Is Gender Bias Affecting Your Approvals? With Tracy Hazzard From the Get Fundable Podcast With Merrill Chandler

So the big question is, how are entrepreneurs and real estate investors like us, ones who want to grow our businesses and who are tired of paying for really expensive alternative lending? How do we tap into the most inexpensive money available and do it without the hassle of typical borrowing? That is the question, and this podcast will give you the answers. I'm Merrill Chandler, and welcome to the Rufable Podcast.

Welcome, everybody. Merrill Chandler here, your host of Are youe Effable? And we're going to be talking about one of the most significant effable subjects out there. Privacy bias. Leaning in or leaning out. Do how do algorithms support all of this AI technology? How do algorithms support our approvals? Right. We're all about being affable. Means being fundable, being getting approval. So we'll be right back and we're gonna do a deep dive with Tracy Hazard. I am a fan of this woman, like four different platforms. We'll discuss why when we come back. See you on the inside.

Everyone. We are back. Okay. This is crazy. I am so excited to have this episode. First of all, it's my inaugural long distance interview episode, so I'm brand new at this one. First of all, I'm so excited to have Traci Hazard. Now I'm going to let her introduce herself because she has so many amazing accomplishments in her life. That's the reason I'm a fan. I'm not a fan of a lot of people, but this woman, I'm all in. So I'll let her introduce herself in just a moment. But this episode is about conversations we had from a Facebook Live where I went off. It was one of my biggest rants about how human beings filter in their experience. And we're going to be talking a little further upstream about how algorithms are designed with these filters. Right. With this bias. And we had overlapping views. We shared some things and we disagreed on others, which I'm always into. So welcome. Tracie, I am so happy to have you here.

Thanks, Meryl. I'm excited to be here. It's such a pleasure to get to talk about something that I'm as passionate about as you are for different reasons, but still passionate about it. You know, I love that. So you asked about my background, and reality is that most of what we're going to talk about today is relevant because I write a column for Inc. Magazine. It's about to be my fifth year writing it.

Congratulations.

Thank you. I've written about because my whole career has been focused on disruptive and evolving technologies. And so I write about these things like AI, like 3D printing, like blockchain stuff most people have no grasp of or understanding about how they get started and where they go, virtual reality, augmented reality, all of these things. And I see them at the very early stages and start to review them and understand them and say, is there an opportunity here? Is something going to happen? Is this really going to go mainstream? That's usually a viewpoint people expect from me and expect from my column and then I get to comment on them. So I also have podcasts about this. I have a podcast on blockchain called the New Trust Economy. I have a podcast for podcasters, as you know, called Feature Brands, which is how we knew each other from other things too. But. And then my very first podcast was on 3D printing and watching a technology, an industry that never did go mainstream like it was projected to go. And so that's where I bring to it this viewpoint that sometimes these things happen in there. But because I deeply know and understand consumer buying habits, because that's my actual long term career expertise. 27 years. Yeah, for 27 years I ghost design products that you buy every day at mass market retail. And so I understand the consumer buying behavior, especially the pattern and habits of women who consume every single product category, including finance. They choose the banks, they choose the credit cards, they choose all of those things. So including the buy habits in finance, real estate, like all types of categories are mostly driven by women. And it's up over over 80 to 90% in some categories that is being driven by the way women think, right? Buying decisions.

Do you guys see already why I love this woman? Right? She's like a professional professional.

She knows this shit inside and out. So first things first, I want to weigh in that it is an absolute fact the most recent numbers we have is 2017. But 65% of all mortgages that are engaged in the United States in 2017, 65% were driven by women. Either women or women in the household as the lead. It could even be to a joint mortgage, but the woman's name is on top. 65%.

Wow, that's bigger than I thought it was. So that's great.

65%. And then when it comes to borrowing in non mortgage relationships, depending on the metric you use for 2017, because there's credit cards, there's all kinds, but it is between that same 65% and 80% of all debt consumption is engineered and the senior applicant is a woman. So not just the buying decisions, but actually executing on the purchase. Right? Engaging the credit instrument that's going to be that. Yep. And then, of course, then they decorate it and then how do you use it?

Right, exactly. So, you know, I mean, this is the thing that, you know, I spent my entire career focusing and understanding on that. And it. And most people don't want to listen to it. Like, the last thing my clients, when I was designing products for Costco wanted to hear, like, this is going to sell better to women. They didn't want to hear it. Which I thought was the most ridiculous backwards thing ever.

Talking about bias.

Yeah, because why shouldn't you care? They would say, we don't care about niche markets. We sell to anyone. And I was like, it's 86% of your sales memberships are driven by women.

This is not a niche.

Right. And that's what your numbers are showing too. Right?

Yeah, completely.

Right.

