1 00:00:01,440 --> 00:00:04,640 Speaker 1: Hello, Hello, Welcome to a new season of Smart Talks 2 00:00:04,640 --> 00:00:10,520 Speaker 1: with IBM, a podcast from Pushkin Industries, iHeartRadio and IBM. 3 00:00:10,840 --> 00:00:15,360 Speaker 1: I'm Malcolm Gladwell. This season, we're talking to new creators, 4 00:00:15,880 --> 00:00:20,720 Speaker 1: the developers, data scientists, CTOs, and other visionaries who are 5 00:00:20,760 --> 00:00:26,080 Speaker 1: creatively applying technology in business to drive change. Channeling their 6 00:00:26,079 --> 00:00:30,680 Speaker 1: knowledge and expertise, they're developing more creative and effective solutions 7 00:00:31,160 --> 00:00:35,360 Speaker 1: no matter the industry. Our guests today are Brian Young 8 00:00:35,520 --> 00:00:40,680 Speaker 1: and Stephen Better, co founders of home lending Pal. Home 9 00:00:40,760 --> 00:00:44,839 Speaker 1: lending Pal is a member of the IBM hyper Protect Accelerator, 10 00:00:45,400 --> 00:00:50,360 Speaker 1: an investment readiness and technical mentorship program that supports impact 11 00:00:50,360 --> 00:00:55,880 Speaker 1: focused startups leveraging highly sensitive data. Their story is a 12 00:00:56,000 --> 00:01:01,760 Speaker 1: perfect place to start our season. They recognize a profound problem, 13 00:01:02,160 --> 00:01:05,920 Speaker 1: the horrible process of getting a home loan, especially if 14 00:01:05,920 --> 00:01:09,200 Speaker 1: you're part of an underserved community, a process that, as 15 00:01:09,240 --> 00:01:13,160 Speaker 1: you'll hear, is not only confusing and complex, but often 16 00:01:13,319 --> 00:01:17,479 Speaker 1: deeply unfair. So Brian and Stephen teamed up to use 17 00:01:17,520 --> 00:01:21,959 Speaker 1: technology to attack that problem in a bunch of creative ways. 18 00:01:22,280 --> 00:01:25,120 Speaker 1: You'll hear how they're tapping into blockchain to make the 19 00:01:25,200 --> 00:01:29,880 Speaker 1: home loan process more transparent and fair, using AI to 20 00:01:29,920 --> 00:01:32,880 Speaker 1: help people learn how to qualify for a loan, and 21 00:01:32,920 --> 00:01:37,720 Speaker 1: relying on IBM technology to store consumer's most sensitive information 22 00:01:38,240 --> 00:01:42,800 Speaker 1: safely in the cloud. Brian and Stephen talked with Jacob Goldstein, 23 00:01:43,160 --> 00:01:47,200 Speaker 1: host of the pushkin podcast What's Your Problem. Jacob has 24 00:01:47,240 --> 00:01:51,040 Speaker 1: covered technology and business for over a decade, first at 25 00:01:51,080 --> 00:01:55,600 Speaker 1: the Wall Street Journal, then at MPR. Now let's get 26 00:01:55,640 --> 00:01:56,400 Speaker 1: into the interview. 27 00:02:04,960 --> 00:02:07,800 Speaker 2: Let's start this like a brom com. How did you 28 00:02:08,000 --> 00:02:10,400 Speaker 2: meet each other and decide to start a company together. 29 00:02:11,040 --> 00:02:13,240 Speaker 3: Stephen was suposed to come to a bachelor party in 30 00:02:13,280 --> 00:02:15,960 Speaker 3: Miami and didn't show up, and it broke my heart. 31 00:02:16,520 --> 00:02:19,079 Speaker 4: There's more to the story than just simply that. 32 00:02:19,639 --> 00:02:22,920 Speaker 3: One of my old employees introduced us. I've just left 33 00:02:22,960 --> 00:02:25,840 Speaker 3: marketto They've been acquired for one point four billion, and 34 00:02:25,880 --> 00:02:28,720 Speaker 3: I am, you know, living the Miami lifestyle. You know, 35 00:02:28,880 --> 00:02:30,960 Speaker 3: I have a condo on the water and all the 36 00:02:31,160 --> 00:02:33,720 Speaker 3: nice things that go with it. A guy named Michael 37 00:02:33,800 --> 00:02:37,000 Speaker 3: Ramsey had asked me, you know, would I help him 38 00:02:37,040 --> 00:02:39,399 Speaker 3: do mortgage lead generation? And I was like, you know, sure, 39 00:02:39,400 --> 00:02:43,359 Speaker 3: I'm not doing anything else? Why not? And I meet Steven. 40 00:02:43,840 --> 00:02:44,959 Speaker 3: He was in North Carolina. 41 00:02:45,520 --> 00:02:48,360 Speaker 4: I left a pretty fruitful career in banking. 42 00:02:48,639 --> 00:02:52,600 Speaker 2: I was an underwriter underwriting loans. Me it's basically deciding 43 00:02:52,639 --> 00:02:54,880 Speaker 2: who should get a loan and at what interest rate? 44 00:02:54,960 --> 00:02:59,239 Speaker 5: Right, absolutely due diligence, right, which is understanding whether or 45 00:02:59,320 --> 00:03:04,919 Speaker 5: not this particular individual has the wherewithal to afford the mortgage, 46 00:03:05,080 --> 00:03:08,280 Speaker 5: also the credit risks that individual presents. 47 00:03:09,080 --> 00:03:13,560 Speaker 4: But there was this disconnect in that process. 48 00:03:13,639 --> 00:03:16,760 Speaker 5: Where you have hidden action taking place on one side 49 00:03:16,800 --> 00:03:19,760 Speaker 5: of the transaction, while you have another side of the 50 00:03:19,760 --> 00:03:21,840 Speaker 5: transaction that tends to hide information. 51 00:03:22,880 --> 00:03:25,959 Speaker 2: And just to be clear, it's the borrower who hides 52 00:03:26,000 --> 00:03:30,000 Speaker 2: information and the bank that hides the action the lender. 53 00:03:30,240 --> 00:03:34,600 Speaker 5: In most cases, but it's usually both sides of the negotias. 54 00:03:34,600 --> 00:03:39,240 Speaker 2: Everybody's hiding stuff from everybody else, absolutely. 55 00:03:38,520 --> 00:03:40,839 Speaker 4: And it's like sort of inadvertent as well too. 56 00:03:41,040 --> 00:03:44,000 Speaker 5: Right in that process, certain things fall through the cracks, 57 00:03:44,000 --> 00:03:47,200 Speaker 5: And you know, falling through the cracks means weeks without 58 00:03:47,280 --> 00:03:50,400 Speaker 5: notification from a bar's perspective as. 59 00:03:50,280 --> 00:03:53,520 Speaker 4: To whether or not you know this deal is moving forward. 60 00:03:53,680 --> 00:03:56,360 Speaker 2: Okay, So the problem is a lack of information on 61 00:03:56,400 --> 00:03:59,440 Speaker 2: both sides, and that winds up leading to bad outcomes. 62 00:03:59,480 --> 00:04:02,880 Speaker 2: It winds up to long delays that are frustrating or 63 00:04:03,000 --> 00:04:04,680 Speaker 2: scary for the borrower. 64 00:04:05,400 --> 00:04:09,400 Speaker 3: Yes, a lot of consumers just don't have anywhere to go. 65 00:04:09,960 --> 00:04:12,280 Speaker 3: If you go online, everything is too broad and generic, 66 00:04:12,360 --> 00:04:13,960 Speaker 3: especially if you know you're not ready to buy at 67 00:04:13,960 --> 00:04:16,760 Speaker 3: that moment. If you talk to a lender or relative, 68 00:04:16,760 --> 00:04:18,880 Speaker 3: if you're not ready to buy at that moment, they'll 69 00:04:18,920 --> 00:04:20,880 Speaker 3: help you, but it's not the same level of help, 70 00:04:20,880 --> 00:04:22,160 Speaker 3: and you're not going to get that same level of 71 00:04:22,160 --> 00:04:24,960 Speaker 3: support over months, because you know, buying house isn't like 72 00:04:25,000 --> 00:04:27,880 Speaker 3: buying a piece of candy online. And so we really 73 00:04:27,960 --> 00:04:29,960 Speaker 3: looked at, Okay, well, how can we give people this 74 00:04:30,640 --> 00:04:34,359 Speaker 3: safe environment to go explore and understand what home ownership 75 00:04:34,400 --> 00:04:36,760 Speaker 3: could look like for them based on their personal information. 