker 1: help fix that problem by turning everybody in the corn, 143 00:08:38,679 --> 00:08:41,560 Speaker 1: by turning everybody into corn like it? What do you 144 00:08:41,559 --> 00:08:45,079 Speaker 1: mean by that? Through the power of math right cryptography specifically, 145 00:08:45,600 --> 00:08:47,840 Speaker 1: we are able to make everyone look the same and 146 00:08:48,320 --> 00:08:53,920 Speaker 1: the underwriter just simply understands the fundamental attributes that ought 147 00:08:54,000 --> 00:08:57,560 Speaker 1: to drive that approval disapproval decision. Right in order to 148 00:08:57,640 --> 00:09:01,319 Speaker 1: help us and also to help our government understand where 149 00:09:01,320 --> 00:09:05,840 Speaker 1: these biases are are coming from, our lenders are required 150 00:09:05,880 --> 00:09:08,880 Speaker 1: to ask you what your raise, what your sex, even 151 00:09:08,880 --> 00:09:11,240 Speaker 1: your age, right Like, all of this comes with with 152 00:09:11,320 --> 00:09:17,400 Speaker 1: this application scenario. But does this information inadvertingly create the bias? 153 00:09:18,160 --> 00:09:21,440 Speaker 1: Can we make everyone look the same and start to 154 00:09:21,559 --> 00:09:26,320 Speaker 1: remove or better identify where these issues are sort of 155 00:09:26,360 --> 00:09:31,120 Speaker 1: coming from. So you're trying to use technology to blind 156 00:09:31,200 --> 00:09:33,960 Speaker 1: all the decision makers in the home loan process to 157 00:09:34,559 --> 00:09:40,880 Speaker 1: race ethnicity, specifically blockchain. There are three big tech ideas 158 00:09:40,920 --> 00:09:44,560 Speaker 1: behind home lending pal at least three that we're going 159 00:09:44,600 --> 00:09:47,720 Speaker 1: to talk about today on the show, and blockchain is 160 00:09:47,920 --> 00:09:50,760 Speaker 1: big tech idea Number one. You may have heard of 161 00:09:50,760 --> 00:09:55,720 Speaker 1: blockchain because it's the key idea behind cryptocurrency, but the 162 00:09:55,760 --> 00:09:59,320 Speaker 1: idea of blockchain is bigger than just digital money and 163 00:09:59,480 --> 00:10:02,079 Speaker 1: much more than just a new way to store information 164 00:10:02,120 --> 00:10:07,240 Speaker 1: on the Internet. Blockchain is a shared, immutable ledger that 165 00:10:07,320 --> 00:10:12,400 Speaker 1: facilitates the process of recording transactions and tracking assets in 166 00:10:12,440 --> 00:10:16,960 Speaker 1: a business network. Brian and Stephen want to use blockchain 167 00:10:17,040 --> 00:10:19,760 Speaker 1: to gather up the information on race and gender that's 168 00:10:19,760 --> 00:10:23,400 Speaker 1: required by law without showing it to the lenders making 169 00:10:23,400 --> 00:10:27,960 Speaker 1: the decisions about who gets alone. Our argument or our 170 00:10:28,559 --> 00:10:32,040 Speaker 1: thesis is that with the leverage of a mutable ledger 171 00:10:32,160 --> 00:10:34,560 Speaker 1: such as blockchain, are able to still collect the information 172 00:10:34,559 --> 00:10:37,719 Speaker 1: that is necessary for the Home Mortgage Disclosure Act or 173 00:10:37,800 --> 00:10:40,920 Speaker 1: HUMDA as Stephen was referring to. But then with a 174 00:10:41,000 --> 00:10:43,480 Speaker 1: smart contract, you don't have to release that information, so 175 00:10:43,600 --> 00:10:46,440 Speaker 1: after the decision, the approval of decline is made for 176 00:10:46,480 --> 00:10:49,360 Speaker 1: the consumer. So you have this big idea for what 177 00:10:49,400 --> 00:10:50,760 Speaker 1: you want to do as a business, which you want 178 00:10:50,760 --> 00:10:54,200 Speaker 1: to do socially, but how do you make creative use 179 00:10:54,240 --> 00:10:56,840 Speaker 1: of technology to do the thing you want to do 180 00:10:56,920 --> 00:10:59,600 Speaker 1: to make it real? You know, we're trying to build 181 00:10:59,600 --> 00:11:02,320 Speaker 1: something that hasn't been done in the mortgage industry, especially 182 00:11:02,320 --> 00:11:04,880 Speaker 1: when you talk about artificial intelligence and a virtual assistant. 183 00:11:04,920 --> 00:11:07,200 Speaker 1: Most people think of that it's just a a one 184 00:11:07,240 --> 00:11:09,640 Speaker 1: way street. You know, we are trying to build this 185 00:11:09,920 --> 00:11:13,839 Speaker 1: human like interaction where it is able to not only understand, 186 00:11:13,960 --> 00:11:17,080 Speaker 1: but to respond and then to leverage those responses and 187 00:11:17,600 --> 00:11:21,280 Speaker 1: create a roadmap towards allowing you to achieve your goals, 188 00:11:21,360 --> 00:11:24,079 Speaker 1: which is probably one of the most creative things that 189 00:11:24,200 --> 00:11:27,440 Speaker 1: I've ever done personally. But it also requires you to 190 00:11:27,559 --> 00:11:30,280 Speaker 1: be willing to accept constructive criticism from the people that 191 00:11:30,320 --> 00:11:33,280 Speaker 1: are going to be using get up front and a 192 00:11:33,360 --> 00:11:35,040 Speaker 1: lot of what we're doing is really trying to find 193 00:11:35,080 --> 00:11:37,640 Speaker 1: creative ways just to get them involved in that conversation 194 00:11:37,679 --> 00:11:39,800 Speaker 1: to say that, hey, you know, we are trying to 195 00:11:39,840 --> 00:11:42,520 Speaker 1: build this to help you. Right now, there's about twenty 196 00:11:42,520 --> 00:11:45,800 Speaker 1: one million mortgage reading millennials to day that are qualified 197 00:11:45,840 --> 00:11:48,319 Speaker 1: to buy a loan, even though they're not trying. They 198 00:11:48,360 --> 00:11:50,920 Speaker 1: just don't know. We're trying to bring greater trust and 199 00:11:50,960 --> 00:11:54,839 Speaker 1: transparency to this process. Yeah. I guess from my perspective, 200 00:11:54,880 --> 00:11:59,440 Speaker 1: beyond just simply understanding the technology and what it's able 201 00:11:59,480 --> 00:12:02,839 Speaker 1: to do, I think it takes the will to go 202 00:12:02,880 --> 00:12:07,960 Speaker 1: ahead and take on that complexity to try something new. Uh, 203 00:12:08,240 --> 00:12:13,120 Speaker 1: we were challenged constantly with why not a simpler solution? Right, 204 00:12:13,679 --> 00:12:17,120 Speaker 1: But in reality, the problem is much more complicated than 205 00:12:17,679 --> 00:12:21,480 Speaker 1: the simplicity these forces wanted to bring into the table. 206 00:12:22,360 --> 00:12:24,480 Speaker 1: You have to have vision, you have to have a 207 00:12:24,520 --> 00:12:28,720 Speaker 1: desire to want to make fundamental change. Yeah, new tech 208 00:12:28,760 --> 00:12:32,200 Speaker 1: built on old, broken processes doesn't allow for systemic change. 209 00:12:32,280 --> 00:12:35,079 Speaker 1: You know, you have to try to find ways to 210 00:12:36,040 --> 00:12:37,760 Speaker 1: not only just to make it easier for people to 211 00:12:37,800 --> 00:12:40,320 Speaker 1: connect to lenders, but at the core of what we 212 00:12:40,320 --> 00:12:42,280 Speaker 1: were trying to build, we really wanted to address the 213 00:12:42,360 --> 00:12:45,520 Speaker 1: systemic issues in the home buying process, and that required 214 00:12:45,600 --> 00:12:48,040 Speaker 1: us to try something different basically, and I think that's 215 00:12:48,080 --> 00:12:50,120 Speaker 1: the most creative thing you can do in an industry 216 00:12:50,120 --> 00:12:53,840 Speaker 1: that typically a Stephen mentioned wanted us to do as simpler. Yeah. 217 00:12:54,320 --> 00:12:56,240 Speaker 1: So one of the ideas you guys have is that 218 00:12:57,200 --> 00:13:03,200 Speaker 1: transparency can help redo bias. So in what way are 219 00:13:03,200 --> 00:13:07,720 Speaker 1: you using technology to bring more transparency to the home 220 00:13:07,760 --> 00:13:12,200 Speaker 1: buying process? When we speak of transparency, when we speak 221 00:13:12,360 --> 00:13:15,679 Speaker 1: of trust, where we're really talking about is just the 222 00:13:15,920 --> 00:13:21,520 Speaker 1: natural features of the blockchain. Right. It's transparent because all 223 00:13:21,600 --> 00:13:26,559 Speaker 1: participants within this framework have access to this decentralized ledger. 224 00:13:27,120 --> 00:13:31,440 Speaker 1: So we are all seeing how these pieces are sort 225 00:13:31,480 --> 00:13:35,040 Speaker 1: of moving. Right, we're playing poker with our cards facing 226 00:13:35,160 --> 00:13:39,559 Speaker 1: up when we're speaking to trust, right, we're speaking of 227 00:13:39,600 --> 00:13:43,679 Speaker 1: the mutability of this information, knowing that if an action 228 00:13:43,800 --> 00:13:46,679 Speaker 1: is taken, it's there on the ledger and we can't 229 00:13:46,760 --> 00:13:52,880 Speaker 1: just simply remove it. So these features lead to this 230 00:13:53,360 --> 00:13:58,839 Speaker 1: forceful curing of certain biases that tends to form within 231 00:13:59,000 --> 00:14:01,920 Speaker 1: certain systems. Um we're not saying that we're going to 232 00:14:02,080 --> 00:14:05,960 Speaker 1: remove all bias, but what we're saying is that we 233 00:14:06,000 --> 00:14:08,360 Speaker 1: feel very confident that we'll be able to reduce it 234 00:14:08,840 --> 00:14:15,160 Speaker 1: significantly without regulatory reinforcement by the simple nature of this 235 00:14:15,240 --> 00:14:20,120 Speaker 1: technology stack that we're developing. So was there some moment 236 00:14:20,280 --> 00:14:23,800 Speaker 1: when you guys had the like lightbulb, the a high 237 00:14:23,880 --> 00:14:27,560 Speaker 1: idea that you could do this. The moment that made 238 00:14:27,560 --> 00:14:30,040 Speaker 1: me realize that this was doable was when our first 239 00:14:30,080 --> 00:14:31,960 Speaker 1: group of lenders invested. There was a group called the 240 00:14:31,960 --> 00:14:35,720 Speaker 1: Mortgage Collaborative. They are a collection of about three hundred 241 00:14:36,800 --> 00:14:39,840 Speaker 1: five lenders I believe across the country. They represent about 242 00:14:40,720 --> 00:14:44,120 Speaker 1: overall originations that happened in the US. When they kind 243 00:14:44,120 --> 00:14:45,800 Speaker 1: of stepped in and we're like, hey, you know, we're 244 00:14:45,840 --> 00:14:49,120 Speaker 1: gonna lead her your development before your series A. We're 245 00:14:49,120 --> 00:14:50,480 Speaker 1: gonna try to help you there. I think that was 246 00:14:50,520 --> 00:14:53,160 Speaker 1: the moment for for me. And then we had shortly 247 00:14:53,200 --> 00:14:55,440 Speaker 1: after that. Joining that round was a group called