First things first, I want to do a deep dive into the actual tech development, but I want to talk about kind of your views on the philosophy of bias. Now, I call them filters. In all of my work, in my writing, the book that my listeners are aware of, the New F Word, is about a new paradigm in borrowing. Right. How borrowers can be a professional borrower rather than just some consumer and then be predated upon by lenders because they don't know what they're doing. Right. But what are your thoughts on just in the human experience of filters, how we're raised, the prejudgments we make, our prejudices, which is the fancy word for prejudgment, judging not on the. On the facts, but on some preconceived notion. What are your. What philosophy? Just big picture, far upstream, about filtering. Just in the human experience.

So I think it's natural. It's part of our nature. It's part of our. The way humankind has evolved and developed. It's. I mean, I think about it like when our kids, an older daughter, two young ones, and you watch them when they're around age 3 to 5, and they go in this. In this age where they want to play with. My daughter's a blonde, not like their mom, but they only want to play with the blonde girls. They don't want to play with the boys and they don't want to play with brunettes. They want to play with the blonde girls. And my daughters go to school out here in Orange county, and there's very few blondes, actually, which is ironic. And so because everyone's from somewhere, it's really ironic. And everyone's from somewhere else, so they feel like the odd girl out, and they're like, I don't have anyone to play with. And so I said, so we have to say. And I said, well, how many girls have brown eyes? Because you have brown eyes? So categorization is at a basic level of sorting and learning about the world. So to think that we don't carry that into adulthood is just stupid, right? I mean, it's not gonna happen. We're carrying that all the way through now as parents. Hopefully we're doing a much better job of saying, like, this is not appropriate behavior.

Inclusive.

Inclusive. Let's talk about what that looks like, right? But it doesn't change the difference that our human nature is. We need to categorize and box things because we have to filter information quickly. That's our job. And it's the job of those algorithms out there to filter the people quickly, right? We want to get to an answer as quick as possible.

See, that's why I want to ask that upstream question, right? Because if we do not take in that, we start filtering our lives. Right? And the reason why I bring this in is I believe we filter to create safety. Anything that's different from us is either unknown or dangerous. And dangerous may be bad, right? We have to train ourselves consciously to understand that different is actually good. It actually expands our capabilities of life. I bring this up because when we start talking about software, we're codifying prejudice by. I mean, we're actually hardwiring it in, and we need. And so I won't take your argument. Tell us a little bit.

It's what I call designed in. And my column is called by design. Right? Like, all of this gets designed in. So really early on in my career, I was lucky to be working with some great researchers who were doing this research with NASA and with color, pattern and texture, which sounds like the weirdest thing ever. But what I learned from it was is that our minds, our eyes, everything that we see, everything that we take in is filtering and building out. No different than we were when we lived on the savannah and we had to worry about what we could eat and whether the lions were going to eat us. Right? Like, we still operate in that at the base level of our brain.

Our lizard brain.

Our lizard brain, Right. And we take in information that way. So if we don't build our environments, if we don't build our systems, if we don't build that to just allow for that to happen, then we already are in resistance to that. So the studies that we were doing on color is that when we have these gray, bland environments, like out in the space station all alone. And it's all white and silver. Right, right. And chrome, actually. It causes extreme stress level because it causes you to go into this. I don't know what's good or anything like that or what's bad. And every time I turn around, a big red light starts flashing. Right. So like that.

Warning, Will Robinson. Warning, Will Robinson.

Exactly. So if we just put some texture in there, color in there, our eyes and our brains can actually calm down and sort of say, okay, there's nothing standing out an alarm in this space, and only where it's supposed to alarm me. So I do something about it, so I don't decompress or whatever that is. Then I do something about it. So that's where we don't build that in often enough. We don't think that through that. We just have to accept this filtering is going on because it's a survival technique. That's why we've been around.

We made it so far.

We made it so far.

I don't know. I can't predict long in the future, but so far we're doing okay. I love it. I can totally see NASA engineers you calling it monochromatic distress. Right?

Exactly.

It's a psychological condition or something.

Well, it's what we actually call discognizance. So, like, it's upsetting or upsetting the cognitive abilities in our brain when we're at that level. So sometimes we call it resonance. Right. So it's when it's not resonating, you know the difference because it's caused this heightened prickliness that you feel. And so it's a good thing. Then we know we're supposed to be. We need to be safe about something, but it's also a bad thing if it's constantly occurring.

Got it. So let's move this into. Since this is a RUF podcast, how does by design bias. And let me make a statement, bias is not in and of itself bad. There are no true good things and bad things except what the intention behind it.

Right. And so, yeah, we can't label it that.

Yeah. So bias is not bad. It's either discriminatory or harmful. Bias would be what is bad. Right. So talk to me for a minute about. Let's give everybody a background. So I went on a rant because this whole Apple and Goldman Sachs thing came out, and there was a gentleman who did a blog. Popular or a tweet? And he was very popular. And he basically said, hey, something's wrong. Because I got, what was it, 20 times the limit that my wife did. But we share the same finances, we share the same in household income, we share all of these things. This is discriminatory.