76 00:04:36,839 --> 00:04:38,840 Speaker 3: And that's kind of when I reached back out to 77 00:04:38,880 --> 00:04:42,160 Speaker 3: Stephen around August of twenty seventeen and said, hey, you know, 78 00:04:42,200 --> 00:04:45,159 Speaker 3: we need to do this together. You understand the back 79 00:04:45,240 --> 00:04:48,320 Speaker 3: ends die from a lender underwriter's perspective, and I understand 80 00:04:48,360 --> 00:04:50,960 Speaker 3: the plight of the consumers, and if we come together, 81 00:04:51,040 --> 00:04:53,640 Speaker 3: this could be something that could be really unique. A 82 00:04:53,720 --> 00:04:56,280 Speaker 3: capitalist solution to a social challenge is probably the best 83 00:04:56,279 --> 00:04:56,960 Speaker 3: way to put it. 84 00:04:57,080 --> 00:04:59,960 Speaker 2: So, Stephen, you're sort of coming from the banking side, 85 00:05:00,120 --> 00:05:04,160 Speaker 2: and Brian, you're sort of coming from the tech side. Absolutely, 86 00:05:05,000 --> 00:05:07,360 Speaker 2: what exactly is the problem that you guys are trying 87 00:05:07,400 --> 00:05:09,280 Speaker 2: to solve when you start this company. 88 00:05:09,400 --> 00:05:13,599 Speaker 3: In its simplest essence, is data democratization, the ability to 89 00:05:13,680 --> 00:05:17,719 Speaker 3: take complex information and simplify so that someone that isn't 90 00:05:17,760 --> 00:05:20,640 Speaker 3: an expert like Stephen can understand what's going on. 91 00:05:21,240 --> 00:05:23,960 Speaker 2: And in this case specifically, what is the data that 92 00:05:24,000 --> 00:05:25,440 Speaker 2: you're trying to democratize? 93 00:05:25,960 --> 00:05:29,000 Speaker 3: Underwriting data, so the decision or the data that is 94 00:05:29,080 --> 00:05:32,360 Speaker 3: utilized to determine whether or not you are approved or 95 00:05:32,400 --> 00:05:33,599 Speaker 3: declined for a home loan. 96 00:05:34,120 --> 00:05:36,960 Speaker 2: So right now, if I go apply for a loan, 97 00:05:37,880 --> 00:05:41,320 Speaker 2: they approve me or they decline me. But do I 98 00:05:41,480 --> 00:05:43,440 Speaker 2: know why not? 99 00:05:43,560 --> 00:05:46,000 Speaker 3: Really? I mean, you get a letter of an adverse letter, 100 00:05:46,040 --> 00:05:48,360 Speaker 3: but it's still very broad and generic. It doesn't really 101 00:05:48,400 --> 00:05:51,120 Speaker 3: tell you what to focus on next, but you do 102 00:05:51,200 --> 00:05:53,320 Speaker 3: have a very high level sense of why you're decline. 103 00:05:54,000 --> 00:05:58,400 Speaker 5: Yeah, there's no true guidance from that point of rejection, 104 00:05:58,960 --> 00:06:02,560 Speaker 5: right there's no fundamental understanding as to what could I 105 00:06:02,600 --> 00:06:06,200 Speaker 5: have done better? And that's really what sets this platform 106 00:06:06,240 --> 00:06:09,920 Speaker 5: apart and also why it's important how we're sort of 107 00:06:10,160 --> 00:06:12,200 Speaker 5: reframing this data workflow. 108 00:06:12,600 --> 00:06:14,200 Speaker 2: I want to get into the details of that, but 109 00:06:14,320 --> 00:06:17,200 Speaker 2: just as we sort of understand the problem a little 110 00:06:17,240 --> 00:06:19,040 Speaker 2: bit more, I mean, one piece of it that we 111 00:06:19,120 --> 00:06:23,920 Speaker 2: haven't talked about is race and the home ownership gap. 112 00:06:23,960 --> 00:06:25,720 Speaker 2: Can you guys talk a little bit about that and 113 00:06:25,720 --> 00:06:28,400 Speaker 2: how it fits with what you're trying to do. 114 00:06:29,800 --> 00:06:33,159 Speaker 3: Yeah, I mean, the home ownership gap, at least for 115 00:06:33,200 --> 00:06:36,800 Speaker 3: African Americans is larger now than it was fifty years 116 00:06:36,800 --> 00:06:40,520 Speaker 3: ago when segregation was legal, which is quite saddening. But 117 00:06:40,640 --> 00:06:43,880 Speaker 3: it's not just African Americans. And when you look at declines, 118 00:06:44,360 --> 00:06:47,880 Speaker 3: whether you are a woman, whether you are a minority, 119 00:06:48,279 --> 00:06:51,080 Speaker 3: whether you're a part of the LBGTQ plus community, it 120 00:06:51,200 --> 00:06:54,240 Speaker 3: shows that there's a higher level of declines for these 121 00:06:54,279 --> 00:06:57,640 Speaker 3: communities than there are for older white males. So you know, 122 00:06:57,680 --> 00:07:00,320 Speaker 3: in our perspective, there has to be a lot that 123 00:07:00,360 --> 00:07:03,560 Speaker 3: needs to be done in terms of resetting reconfiguring the 124 00:07:03,600 --> 00:07:05,920 Speaker 3: system to make it more fair and equitable for all. 125 00:07:06,560 --> 00:07:10,680 Speaker 2: So, if I understand you correctly, you're saying, basically, in 126 00:07:10,720 --> 00:07:14,560 Speaker 2: the current system, white men have an easier time getting 127 00:07:14,680 --> 00:07:16,480 Speaker 2: a mortgage than anybody else. 128 00:07:17,040 --> 00:07:18,880 Speaker 3: Well, you said it I'll just agree with it. 129 00:07:19,680 --> 00:07:21,760 Speaker 2: I think you said it. I think if I understand 130 00:07:21,800 --> 00:07:22,920 Speaker 2: you're correctly you said. 131 00:07:23,920 --> 00:07:25,520 Speaker 3: That is what the data shows us, and it's not 132 00:07:25,560 --> 00:07:27,440 Speaker 3: just my perspective, that's what the data shows us. 133 00:07:27,440 --> 00:07:31,880 Speaker 2: So and so, how are you trying to help fix 134 00:07:31,960 --> 00:07:33,120 Speaker 2: that problem. 135 00:07:33,560 --> 00:07:35,200 Speaker 4: By turning everybody into corn? 136 00:07:36,520 --> 00:07:39,200 Speaker 2: By turning everybody into corn? I like it, Yeah, what 137 00:07:39,240 --> 00:07:40,000 Speaker 2: do you mean by that? 138 00:07:40,280 --> 00:07:43,680 Speaker 5: Through the power of math, right, cryptography specifically, we are 139 00:07:43,720 --> 00:07:47,400 Speaker 5: able to make everyone look the same and the underwriter 140 00:07:47,800 --> 00:07:52,280 Speaker 5: just simply understands the fundamental attributes that ought to drive 141 00:07:52,320 --> 00:07:56,000 Speaker 5: that approval disapproval decision. Right, in order to help us 142 00:07:56,000 --> 00:07:59,920 Speaker 5: and also to help our government understand where these biases 143 00:08:00,960 --> 00:08:04,400 Speaker 5: are coming from. Our lenders are required to ask you 144 00:08:04,720 --> 00:08:07,440 Speaker 5: what your raise, what your sex, even your age, right, Like, 145 00:08:07,560 --> 00:08:11,920 Speaker 5: all of this comes with this application scenario. But does 146 00:08:11,960 --> 00:08:17,360 Speaker 5: this information inadvertently create the bias? Can we make everyone 147 00:08:17,480 --> 00:08:22,320 Speaker 5: look the same and start to remove or better identify 148 00:08:22,440 --> 00:08:24,800 Speaker 5: where these issues are sort of coming from. 149 00:08:25,640 --> 00:08:29,320 Speaker 2: So you're trying to use technology to behind all the 150 00:08:29,360 --> 00:08:33,920 Speaker 2: decision makers in the home loan process, to race, ethnicity. 