Kino 248 00:14:55,520 --> 00:14:58,360 Speaker 1: Mutual or CMfg Adventures their discovery fund, and they are 249 00:14:58,440 --> 00:15:01,200 Speaker 1: the one of the largest selections of credit means in 250 00:15:01,200 --> 00:15:03,720 Speaker 1: the industry. So, you know, typically you have an issue 251 00:15:03,760 --> 00:15:06,640 Speaker 1: where you know, consumers feel like there's a problem that's 252 00:15:06,640 --> 00:15:09,040 Speaker 1: not truly being solved. But to see that lenders were 253 00:15:09,080 --> 00:15:11,360 Speaker 1: looking to try to find solutions like ours, I think 254 00:15:11,400 --> 00:15:13,480 Speaker 1: that was the AHA moment for me. They said, hey, 255 00:15:13,760 --> 00:15:15,800 Speaker 1: you know this could be feasible for us, that the 256 00:15:15,800 --> 00:15:19,360 Speaker 1: people who will actually have to work with you want 257 00:15:19,400 --> 00:15:23,920 Speaker 1: to help you. Like, that's exactly great, but just tell 258 00:15:23,960 --> 00:15:27,680 Speaker 1: me how will it work? Like, walk me through. I'm 259 00:15:27,760 --> 00:15:31,200 Speaker 1: an ordinary person. I want to get alone. I come 260 00:15:31,240 --> 00:15:34,720 Speaker 1: to home lending Pal. What happens when when you're fully 261 00:15:34,840 --> 00:15:38,280 Speaker 1: you know, fully up and running. How's it gonna work? Yeah, 262 00:15:38,400 --> 00:15:40,880 Speaker 1: So you will spend about five minutes going through our 263 00:15:40,880 --> 00:15:44,000 Speaker 1: on boarding process where you're connecting your online bank accounts, 264 00:15:44,040 --> 00:15:47,640 Speaker 1: you're authorizing a soft fco pool. There's a credit report 265 00:15:47,680 --> 00:15:50,760 Speaker 1: basically a credit report you're here. Most people don't realize. So, 266 00:15:50,920 --> 00:15:54,120 Speaker 1: so lenders are utilizing your FICO scores and most of 267 00:15:54,120 --> 00:15:55,840 Speaker 1: the places online that you're able to go to our 268 00:15:55,880 --> 00:15:58,400 Speaker 1: showing advantage scores. So that's kind of the first level 269 00:15:58,400 --> 00:16:01,680 Speaker 1: of disconnect and so we're solving for that first. So 270 00:16:01,720 --> 00:16:05,360 Speaker 1: you go through that process and then after you signed up, 271 00:16:05,720 --> 00:16:10,040 Speaker 1: our virtual assistant keV begins doing his work. He's analyzing 272 00:16:10,080 --> 00:16:13,960 Speaker 1: your profile. Uh. He's really a geared towards helping you 273 00:16:14,040 --> 00:16:18,000 Speaker 1: understand really three or four critical elements you know, one, uh, 274 00:16:18,080 --> 00:16:22,680 Speaker 1: your likelihood for success or approval to some financial modeling 275 00:16:22,680 --> 00:16:24,800 Speaker 1: and forecasts and give you a better understanding of when 276 00:16:24,800 --> 00:16:28,120 Speaker 1: you should begin the process to apply for for a home. 277 00:16:28,160 --> 00:16:29,960 Speaker 1: So how long will it take you to become a 278 00:16:29,960 --> 00:16:33,240 Speaker 1: homeowner or to close on the home? Three the best 279 00:16:33,280 --> 00:16:36,320 Speaker 1: loan product for you and then for the lenders within 280 00:16:36,360 --> 00:16:38,840 Speaker 1: our ecosystem that present the best chance of success with 281 00:16:38,920 --> 00:16:43,239 Speaker 1: them as well. So, so you mentioned a virtual advisor, 282 00:16:43,400 --> 00:16:47,920 Speaker 1: keV virtual meaning it's not a guy named keV right, 283 00:16:49,160 --> 00:16:51,920 Speaker 1: It's it's named after one of my my good friends 284 00:16:51,960 --> 00:16:54,480 Speaker 1: from college that passed from a rare form of germ 285 00:16:54,520 --> 00:16:57,360 Speaker 1: sale cancer. It's probably one of the most helpful, friendly 286 00:16:57,400 --> 00:16:59,240 Speaker 1: people that you've ever met, and it didn't matter who 287 00:16:59,280 --> 00:17:02,000 Speaker 1: you were. Uh So we really wanted to encompass his 288 00:17:02,120 --> 00:17:08,600 Speaker 1: personality into the solution itself. But yes, keV, it becomes 289 00:17:08,640 --> 00:17:10,680 Speaker 1: a friend of pal. You know, so even if you're 290 00:17:10,720 --> 00:17:13,280 Speaker 1: not ready to buy, he just doesn't pass you off 291 00:17:13,320 --> 00:17:15,480 Speaker 1: and say, hey, I'm not going to help. It really 292 00:17:15,520 --> 00:17:18,480 Speaker 1: analyzes your your profile and begins to create a path 293 00:17:19,000 --> 00:17:22,879 Speaker 1: that you can follow to become a homeowner. We have 294 00:17:23,000 --> 00:17:27,320 Speaker 1: arrived at big tech idea number two. keV the Virtual 295 00:17:27,359 --> 00:17:33,359 Speaker 1: Assistant is built using powerful artificial intelligence tools. The AI 296 00:17:33,520 --> 00:17:37,840 Speaker 1: takes the potential homebuyers information and runs it through algorithms 297 00:17:38,040 --> 00:17:40,480 Speaker 1: that tell you things like how likely you are to 298 00:17:40,480 --> 00:17:43,400 Speaker 1: get alone, and what loan makes the most sense for you, 299 00:17:43,800 --> 00:17:46,359 Speaker 1: and how long the whole process is likely to take. 300 00:17:47,320 --> 00:17:50,160 Speaker 1: You can ask keV questions and it'll give you answers. 