Right. I like you. My first reaction was not necessarily. They don't necessarily know what goes into the formula because as I learned from you and from your funding hackers program and from your bootcamp, because I just in full disclosure here, I'm one of Meryl's clients and I love it. But what I've learned from that process is that there are so many factors. Right. And most people don't have that nuanced understanding. Where it raised the red flag to me was when I saw Steve Wozniak and his wife come forward and Steve.

Co founder of Apple.

Co founder of Apple. Right. And Woz says, I got 10 times what my wife got. And if you know California, it's a joint property state, they've been married much long enough to pass that period, which is 10 years or seven years or something like that. And so that shouldn't be the case. I can't imagine that just as much stuff in her name as his name because of the kind of guy he is. And I've met him and I've interviewed him and I've read his book and.

He looks like a teddy bear. And significantly is.

Yeah, exactly. And so. And he also understands how these algorithms could build and how this stuff happens. And he's saying this doesn't seem right. And so whether or not there is actual true, true results in there, discriminatory bias. Right. That still remains to be seen. But what it does is it raises the flag on there probably is something wrong. And so here's where I looked at that. I look at that and say the issue that most companies can't handle, or most coders, if you want to call it that, just at that base level is the unconscious bias that's put in. So you develop a system and you lived in a world where women were property. You have bias built into how you build the algorithm because you don't have that same sort of American acceptance of equality that we do.

Let's create the framework for what she just said. Remember, the outsourcing of coding is really popular in second world and third world countries. They're extremely intelligent, but again, they have not adopted all of the Equal Opportunity act, equal opportunity lending, equal opportunity, etc. Right.

So property ownership. Right. It's different.

Yeah. There may be to put a super fine point on a patriarchal bias to the system that doesn't allow in their subconscious, in their unconscious mind, there may be some version of discrimination. And so really far upstream at the coding level.

Right. But it can also happen in the planning level. So obviously, somebody is, like, sourcing out this coding or these things are happening, but they're also planning it. And when you plan it, because you live in this world with your own filters and your own ideas about the way things are, you don't always think, oh, I have to say that it's understood. The understood things maybe are not understood across cultures, across genders. Like, it doesn't really happen. And we know that there's a significantly lower amount of women, especially white women, in coding positions, black women as well. And these are a large portion of what you're talking about as the lending community. Right. And so without that, they're not represented at the base level in the coding. So there isn't an equality of perspective or a broadness of perspective being represented in how things are developed to begin with. And then also at that understood planning level, we again, have not enough women at a high enough level or in planning positions, especially in technology companies, but in finance companies as well. And so we don't get them involved in the process. So they're not putting in and saying, hey, I need to question our understanding of these points of what they are. So we're developing a code based on maybe a flawed plan with cultural differences and gender differences already inherent in the team we've chosen to build the code.

Yeah.

Now, the third factor of it. Right. And so now then the third factor of it is that we don't really fully test the failure, because we can't. Because in the process, you can't test the failure. So if they were, in fact, testing out every single one of the women who had failed in that group, Steve Wozniak's wife, the other guy's wife. And if they tested out all of these failures and said, now let's put them through general underwriting like we would have normally done, and wow, they shouldn't have failed through the process, then we have a problem with our. We have a code problem because we're actually excluding a community we could be making great money off of. Like, why would we want to do that?

Like, that doesn't make sense. Exactly. That was my exact point, is saying. And I came at it differently. I said, if 65 to 80% of buying decisions are women, then why would we consciously do so? And your countermanding argument was we wouldn't consciously. Because we want to be profitable we may unconsciously.

Right, right.

And that's where I told sky, my producer, like we got to get her on because we need to discuss what the conscious. Because it sounds like it feels like the vitriol out there is discrimination. And one of the things that, and.

I don't, and I don't always jump on that bandwagon like that's not my first reaction to it. While there's probably something going on here, but it's actually hurting the company, it's hurting Apple and Goldman Sachs just as much as it's hurting the potential consumer base out there. And it's not just the perception they're being discriminatory. They're probably excluding really great people to lend to because women are known for higher pay, for paying their debt faster, for less bankruptcies. Right. For all of those things. So it's a better lending group.

Well, and I even made the argument, I'm saying first of all, Goldman Sachs, Apple, Goldman Sachs is a tier two and it's an 80%. So you don't want the damn card in the first place. Whether or not it's discriminatory, you don't want that.

That's why I didn't rush out to get one. So.

Right, so this is a moot point for all of us funding hackers.

That's right. Don't go get one. That's not what we're doing.