151 00:08:33,480 --> 00:08:35,560 Speaker 3: Genrector specifically blockchain. 152 00:08:36,800 --> 00:08:40,440 Speaker 1: There are three big tech ideas behind home lending pal 153 00:08:40,920 --> 00:08:43,240 Speaker 1: at least three that we're going to talk about today 154 00:08:43,280 --> 00:08:47,160 Speaker 1: on the show, and blockchain is big tech idea number one. 155 00:08:47,840 --> 00:08:50,520 Speaker 1: You may have heard of blockchain because it's the key 156 00:08:50,600 --> 00:08:55,360 Speaker 1: idea behind cryptocurrency, but the idea of blockchain is bigger 157 00:08:55,400 --> 00:08:58,480 Speaker 1: than just digital money and much more than just a 158 00:08:58,520 --> 00:09:02,120 Speaker 1: new way to store information on the Internet. Blockchain is 159 00:09:02,160 --> 00:09:07,600 Speaker 1: a shared, immutable ledger that facilitates the process of recording 160 00:09:07,640 --> 00:09:12,960 Speaker 1: transactions and tracking assets in a business network. Brian and 161 00:09:13,000 --> 00:09:16,240 Speaker 1: Stephen want to use blockchain to gather up the information 162 00:09:16,360 --> 00:09:19,800 Speaker 1: on race and gender that's required by law without showing 163 00:09:19,840 --> 00:09:22,920 Speaker 1: it to the lenders making the decisions about who gets 164 00:09:22,920 --> 00:09:23,200 Speaker 1: a loan. 165 00:09:24,520 --> 00:09:28,600 Speaker 3: Our argument, or our thesis is that with the leverage 166 00:09:28,640 --> 00:09:31,280 Speaker 3: of a mutable ledger such as blockchain, you're able to 167 00:09:31,280 --> 00:09:34,319 Speaker 3: still collect the information that is necessary for the Home 168 00:09:34,360 --> 00:09:37,439 Speaker 3: Mortgage Disclosure Act or HUMDA, as Stephen was referring to. 169 00:09:38,000 --> 00:09:40,160 Speaker 3: But then with a smart contract, you don't have to 170 00:09:40,160 --> 00:09:43,360 Speaker 3: release that information, so after the decision, the approval of 171 00:09:43,440 --> 00:09:44,960 Speaker 3: decline is made for the consumer. 172 00:09:45,800 --> 00:09:47,480 Speaker 2: So you have this big idea for what you want 173 00:09:47,520 --> 00:09:49,360 Speaker 2: to do as a business, what you want to do socially, 174 00:09:50,040 --> 00:09:53,560 Speaker 2: but how do you make creative use of technology to 175 00:09:53,720 --> 00:09:55,480 Speaker 2: do the thing you want to do, to make it real. 176 00:09:56,480 --> 00:09:58,200 Speaker 3: You know, we're trying to build something that hasn't been 177 00:09:58,200 --> 00:10:01,280 Speaker 3: done in the mortgage industry. When we talk about artificial 178 00:10:01,320 --> 00:10:03,600 Speaker 3: intelligence and a virtual assistant, most people think of that 179 00:10:03,640 --> 00:10:06,720 Speaker 3: as just a one way street. You know, we are 180 00:10:06,760 --> 00:10:09,599 Speaker 3: trying to build this human like interaction where it is 181 00:10:09,640 --> 00:10:13,079 Speaker 3: able to not only understand, but to respond, and then 182 00:10:13,080 --> 00:10:17,960 Speaker 3: to leverage those responses and create a roadmap towards allowing 183 00:10:18,000 --> 00:10:20,800 Speaker 3: you to achieve your goals, which is probably one of 184 00:10:20,840 --> 00:10:23,800 Speaker 3: the most creative things that I've ever done personally. But 185 00:10:23,880 --> 00:10:27,000 Speaker 3: it also requires you to be willing to accept constructive 186 00:10:27,040 --> 00:10:28,960 Speaker 3: criticism from the people that are going to be using 187 00:10:29,000 --> 00:10:32,160 Speaker 3: get up front, and a lot of what we're doing 188 00:10:32,200 --> 00:10:33,920 Speaker 3: is really trying to find creative ways just to get 189 00:10:33,960 --> 00:10:37,000 Speaker 3: them involved in that conversation to say that, hey, you know, 190 00:10:37,040 --> 00:10:39,559 Speaker 3: we are trying to build this to help you. Right now, 191 00:10:39,559 --> 00:10:42,679 Speaker 3: there's about twenty one million mortgage reading millennials to day 192 00:10:42,800 --> 00:10:45,360 Speaker 3: that are qualified to buy a loan, even though they're 193 00:10:45,400 --> 00:10:48,000 Speaker 3: not trying. They just don't know. We're trying to bring 194 00:10:48,040 --> 00:10:50,400 Speaker 3: greater trust and transparency to this process. 195 00:10:50,880 --> 00:10:54,719 Speaker 5: Yeah, I guess, from my perspective, beyond just simply understanding 196 00:10:54,880 --> 00:10:58,480 Speaker 5: the technology and what it's able to do, I think 197 00:10:58,520 --> 00:11:01,800 Speaker 5: it takes the will to go ahead and take on 198 00:11:02,080 --> 00:11:07,880 Speaker 5: that complexity to try something new. We were challenged constantly 199 00:11:08,200 --> 00:11:12,560 Speaker 5: with why not a simpler solution, right, But in reality 200 00:11:12,600 --> 00:11:17,000 Speaker 5: the problem is much more complicated than the simplicity these 201 00:11:17,000 --> 00:11:20,680 Speaker 5: forces wanted to bring into the table. You have to 202 00:11:20,720 --> 00:11:23,240 Speaker 5: have vision, you have to have a desire to want 203 00:11:23,320 --> 00:11:24,880 Speaker 5: to make fundamental change. 204 00:11:25,240 --> 00:11:29,040 Speaker 3: Yeah, new tech built on old, broken processes doesn't allow 205 00:11:29,080 --> 00:11:32,880 Speaker 3: for systemic change. You have to try to find ways 206 00:11:32,920 --> 00:11:35,560 Speaker 3: to not only just to make it easier for people 207 00:11:35,559 --> 00:11:38,000 Speaker 3: to connect to lenders, but at the core of what 208 00:11:38,040 --> 00:11:40,079 Speaker 3: we were trying to build, we really wanted to address 209 00:11:40,080 --> 00:11:42,880 Speaker 3: the systemic issues in the home buying process, and that 210 00:11:42,960 --> 00:11:45,679 Speaker 3: required us to try something different basically, and I think 211 00:11:45,720 --> 00:11:47,520 Speaker 3: that's the most creative thing you can do in an 212 00:11:47,520 --> 00:11:50,440 Speaker 3: industry that typically, if Steven mentioned wanted us to do, 213 00:11:50,520 --> 00:11:51,040 Speaker 3: is simpler. 214 00:11:51,440 --> 00:11:51,720 Speaker 4: Yeah. 215 00:11:52,160 --> 00:11:54,080 Speaker 2: So one of the ideas you guys have is that 216 00:11:55,080 --> 00:12:01,000 Speaker 2: transparency can help reduce by us. In what way are 217 00:12:01,080 --> 00:12:05,600 Speaker 2: you using technology to bring more transparency to the home 218 00:12:05,640 --> 00:12:06,440 Speaker 2: buying process? 