301 00:17:50,600 --> 00:17:54,560 Speaker 1: But keV is more than your average responder chatbot. It 302 00:17:54,680 --> 00:17:58,919 Speaker 1: speaks conversationally, It knows who you are, understands your needs, 303 00:17:59,280 --> 00:18:03,040 Speaker 1: and helps be on just providing a frequently asked questions link. 304 00:18:03,640 --> 00:18:05,639 Speaker 1: Brian says he thinks a lot of people might be 305 00:18:05,680 --> 00:18:09,680 Speaker 1: more comfortable talking with an AI powered virtual assistant, then 306 00:18:09,720 --> 00:18:13,080 Speaker 1: with a human loan officer at a bank. I think 307 00:18:13,080 --> 00:18:16,160 Speaker 1: it really solves a cultural problem there. There are cultural 308 00:18:16,200 --> 00:18:19,919 Speaker 1: barriers that prevent different segments from becoming homeowners or at 309 00:18:19,960 --> 00:18:22,960 Speaker 1: least impact they're buying decisions in terms of how they 310 00:18:22,960 --> 00:18:26,000 Speaker 1: explore homeownership. So the first part is to try to 311 00:18:26,119 --> 00:18:28,720 Speaker 1: use this virtual assistant just to make them feel comfortable 312 00:18:29,200 --> 00:18:31,800 Speaker 1: getting into the process of what homeownership could look like. 313 00:18:31,840 --> 00:18:34,480 Speaker 1: And then from there, uh, it is about preparing them, 314 00:18:34,480 --> 00:18:37,720 Speaker 1: getting them better qualify so that once they are ready 315 00:18:37,760 --> 00:18:39,960 Speaker 1: to say, hey, I want to come home owner, I 316 00:18:40,040 --> 00:18:43,880 Speaker 1: found the house that I love, allowing that transaction, that 317 00:18:44,280 --> 00:18:46,879 Speaker 1: process to be a lot smoother and easier through the 318 00:18:46,960 --> 00:18:50,119 Speaker 1: use of blockchain. Basically, so when you say cultural, I 319 00:18:50,119 --> 00:18:54,600 Speaker 1: mean does that include in part race and ethnicity, people 320 00:18:54,600 --> 00:18:59,800 Speaker 1: who have traditionally been excluded from the banking sector, from housing. 321 00:19:00,000 --> 00:19:02,440 Speaker 1: I mean it's the dream that sort of AI can 322 00:19:02,480 --> 00:19:06,600 Speaker 1: help people who have been excluded become more included. Yes, 323 00:19:07,000 --> 00:19:10,960 Speaker 1: most white people have resources. They have other friends and 324 00:19:11,040 --> 00:19:15,000 Speaker 1: family who have gone through this process successfully multiple times 325 00:19:15,000 --> 00:19:18,080 Speaker 1: as opposed to just the one time. Within our communities 326 00:19:18,160 --> 00:19:21,199 Speaker 1: is difficult just to find the one person that you 327 00:19:21,240 --> 00:19:24,600 Speaker 1: can discuss this process with, and most of the time 328 00:19:24,720 --> 00:19:28,199 Speaker 1: that one person has gone through a negative experience in 329 00:19:28,320 --> 00:19:31,639 Speaker 1: that right. Brian's parents have experienced difficulty in this instry. 330 00:19:31,720 --> 00:19:35,920 Speaker 1: My parents have experienced difficulty in this process too. Isn't 331 00:19:35,960 --> 00:19:39,520 Speaker 1: until you get to our generation where you have family 332 00:19:39,520 --> 00:19:42,240 Speaker 1: members that have gone through this process multiple times and 333 00:19:42,480 --> 00:19:46,600 Speaker 1: have been successful. So when we speak to keV being 334 00:19:46,800 --> 00:19:51,879 Speaker 1: culturally relevant, it's because keV is there to provide you 335 00:19:52,080 --> 00:19:58,719 Speaker 1: accurate support that historically hasn't been available to these marginalized groups. Stephen, 336 00:19:58,720 --> 00:20:03,240 Speaker 1: you mentioned your own families, your and Brian's families experience 337 00:20:03,320 --> 00:20:06,200 Speaker 1: with getting home loans with the banking system. Do you 338 00:20:06,240 --> 00:20:09,120 Speaker 1: guys mind just talking about that specifically? What have been 339 00:20:09,160 --> 00:20:15,320 Speaker 1: your family's experiences with getting loan? Yeah? Yeah, I mean 340 00:20:15,680 --> 00:20:18,239 Speaker 1: back in the subprime mortgage crisis, and you know, my 341 00:20:18,280 --> 00:20:20,200 Speaker 1: mom nearly lost her dream home that I bought for. 342 00:20:20,880 --> 00:20:22,880 Speaker 1: It was primarily because we were in an arm even 343 00:20:22,880 --> 00:20:24,440 Speaker 1: though we should have been in a v A loan 344 00:20:24,480 --> 00:20:26,960 Speaker 1: because she has a military veteran and an arm an 345 00:20:27,000 --> 00:20:31,080 Speaker 1: adjustable rate loans that was way worse than the mortgages. 346 00:20:31,119 --> 00:20:32,760 Speaker 1: It was way worse. I mean, you know it started 347 00:20:32,760 --> 00:20:34,760 Speaker 1: out better just because you paid less, but once that 348 00:20:34,840 --> 00:20:37,280 Speaker 1: interest rate flips, it becomes way worse if you're not 349 00:20:37,320 --> 00:20:40,320 Speaker 1: prepared for it. And I think you know again, when 350 00:20:40,320 --> 00:20:42,880 Speaker 1: when we talk about these cultural factors, there's really five 351 00:20:42,960 --> 00:20:45,600 Speaker 1: that you deal with. There's there's cultural itself. So things 352 00:20:45,680 --> 00:20:49,240 Speaker 1: like the subprime mortage crisis, where African Americans are hurt 353 00:20:49,320 --> 00:20:52,080 Speaker 1: the most coming out of that, you have red lining, 354 00:20:52,080 --> 00:20:56,000 Speaker 1: reverse redlining, etcetera. Red Lining is basically the history of 355 00:20:56,760 --> 00:21:01,080 Speaker 1: lenders not making loans to people in predominantly black neighborhoods. 