That isn't what you want, even if it were perfectly designed. One of the things that I wanted to share with you was I just came back last week of course from fico and everybody's been a part of this conversation. It was awesome. FICO actually had a class for high level designers and engineers and it's called bias in. Bias out. Exactly what we're talking about. If there's bias in, of course, then the algorithms are going to deliver biased results. How to fight biases in AI and create trust. That's FICO, the name of FICO's thing, which of course they're cutting edge. But alarming headlines appear at increasing rate about biased AI leading to non transparent decisions, discrimination, a lack of trust. Then they say newcomers to application development are confronted with multiple traps and can inadvertently lead to biased and unreasonable automated decision. By comparison, FICO credit score developers have decades of experience in the art and science of mitigating biases through transparent and credible models. So then they go to show these very things, how the FICO algorithm has checks and balances. Now I don't know if Goldman Sachs, Apple uses FICO as one of Their models.

Yeah, we don't know for sure either way.

It sounds like not, but who knows, right?

And one of the things I wanted to bring up is that I would love to do. In fact, I have not done so yet, but I would like to invite the Twitter guy and his wife. I would love to do a fundability analysis and actually put them through and say, here's what they evaluate and do a fundability analysis. Because the very things they say, like, our checking account is shared, our assets are shared, are not fundability metrics by AI software. So I would love to do a compare contrast to show whether there's a bias. I can't say that that would remove all the bias, but I could at least compare it to what we already know, our fundability metrics. Right.

I think that would be wonderful. Yeah, you should definitely do that.

Invite them and say, hey, let's do this. I'll Venmo you get the cost of the credit report.

Get Steve Wozniak and his wife, for that matter. Go for it, Meryl.

Hey, let's do Steve Wozniak sky. We want to make sure that we.

Have let's reach for the stars.

We want to go balls out people when it comes to this stuff. And here's the great thing. If I put them through those metrics, we will know to a significant degree, bias included, because all of the things that we measure, we know are measured by automatic underwriting systems. We know that now, not the nth degree. I don't have fico's code, I don't have lenders code. But I do know if there's the plus or minus 5%, then, yeah, there was bias. But if there is a huge difference, then, okay, guys, you're measuring the wrong things and you're incensed about the wrong things. Right? And that's where our education increases, right?

Yeah. So this is the thing. What FICO is talking about is brute force testing. FICO has more data than anyone else. So they have more pieces of information in which to test out any algorithms and understand whether or not it's filtering out the wrong people or the right people. Right? Because they can follow someone all the way through their life and then say, okay, and then they declare bankruptcy or then this happens, or then they default. Like, so they can see failure in that process as much as they can see success in this process. So as we said before, the algorithm set at Goldman Sachs and at all of those other places are set to create profitability, right? So it's set for a narrow margin of people that they want to that'll give them the least amount of risk and the most amount of profit. Right. So, yeah. And so that's their goal. So they probably set out some sort of metric in the processes saying we expect to have X percent of the people coming through the approval process achieving this kind of rate and this kind of credit limits. And when the algorithm achieves that, they don't try harder to find out about all those other people.

Green light. This works.

Yeah, we're like, it's working and it's way faster than our other. So their model of measurement, too, is about speed. So we're pumping through more people in this automatic underwriting than we could ever pump through an underwriting before. And it costs us a whole lot less because the underwriting had a lot of team and training and support and all of that. Right. So their measurement is also like our profitability as a company is way up because we aren't supporting all of this underwriters. So they're not also questioning, saying, could we be making more money? So they're not asking that question. So it's good enough.

Is good enough. Not great, not great.

And so they're not asking this question is, who are we leaving behind? Where's the opportunities? Not yet. At some point they will, but this is too early. And especially in this day of Apple and Goldman Sachs just launching this card recently. Right? So it's too soon. So they haven't done enough what we call brute force testing. Right? And when you brute force test a code, when you brute force test any kind of algorithm enough, you have to have a way to test the failures. You have no view, because brand new to you, brand new to your company, you have no model for how you used to do it before. You have even less ability.

Did everybody hear that? You're not comparing apples and apples. You're not saying, oh, we tend to apples. And we're not saying that. If I'm hearing you, your argument is that we don't take 10,000 manual and see what the failure rate is. We look at the profitability of the acceptance rate, right, because it costs so much less. But we're not looking for, oh, 82% manually succeeded in getting approved through manual underwriting. They don't push until they get 82% approval rates. Over here, 42% is enough because it's 10 times more profitable because automatic underwriting is 3 seconds and a lot of code. No human beings, no evaluation. Listen to what she's saying, guys.

And they tightened that risk.

Good Enough, not right.

And they've tightened that risk so tight that it is harder to qualify in a way. Right. So they've tightened that risk so tight because they're afraid to make those mistakes because then it could hurt the company. So they always start out tighter and they rarely loosen. Like, that's the understanding. It's like you tight. You start out tight because you need to make sure your company doesn't fail at this and it doesn't approve a lot of the wrong people. And then from that point, we rarely get an algorithm that gets broader. That just doesn't normally happen. An algorithm learns and gets tighter and narrower. That's what it's supposed to do. So it isn't until it starts affecting and hurting profitability that they'll go back and say, what's going wrong here? And what they really should have done is they should have taken 10,000 or whatever they imagine that the number is. Yeah. And see, FICO can do that because they've got a broader set of data points to work with across the board. They could even fake through and say, this person's applying and this person's applying based on somebody's profile, but not really be approving them or doing that. They have information to utilize that are real people and real information. And you want to run them through side by side, automatic underwriting and regular underwriting and see where the cross is. And so I guarantee you they did that, but they probably did it at a very narrow and small subset of. And when they saw that the algorithm was beating out the underwriters, they said, it's good enough.