219 00:12:07,520 --> 00:12:11,360 Speaker 5: When we speak of transparency, when we speak of trust, 220 00:12:11,559 --> 00:12:15,640 Speaker 5: what we're really talking about is just the natural features 221 00:12:15,679 --> 00:12:16,640 Speaker 5: of the blockchain. 222 00:12:17,400 --> 00:12:17,679 Speaker 4: Right. 223 00:12:17,960 --> 00:12:22,440 Speaker 5: It's transparent because all participants within this framework have access 224 00:12:22,480 --> 00:12:28,280 Speaker 5: to this decentralized ledger, So we are all seeing how 225 00:12:28,320 --> 00:12:31,080 Speaker 5: these pieces are sort of moving. Right, we're playing poker 226 00:12:31,600 --> 00:12:36,640 Speaker 5: with our cards facing up. When we're speaking to trust, right, 227 00:12:36,640 --> 00:12:40,760 Speaker 5: we're speaking of the immutability of this information, knowing that 228 00:12:40,840 --> 00:12:43,959 Speaker 5: if an action is taken, it's there on the ledger 229 00:12:43,960 --> 00:12:49,280 Speaker 5: and we can't just simply remove it. So these features 230 00:12:49,880 --> 00:12:55,560 Speaker 5: lead to this forceful curing of certain biases that tend 231 00:12:55,600 --> 00:12:59,600 Speaker 5: to form within certain systems. We're not saying that we're 232 00:12:59,600 --> 00:13:03,120 Speaker 5: going to re move all bias, but what we're saying 233 00:13:03,280 --> 00:13:05,480 Speaker 5: is that we feel very confident that we'll be able 234 00:13:05,559 --> 00:13:12,040 Speaker 5: to reduce it significantly without regulatory reinforcement by the simple 235 00:13:12,200 --> 00:13:15,000 Speaker 5: nature of this technology stack that we're developing. 236 00:13:16,520 --> 00:13:19,480 Speaker 2: So, was there some moment when you guys had the 237 00:13:19,600 --> 00:13:22,960 Speaker 2: like light bulb, the aha idea that you could do this. 238 00:13:23,760 --> 00:13:26,880 Speaker 3: The moment that made me realize that this was doable 239 00:13:27,040 --> 00:13:29,240 Speaker 3: was when our first group of lenders invested. There was 240 00:13:29,280 --> 00:13:32,280 Speaker 3: a group called the Mortgage Collaborative. They are a collection 241 00:13:32,360 --> 00:13:35,600 Speaker 3: of about three hundred and twenty five lenders. I believe 242 00:13:35,640 --> 00:13:38,920 Speaker 3: across the country they represent about twenty five percent overall 243 00:13:38,960 --> 00:13:42,040 Speaker 3: originations that happened in the US when they kind of 244 00:13:42,040 --> 00:13:43,800 Speaker 3: stepped in and were like, hey, you know, we're going 245 00:13:43,880 --> 00:13:46,960 Speaker 3: to lead you your development before your Series A, We're 246 00:13:46,960 --> 00:13:48,120 Speaker 3: going to try to help you there, I think that 247 00:13:48,280 --> 00:13:51,000 Speaker 3: was the moment for me. And then we had shortly 248 00:13:51,040 --> 00:13:53,280 Speaker 3: after that. Joining that round was a group called Queno 249 00:13:53,360 --> 00:13:56,240 Speaker 3: Mutual or CMfg Ventures their discovery fund, and they are 250 00:13:56,280 --> 00:13:59,080 Speaker 3: the one of the largest collections of credit unis in 251 00:13:59,080 --> 00:14:01,600 Speaker 3: the industry. So, you know, typically you have an issue 252 00:14:01,600 --> 00:14:04,480 Speaker 3: where you know, consumers feel like there's a problem that's 253 00:14:04,520 --> 00:14:06,920 Speaker 3: not truly being solved. But to see that lenders were 254 00:14:06,920 --> 00:14:09,160 Speaker 3: looking to try to find solutions like ours, I think 255 00:14:09,200 --> 00:14:11,200 Speaker 3: that was the IHA moment for me. They said, hey, 256 00:14:11,640 --> 00:14:13,400 Speaker 3: you know this could be feasible for us. 257 00:14:13,400 --> 00:14:16,160 Speaker 2: That the people who actually have to work with you 258 00:14:17,040 --> 00:14:21,480 Speaker 2: want to help you. Like, that's exactly great, but just 259 00:14:21,560 --> 00:14:25,080 Speaker 2: tell me how will it work? Like walk me through. 260 00:14:25,120 --> 00:14:27,920 Speaker 2: I'm an ordinary person. I want to get a loan. 261 00:14:28,680 --> 00:14:31,840 Speaker 2: I come to home lending POW. What happens when when 262 00:14:31,920 --> 00:14:34,120 Speaker 2: you're fully you know, fully up and running. How's it 263 00:14:34,120 --> 00:14:34,600 Speaker 2: going to work? 264 00:14:35,880 --> 00:14:38,560 Speaker 3: Yeah, so you will spend about five minutes going through 265 00:14:38,600 --> 00:14:41,840 Speaker 3: our onboarding process where you're connecting your online bank accounts, 266 00:14:41,880 --> 00:14:43,880 Speaker 3: you're authorizing a soft fyco pool. 267 00:14:44,400 --> 00:14:46,560 Speaker 2: There's a credit report, basically a credit report. 268 00:14:46,640 --> 00:14:49,280 Speaker 3: Yeah, you're here. Most people don't realize. So, so lenders 269 00:14:49,280 --> 00:14:52,360 Speaker 3: are utilizing your FICO scores and most of the places 270 00:14:52,400 --> 00:14:54,760 Speaker 3: online that you're able to go to or showing vantage scores. 271 00:14:54,800 --> 00:14:56,960 Speaker 3: So that's kind of the first level of disconnect and 272 00:14:57,000 --> 00:14:59,920 Speaker 3: so we're solving for that first. So you go to 273 00:15:00,080 --> 00:15:04,360 Speaker 3: that process and then after you signed up, our virtual 274 00:15:04,400 --> 00:15:08,600 Speaker 3: assistant keV begins doing his work. He's analyzing your profile. 275 00:15:09,440 --> 00:15:13,440 Speaker 3: He's really geared towards helping you understand really three or 276 00:15:13,480 --> 00:15:17,320 Speaker 3: four critical elements. You know, One you're likelihood for success 277 00:15:17,400 --> 00:15:21,440 Speaker 3: or approval to some financial modeling and forecast and give 278 00:15:21,440 --> 00:15:23,480 Speaker 3: you a better understanding of when you should begin the 279 00:15:24,280 --> 00:15:26,600 Speaker 3: process to apply for a home So how long will 280 00:15:26,640 --> 00:15:29,120 Speaker 3: it take you to become a homeowner or to close 281 00:15:29,120 --> 00:15:32,200 Speaker 3: on a home. Three, the best loan product for you 282 00:15:32,880 --> 00:15:35,240 Speaker 3: and then for the lenders within our ecosystem that present 283 00:15:35,320 --> 00:15:37,400 Speaker 3: the best chance of success with them as well. 284 00:15:37,880 --> 00:15:43,960 Speaker 2: So you mentioned a virtual advisor keV virtual meaning it 285 00:15:44,040 --> 00:15:45,840 Speaker 2: is not a guy named keV right. 286 00:15:47,880 --> 00:15:50,400 Speaker 3: It's named after one of my good friends from college 287 00:15:50,440 --> 00:15:53,000 Speaker 3: that passed from a rare form of germ sale cancer. 288 00:15:53,520 --> 00:15:55,520 Speaker 3: He is probably one of the most helpful, friendly people 289 00:15:55,520 --> 00:15:57,440 Speaker 3: that you've ever met, and it didn't matter who you were, 290 00:15:58,200 --> 00:16:01,040 Speaker 3: so we really wanted to encompass his personality into the 291 00:16:01,680 --> 00:16:07,280 Speaker 3: solution itself. But yes, keV it becomes a friend of pal, 292 00:16:07,840 --> 00:16:09,640 Speaker 3: so even if you're not ready to buy, he just 293 00:16:09,680 --> 00:16:12,120 Speaker 3: doesn't pass you off and say, hey, I'm not going 294 00:16:12,200 --> 00:16:15,480 Speaker 3: to help. It really analyzes your profile and begins to 295 00:16:15,520 --> 00:16:19,600 Speaker 3: create a path that you can follow to become a homeowner. 