356 00:21:01,119 --> 00:21:04,560 Speaker 1: Essential exactly, we're picking which areas they will lend to 357 00:21:04,720 --> 00:21:08,520 Speaker 1: specific groups, yes, and those areas were predominantly white historically 358 00:21:08,520 --> 00:21:12,199 Speaker 1: and predominantly white yes. So you have those elements. You 359 00:21:12,240 --> 00:21:15,600 Speaker 1: have the economic elements where there's this concept of as 360 00:21:15,680 --> 00:21:19,760 Speaker 1: just being unattainable for us. You have the psychological elements 361 00:21:19,800 --> 00:21:22,879 Speaker 1: of being misunderstood thinking that the only way I can 362 00:21:22,920 --> 00:21:25,440 Speaker 1: buy a home is having down to put down towards 363 00:21:25,440 --> 00:21:28,800 Speaker 1: of down payment, and that's just not true. So our 364 00:21:29,359 --> 00:21:32,440 Speaker 1: ultimate objective is just really to make that more attainable 365 00:21:32,480 --> 00:21:34,159 Speaker 1: for for everyone, and it's really for all load of 366 00:21:34,240 --> 00:21:37,720 Speaker 1: modern income bar hours these days, just because with rates increasing, 367 00:21:37,760 --> 00:21:41,080 Speaker 1: with the supply shortages that we have, you know, homeownership 368 00:21:41,119 --> 00:21:42,840 Speaker 1: is really going to become a lot more difficult for 369 00:21:43,600 --> 00:21:46,040 Speaker 1: a lot of people, regardless of their age, sex, and race. 370 00:21:47,560 --> 00:21:49,600 Speaker 1: So you have this industry that suffers from a lack 371 00:21:49,640 --> 00:21:53,280 Speaker 1: of transparency, from historical bias in terms of race and gender. 372 00:21:53,800 --> 00:21:56,480 Speaker 1: You start this technology driven company to try and fix 373 00:21:56,520 --> 00:21:58,760 Speaker 1: those things. As you're building the company, how do you 374 00:21:58,800 --> 00:22:03,080 Speaker 1: come to work with I b m um our need 375 00:22:03,119 --> 00:22:07,520 Speaker 1: for data protection and security? So you're talking about digitizing documents, 376 00:22:07,560 --> 00:22:13,560 Speaker 1: digitizing information to allow greater access to underserved underrepresented groups. 377 00:22:14,240 --> 00:22:18,440 Speaker 1: And IBM had their hyper Protect Accelerator which was entirely 378 00:22:18,480 --> 00:22:22,080 Speaker 1: focused on that, taking small startups like ours and allowing 379 00:22:22,119 --> 00:22:25,879 Speaker 1: them to basically run the pilates that we ran without 380 00:22:25,920 --> 00:22:29,199 Speaker 1: having to worry about people's information getting stolen in essence, 381 00:22:29,680 --> 00:22:31,960 Speaker 1: and then Steve and I were just very aggressive in 382 00:22:32,080 --> 00:22:36,000 Speaker 1: terms of just reaching out to different vps, different executives 383 00:22:36,000 --> 00:22:37,879 Speaker 1: at IBM, kind of saying, you know, here's what we 384 00:22:37,920 --> 00:22:40,520 Speaker 1: want to do, here's what we need, will you help us? 385 00:22:41,160 --> 00:22:44,960 Speaker 1: And being in an industry that is so regulated, it 386 00:22:45,040 --> 00:22:47,199 Speaker 1: helped us really get to that door. Just because you know, 387 00:22:47,520 --> 00:22:51,719 Speaker 1: every bank has a vendor on boarding process that requires 388 00:22:51,760 --> 00:22:55,160 Speaker 1: a very high level of data security to even work 389 00:22:55,160 --> 00:23:00,280 Speaker 1: with them. In in in essence, here's the third big tech 390 00:23:00,359 --> 00:23:04,439 Speaker 1: idea in the Home Lending Peal story, protecting data in 391 00:23:04,480 --> 00:23:07,760 Speaker 1: the cloud. Think about the problem this one is solving. 392 00:23:08,200 --> 00:23:11,119 Speaker 1: Brian and Stephen have this little startup. They need to 393 00:23:11,160 --> 00:23:14,920 Speaker 1: collect supersensitive data from people. Everything you have to show 394 00:23:14,920 --> 00:23:17,640 Speaker 1: the bank when you want to get a mortgage. This 395 00:23:17,760 --> 00:23:22,880 Speaker 1: data has to be secure. IBMS hyper Protect Accelerator enables 396 00:23:22,920 --> 00:23:26,639 Speaker 1: small businesses to store sensitive data in the cloud and 397 00:23:26,760 --> 00:23:30,560 Speaker 1: keep that data secure. Brian says, it lets home Lending 398 00:23:30,600 --> 00:23:33,359 Speaker 1: Pal do something they would never do on their own. 399 00:23:34,880 --> 00:23:38,080 Speaker 1: From a technical perspective, you have different compliance checks that 400 00:23:38,160 --> 00:23:40,480 Speaker 1: you have to meet to work with banking institutions or 401 00:23:40,520 --> 00:23:44,920 Speaker 1: financial institutions. So it allows a small startup like home 402 00:23:45,000 --> 00:23:47,480 Speaker 1: Lending Pal to still be able to meet those checks 403 00:23:47,480 --> 00:23:51,840 Speaker 1: and balances to bring an innovative solution to the table 404 00:23:51,880 --> 00:23:55,120 Speaker 1: for a financial institution, where more than likely as a