Yeah, that's the. Good enough is good enough. And guys, she's making a brilliant point that you are two to three times more profitable. Then we don't need to be inclusive. Right. And to be profitable, I just want to illustrate in reinforcing language, your argument is perfect, that the algorithm is designed to create less and less risk and only until profitability is threatened will they even take a look at broadening the approval model. Right. Because if it's 10 times less expensive, you only need to make 20% of the previous applications to double your money. Right. So that's a brilliant point, Tracy.

The other part of that that I just want to point out to everyone and thinking about it, is that this is also the opportunity. So we may see a company emerge with a better run algorithm, a better machine learning system on how they design it. And so actually having this brought up, whether or not it was true, the perception that it is true.

Looked at Right. Data has to be trustworthy, so it starts everyone to question. And so when you're questioning this, it means there's an opportunity somewhere for someone to make a great card that's going to make a killing because it's going to actually target the women who've been falling out of the other algorithms everywhere.

Manual approval rate.

Exactly. And so that's where we're going to really see something emerge into being really consumer friendly. So I'm always a fan of these kinds of things coming up, even if they're a little hyped up because they do raise awareness to people taking a scrutinizing look at their algorithms.

Perfectly said, guys. Everybody knows that I'm a FICO fanboy, right? But I don't trust a lot of systems unless there's this kind of scrutiny. And like you said, FICO has been doing this since the 50s, have basically set the standard for the vast majority of organizations out there who subscribe to their willingness now it costs to buy this kind of scrutiny. And I don't know who does and who doesn't use FICO. I know that there were 600 banks and financial institutions represented a FICO world, 600 of them.

So here's something that we heard, Meryl, because we're in your program and we were out there and I'm looking at buying a new car and because my car loan ran out. And you know how bad that is on your score, right? I mean, seriously, my score dropped 30 points like overnight.

I rest my case. This is a witness.

It's huge. And I had the better score of Tom and I, of my husband and partner. And so looking at that drop, we were like, okay, it can only be the car loan because nothing else changed in that two month period of time.

This is reverse bias, right?

Exactly. And so I was like, well, then we only have one car here, so I've been wanting to buy a car. So I'm like, this is great. So we go out to investigate, we start to make some phone calls around to the banks and we come to discover that most of them don't know who, which one of the credit bureaus they pull or if any. They have no idea because it's some mysterious automatic underwriting. Right? Now one of the banks, because we called the credit union that I've been involved with since I was in college. And so Tom called the credit union, said we only pull Experian now because no one trusts Equifax because of the perception of them being hacked so much. And transunion is ridiculously slow at updating their systems. So we can't trust that they have current data. And so I was like, well that was totally honest and like at least someone was paying attention. But it's a credit union and their company's small and they're just regional and they have like six or seven branches, you know, so they can probably have more information down at the base level than most big banks do.

Does apply. There's a lot of people who've pulled their contracts from Equifax. Now I am going to put some conversations I had with the VP of Equifax. They know that the data breach was just a gut wrenching. I mean it was horrible. Right. Well, they are now partnering on a platform with FICO to not just shore up, but, but this is a market opportunity to, for Equifax to regain some level of trustworthiness and create a joint platform I believe is going to be mind boggling. Before this, before this. Credit reporting and credit scoring. Legally they have to remain apart, but the underwriting platform allowed by data and FICO could be a juggernaut. So I'm just saying three steps backwards, seven steps forward. Right? The slingshot effect still moving.

Right. Here's the thing. It's like when you have a company like that has as much data and information as they do and who's been as good at it for as many years as they do, they take that with the respect and authority that they've earned. I was interviewing Jerry Cuomo who is IBM and he is the VP of their blockchain technology, which is a whole new division. Like why is IBM going blockchain? Right. Like it's a question. And I was interviewing him on the New Trust economy and I started asking him some questions about like this algorithm thing. And because they're building what is called a private blockchain, which is different than the public one. Right, Got it. And a public one has a higher perception of trust because everyone can be involved at any point in it. Right. And so it has the checks of the community. Right, Exactly. It has the group. But he said there's so many applications and corporations and understanding where we can't have a public blockchain. However, he said we built the coding basis for what is our blockchain out in the public space and open source because we understood that while we had a lot of data, because we have so many different clients and everything at IBM they cannot brute force test that properly themselves. And so they put it out in open source and then they pulled it back in and from their learnings decided what was going to be a part of their private blockchain because they knew how they wanted to service their corporate clients with it, and so that's the better way to do it. So if FICO is out here building this amazing core data and understanding and now you build off of that, you have a higher likelihood for success in whatever algorithm and whatever underwriting system you built.