296 00:16:20,560 --> 00:16:24,240 Speaker 1: We have arrived at big tech idea number two. keV 297 00:16:24,560 --> 00:16:30,080 Speaker 1: the Virtual Assistant is built using powerful artificial intelligence tools. 298 00:16:30,400 --> 00:16:34,560 Speaker 1: The AI takes the potential home buyer's information and runs 299 00:16:34,560 --> 00:16:37,960 Speaker 1: it through algorithms that tell you things like how likely 300 00:16:38,000 --> 00:16:40,200 Speaker 1: you are to get a loan, and what loan makes 301 00:16:40,240 --> 00:16:42,640 Speaker 1: the most sense for you, and how long the whole 302 00:16:42,680 --> 00:16:46,560 Speaker 1: process is likely to take. You can ask keV questions 303 00:16:46,880 --> 00:16:49,720 Speaker 1: and it'll give you answers. But keV is more than 304 00:16:49,760 --> 00:16:55,000 Speaker 1: your average responder chatbot. It speaks conversationally it knows who 305 00:16:55,000 --> 00:16:58,800 Speaker 1: you are, understands your needs, and helps beyond just providing 306 00:16:59,000 --> 00:17:02,600 Speaker 1: a frequently asked questions link. Brian says he thinks a 307 00:17:02,640 --> 00:17:05,000 Speaker 1: lot of people might be more comfortable talking with an 308 00:17:05,040 --> 00:17:09,040 Speaker 1: AI powered virtual assistant than with a human loan officer 309 00:17:09,080 --> 00:17:09,600 Speaker 1: at a bank. 310 00:17:10,640 --> 00:17:13,520 Speaker 3: I think it really solves a cultural problem. There are 311 00:17:13,560 --> 00:17:17,639 Speaker 3: cultural barriers that prevent different segments from becoming homeowners or 312 00:17:17,680 --> 00:17:20,640 Speaker 3: at least impact their buying decisions in terms of how 313 00:17:20,680 --> 00:17:22,919 Speaker 3: they explore home ownership. So the first part is to 314 00:17:23,640 --> 00:17:25,760 Speaker 3: try to use as virtual assistant just to make them 315 00:17:25,760 --> 00:17:29,120 Speaker 3: feel comfortable getting into the process of what home ownership 316 00:17:29,119 --> 00:17:31,680 Speaker 3: could look like. And then from there it is about 317 00:17:31,720 --> 00:17:35,159 Speaker 3: preparing them, getting them better qualified so that once they 318 00:17:35,160 --> 00:17:37,760 Speaker 3: are ready to say, hey, I want to come a homeowner, 319 00:17:37,800 --> 00:17:41,320 Speaker 3: I found the house that I love, allowing that transaction 320 00:17:41,520 --> 00:17:44,680 Speaker 3: that process to be a lot smoother and easier through 321 00:17:44,680 --> 00:17:45,639 Speaker 3: the use of blockchain. 322 00:17:45,680 --> 00:17:48,680 Speaker 2: Basically, so when you say cultural, I mean, does that 323 00:17:48,840 --> 00:17:53,840 Speaker 2: include in part race and ethnicity, people who have traditionally 324 00:17:53,880 --> 00:17:57,959 Speaker 2: been excluded from the banking sector, from housing. I mean 325 00:17:58,240 --> 00:18:00,600 Speaker 2: is the dream that sort of AI I can help 326 00:18:00,720 --> 00:18:03,920 Speaker 2: people who've been excluded become more included. 327 00:18:04,200 --> 00:18:06,880 Speaker 4: Yes, most white people have resources. 328 00:18:07,000 --> 00:18:09,960 Speaker 5: They have other friends and family who have gone through 329 00:18:10,000 --> 00:18:13,960 Speaker 5: this process successfully multiple times as opposed to just the 330 00:18:14,040 --> 00:18:17,080 Speaker 5: one time. Within our communities, it's difficult just to find 331 00:18:17,160 --> 00:18:20,760 Speaker 5: the one person that you can discuss this process with, 332 00:18:21,320 --> 00:18:23,840 Speaker 5: and most of the time that one person has gone 333 00:18:23,920 --> 00:18:27,960 Speaker 5: through a negative experience in that right, Brian's parents, I 334 00:18:27,960 --> 00:18:31,040 Speaker 5: have experienced difficulty in the since my parents have experienced 335 00:18:31,040 --> 00:18:34,439 Speaker 5: difficulty in this process too. Isn't until you get to 336 00:18:34,560 --> 00:18:38,160 Speaker 5: our generation where you have family members that have gone 337 00:18:38,200 --> 00:18:42,359 Speaker 5: through this process multiple times and have been successful. So 338 00:18:42,560 --> 00:18:47,240 Speaker 5: when we speak to keV being culturally relevant, it's because 339 00:18:47,720 --> 00:18:52,639 Speaker 5: keV is there to provide you accurate support that historically 340 00:18:52,720 --> 00:18:55,840 Speaker 5: hasn't been available to these marginalized groups. 341 00:18:56,160 --> 00:18:59,960 Speaker 2: Stephen, you mentioned your own families, your and Brian's families 342 00:19:00,440 --> 00:19:04,000 Speaker 2: experience with getting whole loans with the banking system. Do 343 00:19:04,040 --> 00:19:06,679 Speaker 2: you guys mind just talking about that specifically? What have 344 00:19:06,800 --> 00:19:10,520 Speaker 2: been your family's experiences with getting loans? 345 00:19:10,560 --> 00:19:14,760 Speaker 3: Yeah? Please? Yeah? Yeah, I mean back in the subprime 346 00:19:14,800 --> 00:19:17,040 Speaker 3: mortgage crisis, and you know, my mom nearly lost her 347 00:19:17,080 --> 00:19:19,760 Speaker 3: dream home that I thought for. It was primarily because 348 00:19:19,760 --> 00:19:21,280 Speaker 3: we were in an arm even though we should have 349 00:19:21,320 --> 00:19:23,639 Speaker 3: been in a VA loan because she was a military veteran. 350 00:19:23,880 --> 00:19:27,040 Speaker 2: An un arm an adjustable rate loans. Somebody sold a 351 00:19:27,119 --> 00:19:29,199 Speaker 2: loan that was way worse than the mortgage, So it's 352 00:19:29,240 --> 00:19:29,680 Speaker 2: way worse. 353 00:19:29,720 --> 00:19:31,480 Speaker 3: I mean, you know, it started out better just because 354 00:19:31,520 --> 00:19:33,600 Speaker 3: you pay less, but once that interest rate flips, it 355 00:19:33,640 --> 00:19:36,000 Speaker 3: becomes way worse if you're not prepared for it. And 356 00:19:36,040 --> 00:19:39,080 Speaker 3: I think you know, again, when we talk about these 357 00:19:39,119 --> 00:19:41,760 Speaker 3: cultural factors, there's really five that you deal with. There's 358 00:19:41,800 --> 00:19:45,359 Speaker 3: there's cultural itself. So things like the subprime mortgage crisis, 359 00:19:45,400 --> 00:19:48,640 Speaker 3: where African Americans are hurt the most coming out of that, 360 00:19:48,720 --> 00:19:51,320 Speaker 3: you have redlining, reverse redlining, et cetera. 361 00:19:52,119 --> 00:19:55,919 Speaker 2: Red Lining is basically the history of lenders not making 362 00:19:56,000 --> 00:19:58,879 Speaker 2: loans to people in predominantly black neighborhoods. 363 00:19:58,960 --> 00:20:02,439 Speaker 5: Essentially exactly, we're picking which areas they will lend to 364 00:20:02,560 --> 00:20:03,479 Speaker 5: specific groups. 