startup, 405 00:23:55,119 --> 00:23:56,560 Speaker 1: you're not going to have the ability to do that 406 00:23:56,600 --> 00:23:58,639 Speaker 1: on your own, just because it is so expensive to 407 00:23:58,680 --> 00:24:01,199 Speaker 1: either have in turn servers or to try to do 408 00:24:01,240 --> 00:24:03,399 Speaker 1: it on your own. As well, so, so people have 409 00:24:03,440 --> 00:24:06,280 Speaker 1: to trust you to use home landing piller, right, Like, 410 00:24:06,320 --> 00:24:09,719 Speaker 1: I'm giving you everything, How do you convince me? How 411 00:24:09,720 --> 00:24:12,000 Speaker 1: do you convince customers that you're going to keep their 412 00:24:12,080 --> 00:24:16,879 Speaker 1: data safe? Absolutely? Um. Part of it is doing stuff 413 00:24:16,880 --> 00:24:20,399 Speaker 1: like this where we're acknowledging and making the consumers aware 414 00:24:20,440 --> 00:24:22,720 Speaker 1: of our relationship with IBM and how IBM is handling 415 00:24:22,760 --> 00:24:26,680 Speaker 1: our storage of the data and the sensitive data itself. Technically, 416 00:24:26,680 --> 00:24:30,440 Speaker 1: the IBM description of it is their confidential computing services 417 00:24:30,520 --> 00:24:33,240 Speaker 1: or cloud services, and it's basically saying that even though 418 00:24:33,240 --> 00:24:36,119 Speaker 1: the information is stored in the cloud, IBM is going 419 00:24:36,119 --> 00:24:38,760 Speaker 1: to do a lot to help Home Lending Pal protect 420 00:24:38,800 --> 00:24:41,359 Speaker 1: this sensitive data. Part of it is being able to 421 00:24:41,400 --> 00:24:44,320 Speaker 1: show ibm s logo on our website. You you'd be 422 00:24:44,320 --> 00:24:47,119 Speaker 1: surprised how much logo recognition helps people understand that this 423 00:24:47,240 --> 00:24:50,800 Speaker 1: is a legit business, a legit company, if you will. 424 00:24:51,359 --> 00:24:53,359 Speaker 1: And then there's also stuff like you know, people seeing 425 00:24:54,240 --> 00:24:56,920 Speaker 1: the address of the business, contact information for the business. 426 00:24:56,960 --> 00:24:59,639 Speaker 1: Like all this stuff factors into why people will be 427 00:24:59,680 --> 00:25:01,680 Speaker 1: willing to give us their data. But a lot of 428 00:25:01,720 --> 00:25:03,840 Speaker 1: that it's very contingent on just people seeing the IBM 429 00:25:03,880 --> 00:25:06,119 Speaker 1: logo and saying that, hey, you know, we can if 430 00:25:06,160 --> 00:25:08,600 Speaker 1: we don't trust home Landing Path, we definitely trust IBM 431 00:25:08,640 --> 00:25:11,119 Speaker 1: with this aspect of the business. So what is the 432 00:25:11,160 --> 00:25:14,320 Speaker 1: sort of story of of working with IBM on this. 433 00:25:14,440 --> 00:25:16,840 Speaker 1: I mean, did you just figure out that they had 434 00:25:16,840 --> 00:25:18,880 Speaker 1: the thing you need or did they sort of work 435 00:25:18,920 --> 00:25:22,240 Speaker 1: with you to to build the thing you need? We 436 00:25:22,320 --> 00:25:27,080 Speaker 1: told them what we wanted. I think there's a certain 437 00:25:28,040 --> 00:25:30,879 Speaker 1: special relationship that we have with IBM. As I mentioned, 438 00:25:31,040 --> 00:25:34,760 Speaker 1: Steve and I are are very aggressive internally and externally 439 00:25:34,800 --> 00:25:37,439 Speaker 1: in terms of getting things changed in this industry, especially 440 00:25:37,480 --> 00:25:40,879 Speaker 1: when talk about systemic change, and sometimes that requires you 441 00:25:40,960 --> 00:25:43,600 Speaker 1: to make very big ask, you know, swing for the 442 00:25:43,600 --> 00:25:47,640 Speaker 1: fences and see what happens. And as we found out more, 443 00:25:47,680 --> 00:25:50,239 Speaker 1: as we we hired better talent, as we understood more 444 00:25:50,240 --> 00:25:52,440 Speaker 1: of what we were trying to do, it made it 445 00:25:52,480 --> 00:25:54,679 Speaker 1: a lot easier for us to really share this vision 446 00:25:54,680 --> 00:25:58,760 Speaker 1: with IBM. And then now they're able to recommend products 447 00:25:58,800 --> 00:26:00,640 Speaker 1: to say we see you try to do it this way, 448 00:26:00,720 --> 00:26:02,840 Speaker 1: but maybe you want to use our internal product and 449 00:26:02,920 --> 00:26:04,760 Speaker 1: do it with this instead, And so that makes it 450 00:26:04,760 --> 00:26:07,240 Speaker 1: a lot easier for us to try to bring artificial 451 00:26:07,240 --> 00:26:11,160 Speaker 1: intelligence and blockchain to an industry that hasn't historically accepted 452 00:26:11,240 --> 00:26:15,320 Speaker 1: new technology that well, So where are you in your 453 00:26:15,400 --> 00:26:18,080 Speaker 1: journey as a company. I know you're still sort of 454 00:26:18,080 --> 00:26:21,680 Speaker 1: working on it. What can customers do now with your product? 