Absolutely. So no time this time to talk about blockchain. But hello, everybody's going to be responsible for everybody's. The security of everybody's data. Right? I mean, it's just going to be a bit here and a bit there and a bit there and they're all online and we all share the bits.

After the experience of going around a bunch of banks on Monday, which kind of made me feel a little like a bank robber, like I was casing the place. Because you go to three in one place. Yeah, I put that in the Facebook group because that's what it felt like. When you go to three banks in one day to open accounts, like if any, even though you're giving them your check and money, it feels a little like you're doing something wrong. But after experiencing like everybody's security for online platforms, some of the archaicness of like getting your PIN number, I was like, wow, not enough has changed here. I can see why the trust in banking systems and in our financial system is so low.

Because a profitable enough is enough, right? There's not a social consciousness to say, hey, we're at 40%, but it's good enough. Because our loss, just like the car.

As long as we're making money year over year, who cares, right?

And any suits are handled within the value of a human life is based on what the payout would be at GM for having accidents that they have to pay out on for poor design. Right. Horrible modeling. Banks are in the same business of saying how profitable is enough? Given that we're going to have some data breaches or we're going to have some failures, but those failures are not enough to make us want to improve. All of a sudden Equifax, no credit bureau is a progressive institution. All of a sudden they have this massive failure and then now they're partnering with somebody who is truly been a pioneer in every iteration of their algorithm development and even trying to do their best to build bias out of it, if not make it conscious. Right? It's like everything else, it's natural selection, Right. When you fail is the only time you're Willing to listen, to improve, and now things will get better. But look at all the failures in the banking system, the, the horrible security. When they say bank level security, that isn't saying much.

Well, and this is what, when somebody does ask me about blockchain, that's what I was like. Just imagine you build a big fortress around all of your data and you put red Bullseye targets all over the thing. That's why no data is safe in a non blockchain world. Right. Like that was the first opportunity I saw. And a distributed data means I don't know what's valuable and what's not. That's a whole lot easier. Right. And so the same thing here, so this is what I wanted to say is that when you think about the FICO model and you think about all those and I think it's like 30 points of data that you want to something like 30, 30 points of information that they're utilizing to value you and to value your ability to be lended to. Like when you look at that fundability score, the more data points the better. If it was only our credit score, it would be so biased. Like at the end you have to understand because it's one single point of.

Data managed by fundability, we have at least 30. Right. I mean that's what we drive it all. And of course algorithms, what we call fundability is also what lenders use in their software. Am I willing to give them money based on the summation of this algorithm? All these data points that I'm measuring, We just call it fundability to make our borrowers feel like, oh, I got some power in this.

Well on the AI side it's really called risk mitigation because that's probably mostly what they're asking.

That's their perspective.

Yeah, their perspective is not just profitability, but on the coding side their job is risk mitigation. So their goal is to net a result that is profitable, but their job is to mitigate the risk.

Okay, so let's back out from the boots on the ground to the hundred, maybe the thousand foot view. Tell me what it is about you that like your character traits that make you a so not just intelligent but so perceptive of this cutting edge technology. Your entire background is, is about evaluating the likelihood of cutting edge brand new technology. What is it in your character that makes that so fascinating to you?

I think it's just basic curiosity. So it's basic curiosity and questioning. Like those two things where you never believe what you read so early on.

It's on The Internet. It must be true.

It must be true, right? I never believe the first thing I read. Right. It doesn't happen. So my father instilled in me this idea of very, very early on that when you wanted to read about a subject, you should read three to five books about it. Back then we didn't have the Internet, so, you know, I'm sure you probably have to go through 20 websites, but back then you could read three to five books, and that way you got a better prodder perspective. And that one of those books always had to be literature and not a history, not a biography or anything like that. It had to be literature from that day and age. And because literature tells you more about what people really thought, because they disguise it, especially as you're looking at ancient history or longer history, they would disguise it because it was the only way they could dissent. And so you would have the dissenting opinion in there somehow. And so that's what he taught me. And so it always is this discerning level of things that I look at where I'd be like, okay, now it's got me curious. I'm interested to learn more. Where am I going to find more? So the next part of that is that I learned very early on to get to source material faster. See, you've gone to FICO World. That's where it's material, right?

That's okay. You know where I want to take our listeners.

Keep going. It's so critically important to hit to the source material or something. So our mutual friend Aaron Young, who you were just at his event and everything, as part of one of his courses that I'm taking, assigned me the Science of Getting Rich, which I had not realized is actually the source material for thinking grow rich. Ah.

So Napoleon Hill original.