365 00:20:03,640 --> 00:20:06,879 Speaker 2: Yes, and those areas were predominantly white historically. 366 00:20:06,400 --> 00:20:10,040 Speaker 3: And predominantly white yes. So you have those elements. You 367 00:20:10,080 --> 00:20:13,520 Speaker 3: have the economic elements where there's this concept of it's 368 00:20:13,600 --> 00:20:17,600 Speaker 3: just being unattainable for us. You have the psychological elements 369 00:20:17,600 --> 00:20:20,719 Speaker 3: of being misunderstood thinking that the only way I can 370 00:20:20,760 --> 00:20:22,840 Speaker 3: buy a home is having twenty percent down to put 371 00:20:22,840 --> 00:20:25,520 Speaker 3: down towards the down payment, and that's just not true. 372 00:20:26,160 --> 00:20:29,200 Speaker 3: So our ultimate objective is just really to make that 373 00:20:29,600 --> 00:20:31,960 Speaker 3: more attainable for everyone, and it's really for all load 374 00:20:31,960 --> 00:20:35,520 Speaker 3: of modern income borrowers these days, just because with rates increasing, 375 00:20:35,600 --> 00:20:38,560 Speaker 3: with the supply shortages that we have, you know, home 376 00:20:38,560 --> 00:20:40,520 Speaker 3: ownership is really going to become a lot more difficult 377 00:20:40,560 --> 00:20:43,400 Speaker 3: for a lot of people, regardless of their age, sex, 378 00:20:43,440 --> 00:20:43,920 Speaker 3: and race. 379 00:20:45,400 --> 00:20:47,480 Speaker 2: So you have this industry that suffers from a lack 380 00:20:47,520 --> 00:20:51,119 Speaker 2: of transparency, from historical bias in terms of race and gender. 381 00:20:51,640 --> 00:20:54,320 Speaker 2: You start this technology driven company to try and fix 382 00:20:54,359 --> 00:20:56,639 Speaker 2: those things. As you're building the company, how do you 383 00:20:56,640 --> 00:21:03,280 Speaker 2: come to work with IBM. 384 00:21:00,560 --> 00:21:03,480 Speaker 3: Our need for data protection and security? So you're talking 385 00:21:03,520 --> 00:21:10,200 Speaker 3: about digitizing documents, digitizing information to allow greater access to underserved, 386 00:21:10,280 --> 00:21:15,679 Speaker 3: underrepresented groups. And IBM had their Hyperprotect Accelerator which was 387 00:21:15,920 --> 00:21:19,439 Speaker 3: entirely focused on that, taking small startups like ours and 388 00:21:19,520 --> 00:21:22,680 Speaker 3: allowing them to basically run the pilots that we ran 389 00:21:23,400 --> 00:21:27,080 Speaker 3: without having to worry about people's information getting stolen in essence, 390 00:21:27,520 --> 00:21:29,840 Speaker 3: and then Steve and I were just very aggressive in 391 00:21:29,920 --> 00:21:33,800 Speaker 3: terms of just reaching out to different vps, different executives 392 00:21:33,840 --> 00:21:35,760 Speaker 3: at IBM kind of saying, you know, here's what we 393 00:21:35,760 --> 00:21:38,360 Speaker 3: want to do, here's what we need, will you help us, 394 00:21:39,000 --> 00:21:42,880 Speaker 3: And being in an industry that is so regulated, it 395 00:21:42,880 --> 00:21:45,080 Speaker 3: helped us really get through that door, just because you know, 396 00:21:45,359 --> 00:21:49,560 Speaker 3: every bank has a vendor on boarding process that requires 397 00:21:49,600 --> 00:21:53,000 Speaker 3: a very high level of data security to even work 398 00:21:53,000 --> 00:21:53,320 Speaker 3: with them. 399 00:21:53,320 --> 00:21:58,840 Speaker 1: In an essence, here's the third big tech idea in 400 00:21:58,920 --> 00:22:04,000 Speaker 1: the Home Lending Pulse, protecting data in the cloud. Think 401 00:22:04,040 --> 00:22:06,920 Speaker 1: about the problem this one is solving. Brian and Stephen 402 00:22:07,040 --> 00:22:10,400 Speaker 1: have this little startup. They need to collect super sensitive 403 00:22:10,440 --> 00:22:13,160 Speaker 1: data from people. Everything you have to show the bank 404 00:22:13,400 --> 00:22:16,560 Speaker 1: when you want to get a mortgage, This data has 405 00:22:16,640 --> 00:22:21,679 Speaker 1: to be secure. IBM's hyper Protect Accelerator enables small businesses 406 00:22:21,920 --> 00:22:24,960 Speaker 1: to store sensitive data in the cloud and keep that 407 00:22:25,080 --> 00:22:29,119 Speaker 1: data secure. Brian says, it lets home Lending Pal do 408 00:22:29,280 --> 00:22:31,200 Speaker 1: something they would never do on their own. 409 00:22:32,720 --> 00:22:35,960 Speaker 3: From a technical perspective, you have different compliance checks that 410 00:22:36,000 --> 00:22:38,359 Speaker 3: you have to meet to work with banking institutions or 411 00:22:38,359 --> 00:22:42,760 Speaker 3: financial institutions. So it allows a small startup like home 412 00:22:42,880 --> 00:22:45,320 Speaker 3: Lending Pal to still be able to meet those checks 413 00:22:45,320 --> 00:22:49,679 Speaker 3: and balances. To bring an innovative solution to the table 414 00:22:49,720 --> 00:22:52,959 Speaker 3: for a financial institution, where more than likely as a startup, 415 00:22:52,960 --> 00:22:54,399 Speaker 3: you're not going to have the ability to do that 416 00:22:54,440 --> 00:22:56,480 Speaker 3: on your own, just because it is so expensive to 417 00:22:56,520 --> 00:22:59,159 Speaker 3: either have internal servers or to try to do it 418 00:22:59,200 --> 00:22:59,920 Speaker 3: on your own as well. 419 00:23:00,080 --> 00:23:03,600 Speaker 2: So people have to trust you to use home Landing 420 00:23:03,640 --> 00:23:06,560 Speaker 2: pow right, Like, I'm giving you everything, how do you 421 00:23:06,720 --> 00:23:09,320 Speaker 2: convince me? How do you convince customers that you're going 422 00:23:09,359 --> 00:23:11,359 Speaker 2: to keep their data safe? 423 00:23:11,880 --> 00:23:15,240 Speaker 3: Absolutely? Part of it is doing stuff like this where 424 00:23:15,240 --> 00:23:18,920 Speaker 3: we're acknowledging and making the consumers aware of our relationship 425 00:23:18,920 --> 00:23:21,680 Speaker 3: with the IBM and how IBM is handling our storage 426 00:23:21,680 --> 00:23:24,639 Speaker 3: of the data and the sensitive data itself. Technically, the 427 00:23:24,680 --> 00:23:28,520 Speaker 3: IBM description of it is their confidential computing services or 428 00:23:28,520 --> 00:23:31,159 Speaker 3: cloud services, and it's basically saying that even though the 429 00:23:31,160 --> 00:23:34,119 Speaker 3: information is stored in the cloud, IBM is going to 430 00:23:34,119 --> 00:23:36,680 Speaker 3: do a lot to help Home Lending Pal protect this 431 00:23:36,800 --> 00:23:39,480 Speaker 3: sensitive data. Part of it is being able to show 432 00:23:39,520 --> 00:23:42,880 Speaker 3: IBM's logo on our website. You'll be surprised how much 433 00:23:42,920 --> 00:23:46,920 Speaker 3: logo recognition helps people understand that this is a legit business, 434 00:23:46,960 --> 00:23:49,880 Speaker 3: a legit company, if you will. And then there's also 435 00:23:49,920 --> 00:23:53,360 Speaker 3: stuff like you know, people seeing the address of the business, 436 00:23:53,440 --> 00:23:56,160 Speaker 3: contact information for the business. Like, all this stuff factors 437 00:23:56,200 --> 00:23:58,960 Speaker 3: into why people would be willing to give us their data. 438 00:23:59,080 --> 00:24:00,679 Speaker 3: But a lot of that is very contingent on just 439 00:24:00,680 --> 00:24:02,920 Speaker 3: people seeing the IBM logo and saying that, hey, you 440 00:24:03,000 --> 00:24:05,320 Speaker 3: know we can if we don't trust home Lending Pod, 441 00:24:05,320 --> 00:24:07,960 Speaker 3: we definitely trust IBM with this aspect of the business. 