455 00:26:23,359 --> 00:26:29,040 Speaker 1: They could get recommendations right now. We're fully licensed Colorado, 456 00:26:29,160 --> 00:26:33,879 Speaker 1: Florida and in North Carolina, So right now, customers from 457 00:26:34,000 --> 00:26:36,160 Speaker 1: those days can expect to be connected with the lender 458 00:26:36,680 --> 00:26:42,040 Speaker 1: with full guidance as to what exactly they're getting into 459 00:26:42,119 --> 00:26:47,040 Speaker 1: and what pricing expectations they auto be presented with. Have 460 00:26:47,160 --> 00:26:49,480 Speaker 1: you heard back? I mean, I know that this is 461 00:26:49,560 --> 00:26:51,840 Speaker 1: kind of a weird question, given that the whole point 462 00:26:51,880 --> 00:26:55,359 Speaker 1: is that people can be anonymized, But are you able 463 00:26:55,400 --> 00:26:57,679 Speaker 1: to talk to your customers? Have have inn off? Your 464 00:26:57,680 --> 00:27:01,760 Speaker 1: customers told you about how it's hell to them. Surprisingly, 465 00:27:02,320 --> 00:27:04,359 Speaker 1: a lot of our customers will reach out to us 466 00:27:04,359 --> 00:27:06,960 Speaker 1: and give us use cases. We've had local TV interviews 467 00:27:06,960 --> 00:27:10,159 Speaker 1: where they've interviewed them about those success stories. We'll have 468 00:27:10,240 --> 00:27:12,320 Speaker 1: customers that all reach out to us with challenges that 469 00:27:12,359 --> 00:27:14,520 Speaker 1: they're having and hoping that we can help them through those. 470 00:27:15,040 --> 00:27:17,600 Speaker 1: Even if we have to manually connect a borrower to 471 00:27:17,680 --> 00:27:19,760 Speaker 1: a lender and a state that we don't operate and 472 00:27:19,800 --> 00:27:22,639 Speaker 1: we're more than happy to do that in exchange for that, 473 00:27:22,680 --> 00:27:25,520 Speaker 1: they're basically helping us build out this new process. And 474 00:27:25,560 --> 00:27:28,200 Speaker 1: so that's kind of the beauty of the system is that, 475 00:27:28,240 --> 00:27:31,080 Speaker 1: you know, customers are coming in at all stages of 476 00:27:31,119 --> 00:27:34,240 Speaker 1: the buying cycle. You have some that are still renting 477 00:27:34,280 --> 00:27:37,120 Speaker 1: at that day dreaming phase where they're really trying to understand, 478 00:27:37,119 --> 00:27:39,439 Speaker 1: you know, is home ownership a feasible option for me? 479 00:27:39,840 --> 00:27:41,920 Speaker 1: And then you have some that are you know, trying 480 00:27:41,960 --> 00:27:45,040 Speaker 1: to test out new features like optimal character recognition software 481 00:27:45,040 --> 00:27:47,120 Speaker 1: where they're able to upload documents and see how those 482 00:27:47,119 --> 00:27:50,560 Speaker 1: documents transferred to lenders. So I really think that is 483 00:27:50,600 --> 00:27:53,240 Speaker 1: the beauty about what we're building is that the people 484 00:27:53,240 --> 00:27:56,320 Speaker 1: have helped us build it so far. Are there any 485 00:27:56,320 --> 00:28:02,360 Speaker 1: particular stories you've heard from customers that have stayed with you? Um? Honestly, 486 00:28:02,359 --> 00:28:04,280 Speaker 1: I think the one that's most relevant to me, that 487 00:28:04,400 --> 00:28:06,359 Speaker 1: sticks closest to my heart is my mom. You know, 488 00:28:06,480 --> 00:28:09,040 Speaker 1: she was looking to try to buy another house and 489 00:28:09,080 --> 00:28:11,560 Speaker 1: we were able to get her approved for a little 490 00:28:11,560 --> 00:28:13,760 Speaker 1: bit over six D fifty thousand, which was about fifty 491 00:28:13,800 --> 00:28:16,320 Speaker 1: thousand more than what she had heard from anyone else 492 00:28:16,359 --> 00:28:19,040 Speaker 1: in the area. Uh, So you know, we've really been excited. 493 00:28:19,040 --> 00:28:20,960 Speaker 1: At least I had really been excited about that one. 494 00:28:21,320 --> 00:28:23,320 Speaker 1: That's great, You're not gonna do better than your mom, 495 00:28:23,760 --> 00:28:26,680 Speaker 1: right for me, that was the whole reason I built 496 00:28:26,680 --> 00:28:28,840 Speaker 1: the system. So, you know, so that one really sticks 497 00:28:28,840 --> 00:28:31,720 Speaker 1: closest to me is because, uh, we've had some users 498 00:28:31,720 --> 00:28:34,000 Speaker 1: that have gone through the entire process and have helped 499 00:28:34,040 --> 00:28:36,919 Speaker 1: us go from our initial phase and we've really been 500 00:28:37,000 --> 00:28:40,040 Speaker 1: launching in phases where at first it was more show 501 00:28:40,160 --> 00:28:42,560 Speaker 1: just showing the affordability amount, like you know, what was 502 00:28:42,600 --> 00:28:44,880 Speaker 1: the amount of home that you could afford? Uh? And now, 503 00:28:45,000 --> 00:28:48,280 Speaker 1: as Steven mentioned, we're getting into this much more interactive, uh, 504 00:28:48,360 --> 00:28:52,080 Speaker 1: conversational dialogue where consumers are not only showing kind of 505 00:28:52,120 --> 00:28:54,640 Speaker 1: what they want to buy, but also getting into kind 506 00:28:54,640 --> 00:28:57,240 Speaker 1: of what their feelings are, what is what are their 507 00:28:57,240 --> 00:28:59,760 Speaker 1: sentiments that they're looking for in a potential relationship with 508 00:28:59,840 --> 00:29:02,880 Speaker 1: they lender. Uh. And so we're really excited when consumers 509 00:29:02,920 --> 00:29:05,360 Speaker 1: come in and they test new features and they say, hey, 510 00:29:05,400 --> 00:29:08,120 Speaker 1: this is working great, this isn't working, Uh, you know 511 00:29:08,200 --> 00:29:09,920 Speaker 1: what about this? And we think that's really going to 512 00:29:10,040 --> 00:29:12,640 Speaker 1: lead into