Oh, yeah. So this is the original part before Napoleon Hill, because it was out in 1910 and Napoleon Hill was 1937, somewhere around there. And so, yeah, so taking a look at that, it's so interesting to be saying, oh my gosh, I'm at the source material for something. I had not been curious enough about it to like look into that deeper.

To go to 1910 instead of 1930.

Right, exactly. But when you look at something like that, it brings an awareness of where were people's minds at that time and where was that? So I always like to go to source material because you're always going to find the professor perspective. But it's really important to get that broader brush. Look at it, because it's between the lines. That is where the truth is, it's between the lines of the opportunity gaps. That's what you're finding and you're seeing. And so when I find someone like you, and this is why I didn't need to take his boot camp. I took it. I was going to hire him anyway, right? So I took the boot camp because I was curious. I wanted to understand it better because I thought it broadened my view of things. I did. I sat through your two day boot camp and I learned hundreds of points of things and took I don't know how many notes. But now everything that your team does with me, I understand at a more intimate level. And that makes a difference in how I approach the world. And it makes it so that I am capable of seeing where the innovation point can lie. And that's in evaluating someone, but also in doing that for myself because that's my job, is to innovate for my clients, right? So if I'm at that innovation point, I know if I just mess with this 4% of something, that's the critical point because that's the opportunity gap. That's where we want to get to. Because that's the true value, original value in any company, in any corporation, in any technology.

She's not a plant. I did not lead her to this. But I ask you the question about what part of you. Because everything we do, personally and professionally, everything derives from this little awesome mess inside of us that's trying to filter the world and grow and become. And become responsible adults, right? Sometimes from huge messes of our lives, right? And yet the curiosity and getting to the source material. Because I always teach that only the truth is actionable. But the truth, you said between the lines and I say it's the pendulum, right? You got this view and this view, but all views are accounted for somewhere in the middle, right? Nobody's rarely is somebody just, just truly, truly wrong in the historical perspective, right? I mean, every. We've been doing a lot of things, a lot of ways for a long time. And out of that this opportunity thing, this is new language for me. So forgive me, but this opportunity gap is where you are. You're saying that as the pendulum comes to rest, that last 4% is where we get to see best practices, best opportunities. What I strive in rufable, everything that we do is I am not attached to a single thing a lender or FICO does. I'm committed to finding out the next best thing that hits that 4%, right? That new level of accuracy. And we call it the Bullseye. But, yeah. So thank you for that. And I can tell that we're kindred spirits.

Yeah, absolutely. The thing that is so important about that and what you're doing and what in general is that when you're not attached to all of the things that happen along the way. And I've worked with a lot of inventors over the years because of my product work, and they're so attached to their thing. Right. They're so in love with their thing, you know? But when you're not attached to that, that's really where you can see is where the opportunities lie. How does things have to shift? Where do I need to go? And so this is our opportunity to be truly what I call competitive proof. Because you're always so far ahead of your competition in deep knowledge and understanding of what the thing, so they can copy one little detail of what you do and make a big marketing buzz about it. But at the end of the day, it's missing all that deep, rounded data that is making your clients truly successful. I always look at that. I always think that that's the more important thing is to do is you're compounding on the most important things again and again that are going to create continued success.

Awesome. We're getting close here. What would you say would be the single most important in this aspect of personal bias and institutionalized bias? What would you tell my listeners is like the most important way to bias proof themselves? Because if we do it personally, we have a good chance of getting it out of the institution.

Right.

So what would be your recommendation as a human being, as a powerful, intelligent, skilled and capable woman, how would you tell me and my listeners how to bias proof themselves?

So it's not easy. Like, this is hard. It's a lot of inner work. Right. And I don't think we can do that at any point. I think. I don't look at it as bias proofing. I look at it removing the really negative bias. Right. So we want to remove the things that are really harmful and hurtful. And what might have been harmful and hurtful today is not necessarily what was harmful and hurtful 10 years ago. So I'm not a jump on the bandwagon and let's go rewrite history here. Right. I don't believe in that as well. Right. We need to say, okay, well, it's my job to not be racist. It's my job to not be sexist. It's not my job to make sure that my kids don't grow up with that. That's the bias I want to pull out in everything that we do and say. So little example of this was a couple of years ago. My daughter's Montessori school was having Mother's Day and Father's Day events. And Father's Day event was at the end of the day. And Mother's Day event was like mid.

Morning at 11am after I get off the tennis court. My Bellini.

Yeah, I'm like, I'm busy. I got interviews. This is inconvenient. Why can't mine be at the end of the day? I looked at my husband and I said, if I say something about this, it's not going to be received in the way that it needs to be unless you say something about this.

And now we have to address the bias in a way that the bias can actually hear us.

Right? Exactly. And they can't hear us if I'm just complaining and oh, it's another working mom complaining, right?

It's a mom.

Yeah, exactly. But when a dad says, do you realize you did this? They heard it. And the next year we now have it. It's always 9am in the morning for all parents. Mother's Day, Father's Day, Grandparents, well done. 9:00am in the morning.