442 00:24:08,480 --> 00:24:11,320 Speaker 2: So what is the sort of story of working with 443 00:24:11,440 --> 00:24:14,160 Speaker 2: IBM on this? I mean, did you just figure out 444 00:24:14,200 --> 00:24:16,120 Speaker 2: that they had the thing you need? Or did they 445 00:24:16,119 --> 00:24:18,680 Speaker 2: sort of work with you to build the thing you need? 446 00:24:20,000 --> 00:24:24,040 Speaker 3: We told them what we wanted. I think there's a 447 00:24:24,560 --> 00:24:28,720 Speaker 3: certain special relationship that we have with IBM. As I mentioned, 448 00:24:28,720 --> 00:24:31,960 Speaker 3: you know, Steve and I are very aggressive of internally 449 00:24:32,000 --> 00:24:34,879 Speaker 3: and externally in terms of getting things changed in this industry, 450 00:24:34,960 --> 00:24:38,040 Speaker 3: especially when we talk about systemic change, and sometimes that 451 00:24:38,080 --> 00:24:41,200 Speaker 3: requires you to make very big ask, you know, swing 452 00:24:41,240 --> 00:24:44,679 Speaker 3: for the fences and see what happens. And as we 453 00:24:44,800 --> 00:24:47,399 Speaker 3: found out more, as we hired better talent, as we 454 00:24:47,480 --> 00:24:50,000 Speaker 3: understood more of what we were trying to do, it 455 00:24:50,040 --> 00:24:51,920 Speaker 3: made it a lot easier for us to really share 456 00:24:51,960 --> 00:24:55,639 Speaker 3: this vision with IBM. And then now they're able to 457 00:24:55,680 --> 00:24:58,040 Speaker 3: recommend products to say, we see you're trying to do 458 00:24:58,080 --> 00:24:59,879 Speaker 3: it this way, but maybe you want to use our 459 00:25:00,000 --> 00:25:02,199 Speaker 3: internal product and do it with this instead, And so 460 00:25:02,240 --> 00:25:03,840 Speaker 3: that makes it a lot easier for us to try 461 00:25:03,840 --> 00:25:06,960 Speaker 3: to bring artificial intelligence and blockchain to an industry that 462 00:25:07,080 --> 00:25:13,359 Speaker 3: hasn't historically accepted new technology that well. 463 00:25:11,840 --> 00:25:14,320 Speaker 2: So where are you in your journey as a company. 464 00:25:14,760 --> 00:25:17,360 Speaker 2: I know you're still sort of working on it. What 465 00:25:17,440 --> 00:25:19,520 Speaker 2: can customers do now with your product? 466 00:25:21,200 --> 00:25:26,879 Speaker 5: They can get recommendations right now, we're fully licensed Colorado, 467 00:25:27,000 --> 00:25:31,719 Speaker 5: Florida and in North Carolina, So right now, customers from 468 00:25:31,840 --> 00:25:34,000 Speaker 5: those dates can expect to be connected with a lender 469 00:25:34,520 --> 00:25:39,879 Speaker 5: with full guidance as to what exactly they're getting into 470 00:25:39,960 --> 00:25:44,000 Speaker 5: and what pricing expectations they ought to be presented with. 471 00:25:44,840 --> 00:25:47,199 Speaker 2: Have you heard back? I mean, I know that this 472 00:25:47,280 --> 00:25:49,400 Speaker 2: is kind of a weird question, given that the whole 473 00:25:49,440 --> 00:25:52,919 Speaker 2: point is that people can be anonymized, But are you 474 00:25:52,960 --> 00:25:56,160 Speaker 2: able to talk to your customers? Haven of your customers 475 00:25:56,560 --> 00:25:58,440 Speaker 2: told you about how it's helped them. 476 00:25:58,760 --> 00:26:02,000 Speaker 3: Surprisingly, a lot of our customers will reach out to 477 00:26:02,080 --> 00:26:04,080 Speaker 3: us and give us the use cases. We've had local 478 00:26:04,119 --> 00:26:07,120 Speaker 3: TV interviews where they've interviewed them about those success stories. 479 00:26:07,680 --> 00:26:10,080 Speaker 3: We'll have customers that reach out to us with challenges 480 00:26:10,119 --> 00:26:11,760 Speaker 3: that they're having and hoping that we can help them 481 00:26:11,800 --> 00:26:14,919 Speaker 3: through those, even if we have to manually connect a 482 00:26:14,960 --> 00:26:17,439 Speaker 3: borrower to a lender and a state that we don't operate, 483 00:26:17,480 --> 00:26:20,240 Speaker 3: and we're more than happy to do that. In exchange 484 00:26:20,240 --> 00:26:23,200 Speaker 3: for that, they're basically helping us build out this new process. 485 00:26:23,280 --> 00:26:25,679 Speaker 3: And so that's kind of the beauty of the system 486 00:26:25,800 --> 00:26:28,840 Speaker 3: is that you customers are coming in at all stages 487 00:26:28,880 --> 00:26:32,119 Speaker 3: of the buying cycle. You have some that are still renting, 488 00:26:32,119 --> 00:26:34,959 Speaker 3: at that daydreaming phase where they're really trying to understand, 489 00:26:35,000 --> 00:26:37,280 Speaker 3: you know, is home ownership a feasible option for me? 490 00:26:37,680 --> 00:26:39,800 Speaker 3: And then you have some that are, you know, trying 491 00:26:39,800 --> 00:26:42,840 Speaker 3: to test out new features like optimal character recognition software 492 00:26:42,880 --> 00:26:44,960 Speaker 3: where they're able to upload documents and see how those 493 00:26:44,960 --> 00:26:48,399 Speaker 3: documents transfer to lenders. So I really think that is 494 00:26:48,440 --> 00:26:51,080 Speaker 3: the beauty about what we're building, is that the people 495 00:26:51,119 --> 00:26:52,440 Speaker 3: have helped us build it so far. 496 00:26:53,640 --> 00:26:56,600 Speaker 2: Are there any particular stories you've heard from customers that 497 00:26:56,640 --> 00:26:57,320 Speaker 2: have stayed with you. 498 00:27:00,280 --> 00:27:02,119 Speaker 3: I think the one that's most relevant to me, that 499 00:27:02,240 --> 00:27:04,199 Speaker 3: sticks closest to my heart is my mom. You know, 500 00:27:04,280 --> 00:27:06,879 Speaker 3: she was looking to try to buy another house, and 501 00:27:06,920 --> 00:27:09,440 Speaker 3: we were able to get her approved for a little 502 00:27:09,440 --> 00:27:11,320 Speaker 3: bit over six hundred and fifty thousand, which was about 503 00:27:11,320 --> 00:27:13,919 Speaker 3: fifty thousand more than what she had heard from anyone 504 00:27:13,960 --> 00:27:16,800 Speaker 3: else in the area. So, you know, we've really been excited. 