our series A raise here in the next 513 00:29:12,680 --> 00:29:14,440 Speaker 1: couple of months where we'll go out and raise hopefully 514 00:29:14,440 --> 00:29:17,479 Speaker 1: eight figures and more to really flush out the features 515 00:29:17,480 --> 00:29:19,720 Speaker 1: that consumers have said they wanted the most. Is really 516 00:29:19,720 --> 00:29:23,880 Speaker 1: what we're most excited about. What's your dream for home 517 00:29:24,000 --> 00:29:25,920 Speaker 1: landing Pale If you think whatever, I don't know. Five 518 00:29:26,000 --> 00:29:28,480 Speaker 1: years in the future, ten years in the future, where 519 00:29:28,480 --> 00:29:30,800 Speaker 1: are you. I want to see at least a million people, 520 00:29:30,800 --> 00:29:34,840 Speaker 1: hopefully in a million minorities become homeowners by utilizing our product. 521 00:29:35,440 --> 00:29:38,320 Speaker 1: You know, we operate in an industry that's very lucrative 522 00:29:38,360 --> 00:29:41,520 Speaker 1: for a lot of people. Having supported IBM will hopefully 523 00:29:41,560 --> 00:29:44,320 Speaker 1: help us from a business perspective, but I don't want 524 00:29:44,360 --> 00:29:46,440 Speaker 1: us to lose sight of our social impact goals and 525 00:29:46,440 --> 00:29:48,880 Speaker 1: the things that we're really set out before, which was 526 00:29:48,960 --> 00:29:51,080 Speaker 1: to make the process more eguiable for everyone. You know, 527 00:29:51,120 --> 00:29:54,360 Speaker 1: I think if we were to be acquired or to 528 00:29:54,360 --> 00:29:56,520 Speaker 1: to do an initial public offering in five years and 529 00:29:56,520 --> 00:29:58,360 Speaker 1: we're not doing that, then for me it would not 530 00:29:58,400 --> 00:30:01,560 Speaker 1: be as as sweet as if it were to ensure 531 00:30:01,600 --> 00:30:03,920 Speaker 1: that we're actually doing stuff to close the gap for people. 532 00:30:05,360 --> 00:30:07,280 Speaker 1: Thank you guys so much for your time. I really 533 00:30:07,360 --> 00:30:10,680 Speaker 1: it was great to talk with you. Pleasure. Thank you 534 00:30:18,120 --> 00:30:20,920 Speaker 1: Malcolm glave all here. To end today's show, I want 535 00:30:20,920 --> 00:30:23,360 Speaker 1: to talk about someone who we didn't hear from in 536 00:30:23,400 --> 00:30:28,480 Speaker 1: the interview, but who we heard about, Brian's mom, because 537 00:30:28,480 --> 00:30:31,280 Speaker 1: her story really is the story of home Lending Pall. 538 00:30:32,000 --> 00:30:34,520 Speaker 1: Remember how Brian told us that back in the odds, 539 00:30:34,840 --> 00:30:37,640 Speaker 1: his mom got that crappy mortgage, the one that left 540 00:30:37,640 --> 00:30:40,760 Speaker 1: her paying higher interest rates than she should have been paying. 541 00:30:41,520 --> 00:30:44,760 Speaker 1: That happened to a lot of people, particularly people of color. 542 00:30:45,480 --> 00:30:48,680 Speaker 1: It was that story and others like it, that really 543 00:30:48,760 --> 00:30:52,320 Speaker 1: inspired Brian to team up with Stephen to build Home 544 00:30:52,400 --> 00:30:56,320 Speaker 1: Lending Pall. They wanted to fix a home lending system 545 00:30:56,400 --> 00:31:01,160 Speaker 1: that had been opaque and unfair basically forever. Most people 546 00:31:01,200 --> 00:31:04,880 Speaker 1: applying for mortgages aren't thinking about the technology that's behind 547 00:31:04,880 --> 00:31:07,880 Speaker 1: the scenes. We all just want a good mortgage with 548 00:31:08,000 --> 00:31:12,480 Speaker 1: fair terms. And because Brian and Stephen made creative use 549 00:31:12,560 --> 00:31:17,560 Speaker 1: of ib M technology using AI, blockchain, and cloud to 550 00:31:17,680 --> 00:31:22,120 Speaker 1: rethink the home loan process, that is now possible for 551 00:31:22,160 --> 00:31:27,760 Speaker 1: all of us. On the next episode of Smart Talks 552 00:31:27,800 --> 00:31:31,560 Speaker 1: with IBM, as AI becomes more widespread, how do we 553 00:31:31,680 --> 00:31:36,440 Speaker 1: ensure that it is built and deployed responsibly, We talk 554 00:31:36,520 --> 00:31:41,680 Speaker 1: with Theedra bonadiras trustworthy AI practice leader within ib M Consulting. 555 00:31:44,160 --> 00:31:48,680 Speaker 1: Smart Talks with IBM is produced by Molly Sosha, Alexandra Gerraton, 556 00:31:49,040 --> 00:31:54,000 Speaker 1: Royston Preserve and Edith Rousselo with Jacob Goldstein. We're edited 557 00:31:54,040 --> 00:31:58,280 Speaker 1: by Jan Guerra. Our engineers are Jason Gambrel, Sarah Brigre 558 00:31:58,520 --> 00:32:03,640 Speaker 1: and Ben Tolliday. Theme song by Gramoscope. Special thanks to 559 00:32:03,720 --> 00:32:07,880 Speaker 1: Colly Igliore, Andy Kelly, Kathy Callaghan and the eight Bar 560 00:32:08,000 --> 00:32:10,520 Speaker 1: and I b M teams, as well as the Pushkin 561 00:32:10,800 --> 00:32:14,720 Speaker 1: marketing team. Smart Talks with ib M is a production 562 00:32:14,760 --> 00:32:18,200 Speaker 1: of Pushkin Industries and i Heart Media. To find more 563 00:32:18,240 --> 00:32:22,920 Speaker 1: Pushkin podcasts, listen on the iHeart Radio app, Apple Podcasts, 564 00:32:23,320 --> 00:32:28,000 Speaker 1: or wherever you listen to podcasts. I'm Malcolm Gladwell. This 565 00:32:28,200 --> 00:32:40,000 Speaker 1: is a paid advertisement from IBM.