Social change people. Tracy Hazard, Social modification.

But whenever you see something like that, it's important to not get all in a huff about it, but to make a point and to say, hey, do you realize that you're doing this? And it's not actually the best solution? And that's always a better approach. And every little thing matters over time. It's what I tell my daughters all the time. Every little bit of inclusion that you can do. Every time you see injustice and you stand up for that. Because every time you sit back, it's as bad as accepting it, it's as bad as doing it. And those are the little things that matter in our own personal way that we address things. The institutional bias. It is our job. If you're in the institution, if you're building that company, it is your job to speak up as fast as possible because you cannot let that happen. Someone gets hurt. So when I was doing product development, I mean, people could get hurt on a product. And you see it. That's why we have all those massive recalls and lawsuits. If you don't speak up, you are potentially being a part of the harming process.

You're assessing to the harm.

So it is your job to raise the red flag. That's why we have whistleblowers. It's important in the process.

Thank you. Because I just went to just like we were talking about in profit mode, like it is good enough to be profitable is our bias or inclusion, etc. How often do we. Well, it's good enough in our companies, it's good enough. How do we support mediocrity? How do we support minimalism? How do we support that it's okay to just be meet legal standards rather than just keep rounding up, keep rounding up, keep rounding up until can get.

Really overwhelming to people, right? To say, oh my gosh, like when I was early on in my career I was working on sustainability textbooks, so textiles that would be recycled or other things like that. And like everyone was like up in arms, like it's never going to happen. There's too many things that have to occur. It's not going to happen. Yeah, exactly. Let's take a small step. Because every bit is better than it was yesterday and every bit means it's out of landfill or out of this. So always having that continuous improvement mindset is really important in everything that we do. And not to get overwhelmed and say, well, it's not good enough yet. That means that we don't make progress because that's where people sit around and ruminate about it or bitch about it. Like it's not going to be good enough. It's never going to be good enough. Why do anything right that's not making progress? And so if we really truly want it, you design the systems in your company to be continually learning. That's why I actually really love AI at the end of the day. Because machine learning, it's supposed to learn.

As long as you designed to learn. If there's no bias in it, Right.

Where people don't always do it, they stop faster than a machine will. Right. And so as long as we keep fiddling with the model and keeping our eye on its bias, hey, that machine learning is really valuable. So when people ask me, do you mind if Alexis is listening in in your house? I say no, because I don't mind because I know she's learning my habits better. I know she's learning about my daughters better. And there's this great game on there called Akinator. And I think it's like a K I N a T O R something like that. Akinator, also known as, yeah, go and say, Alexa, play Akinator. Wait till you play it with like a five year old because their logic is so different. And she'll say, okay, think of a character or something like that. And so you immediately go and you think of a character and she'll ask you a bunch of questions and then before you know it, she will have the answer of what you're. What's in your head. It's like she's guessing your mind. But it's a really good insight because this game is teaching Alexa to learn about how our children think, because children at different ages think differently. So what she Learned from my 5 year old was that she didn't understand the difference when she said is a male or a female? My daughter didn't understand the difference because it was girl or boy in her head.

Yes, yes, yes, yes.

And so it took Alexa a really long time and then re sorted out that that's with a flaw in it and then ended up at the solution. So that's where our terminology gets better. Like all of those things. So play with it, learn with it. Brute force, Test it on your own.

Yeah. AI does not necessarily become Skynet from Terminator series, guys. Okay?

It can. We got to watch it.

We want to make sure. I want to draw this to a close. I want to remind you guys. And thank you, Traci, for being so. Your humanity comes out. This isn't just tech, guys. You guys all binge Rufable because there's so much great content. But at the end of the day, at the end of the podcast, it means how does this improve not just my standard of living, how does it improve my quality of life? How does it create more opportunity for me to bless the lives of my children, my loved ones, my family, and my community? So, you guys, thank you, Traci, for joining me on this. Absolutely wonderful. We share more in common than we are diverse. You brought a wonderful opportunity for me to expand the conversation after my rant, which is on a previous podcast. Go watch the rant and then you'll see Traci's logic and compassion and straightforward thinking. And you go, yes. I totally get why she's here. Thank you for joining me on this spectacular version of Rufable. Guys, make sure you go to getfundable.com to read this. We converted into a blog. Read the blog. Take it in. Learn for yourself how you can remove bias as much as possible from your life so that we raise our little ones and we love our big ones in a way that is inclusive and not exclusive. This is Merrill Chandler. It has been a wonderful time. Thank you so much. Have a spectacular day, guys. Hey, guys, thank you for listening to Ruffable. Please leave comments because I would love to read about your Aha. Moments from this episode. And be sure to visit fundinghackers.com to view the blog post, get important links, join our community, and much, much more. And you gotta tell your friends about this podcast. We want them to become effortless. Football 2.