505 00:27:16,840 --> 00:27:18,919 Speaker 3: At least I had really been excited about that one. 506 00:27:19,160 --> 00:27:21,440 Speaker 2: That's great. You're not going to do better than your mom. 507 00:27:21,600 --> 00:27:24,199 Speaker 3: Right your mom me. That was the whole reason I 508 00:27:24,240 --> 00:27:26,679 Speaker 3: built the systems, you know, So that one really sticks 509 00:27:26,680 --> 00:27:29,639 Speaker 3: closest to me is because we've had some users that 510 00:27:29,680 --> 00:27:32,040 Speaker 3: have gone through the entire process and have helped us 511 00:27:32,040 --> 00:27:35,199 Speaker 3: go from our initial phase and we've really been launching 512 00:27:35,200 --> 00:27:38,119 Speaker 3: in phases where at first it was more show just 513 00:27:38,160 --> 00:27:40,480 Speaker 3: showing the affordability amount, like you know, what was the 514 00:27:40,520 --> 00:27:42,919 Speaker 3: amount of home that you could afford? And now, as 515 00:27:42,920 --> 00:27:47,000 Speaker 3: Steven mentioned, we're getting into this much more interactive conversational 516 00:27:47,080 --> 00:27:50,040 Speaker 3: dialogue where consumers are not only showing kind of what 517 00:27:50,080 --> 00:27:52,600 Speaker 3: they want to buy, but also getting into kind of 518 00:27:52,600 --> 00:27:55,560 Speaker 3: what their feelings are, what is what are their sentiments 519 00:27:55,560 --> 00:27:58,200 Speaker 3: that they're looking for in a potential relationship with they lender. 520 00:27:58,960 --> 00:28:01,640 Speaker 3: So we're really excited and consumers come in and they 521 00:28:01,680 --> 00:28:04,119 Speaker 3: test new features and they say, hey, this is working, great, 522 00:28:04,160 --> 00:28:06,960 Speaker 3: this isn't working you know what about this? And we 523 00:28:07,000 --> 00:28:09,720 Speaker 3: think that's really going to lead into our series A 524 00:28:09,840 --> 00:28:11,360 Speaker 3: raise here in the next couple of months, where we'll 525 00:28:11,359 --> 00:28:13,720 Speaker 3: go out and raise hopefully eight figures are more to 526 00:28:13,800 --> 00:28:16,320 Speaker 3: really flush out the features that consumers have said they 527 00:28:16,320 --> 00:28:18,760 Speaker 3: wanted the most. Is really what we're most excited about. 528 00:28:20,760 --> 00:28:23,200 Speaker 2: What's your dream for home lending pal If you think whatever, 529 00:28:23,200 --> 00:28:25,080 Speaker 2: I don't know, five years in the future, ten years 530 00:28:25,080 --> 00:28:26,679 Speaker 2: in the future, where are you. 531 00:28:27,119 --> 00:28:29,000 Speaker 3: I want to see at least a million people, hopefully 532 00:28:29,160 --> 00:28:33,520 Speaker 3: a million minorities become homeowners by utilizing our product. You know, 533 00:28:33,560 --> 00:28:36,399 Speaker 3: we operate in an industry that's very lucrative for a 534 00:28:36,400 --> 00:28:39,720 Speaker 3: lot of people. Having supportive IBM will hopefully help us 535 00:28:39,720 --> 00:28:42,400 Speaker 3: from a business perspective. But I don't want us to 536 00:28:42,440 --> 00:28:44,560 Speaker 3: lose sight of our social impact goals and the things 537 00:28:44,600 --> 00:28:47,080 Speaker 3: that were really set out before, which was to make 538 00:28:47,120 --> 00:28:49,240 Speaker 3: the process more agguitable for everyone. You know, I think 539 00:28:49,240 --> 00:28:52,520 Speaker 3: if we were to be acquired or to do an 540 00:28:52,560 --> 00:28:55,240 Speaker 3: initial public offering in five years and we're not doing that, 541 00:28:55,320 --> 00:28:57,600 Speaker 3: then for me, it would not be as sweet as 542 00:28:57,640 --> 00:29:00,480 Speaker 3: if it were. To ensure that we're actually doing stuff 543 00:29:00,480 --> 00:29:03,880 Speaker 3: to close the gap for people. Thank you guys so 544 00:29:03,960 --> 00:29:05,720 Speaker 3: much for your time. I really it was great to 545 00:29:05,760 --> 00:29:06,200 Speaker 3: talk with you. 546 00:29:07,000 --> 00:29:09,280 Speaker 4: Pleasure Well, thank you absolutely. 547 00:29:15,960 --> 00:29:18,880 Speaker 1: Malcolm Glavell. Here to end today's show, I want to 548 00:29:18,880 --> 00:29:21,760 Speaker 1: talk about someone who we didn't hear from in the interview, 549 00:29:22,040 --> 00:29:26,960 Speaker 1: but who we heard about, Brian's mom, because her story 550 00:29:27,160 --> 00:29:30,440 Speaker 1: really is the story of home Lending Pal. Remember how 551 00:29:30,480 --> 00:29:33,080 Speaker 1: Brian told us that back in the odds, his mom 552 00:29:33,160 --> 00:29:35,920 Speaker 1: got that crappy mortgage, the one that left her paying 553 00:29:36,120 --> 00:29:39,440 Speaker 1: higher interest rates than she should have been paying. That 554 00:29:39,560 --> 00:29:42,680 Speaker 1: happened to a lot of people, particularly people of color. 555 00:29:43,360 --> 00:29:46,560 Speaker 1: It was that story and others like it that really 556 00:29:46,680 --> 00:29:50,160 Speaker 1: inspired Brian to team up with Stephen to build Home 557 00:29:50,240 --> 00:29:54,000 Speaker 1: Lending Pal. They wanted to fix a home lending system 558 00:29:54,240 --> 00:29:59,040 Speaker 1: that had been opaque and unfair basically forever. Most people 559 00:29:59,080 --> 00:30:02,760 Speaker 1: applying for mortgage aren't thinking about the technology that's behind 560 00:30:02,760 --> 00:30:05,760 Speaker 1: the scenes. We all just want a good mortgage with 561 00:30:05,840 --> 00:30:10,360 Speaker 1: fair terms. And because Brian and Stephen made creative use 562 00:30:10,440 --> 00:30:16,080 Speaker 1: of IBM technology using AI, blockchain, and cloud to rethink 563 00:30:16,200 --> 00:30:20,200 Speaker 1: the home loan process, that is now possible for all 564 00:30:20,240 --> 00:30:26,400 Speaker 1: of us. On the next episode of Smart Talks with IBM. 565 00:30:26,680 --> 00:30:30,040 Speaker 1: As AI becomes more widespread, how do we ensure that 566 00:30:30,120 --> 00:30:35,520 Speaker 1: it is built and deployed responsibly? We talk with Phedro Bonadius, 567 00:30:35,880 --> 00:30:42,680 Speaker 1: trustworthy AI practice leader within IBM Consulting. Smart Talks with 568 00:30:42,720 --> 00:30:47,719 Speaker 1: IBM is produced by Molly Sosia, Alexandra Garatin, Royston Preserve 569 00:30:48,080 --> 00:30:52,680 Speaker 1: and Edith Russolo with Jacob Goldstein. We're edited by Jen Guera. 570 00:30:53,040 --> 00:30:57,400 Speaker 1: Our engineers are Jason Gambrel, Sarah Buguier and Ben Tolliday. 571 00:30:58,040 --> 00:31:02,760 Speaker 1: Theme song by Gramoscope. Special thanks to Carli Migliori and 572 00:31:02,920 --> 00:31:06,040 Speaker 1: e Kelly, Kathy Callahan and the eight Bar and I 573 00:31:06,200 --> 00:31:09,920 Speaker 1: b M teams, as well as the Pushkin marketing team. 574 00:31:10,320 --> 00:31:12,640 Speaker 1: Smart Talks with I b M is a production of 575 00:31:12,760 --> 00:31:17,760 Speaker 1: Pushkin Industries and iHeartMedia. To find more Pushkin podcasts, listen 576 00:31:17,840 --> 00:31:22,240 Speaker 1: on the iHeartRadio app, Apple Podcasts, or wherever you listen 577 00:31:22,440 --> 00:31:27,520 Speaker 1: to podcasts. I'm Malcolm Gladwell. This is a paid advertisement 578 00:31:27,880 --> 00:31:37,120 Speaker 1: from ib M.