In this pod,Carla Canino, CEO of Kindlee AI, shares with us how AI bias is costing lenders $443 billion annually and what financial institutions can do about it.

Key highlights:

• 65% of loan declines are actually creditworthy customers being misclassified by biased AI models

• Traditional lending models ignore 23% disabled, 20% elderly, and 22% neurodiverse populations

• Small banks waste $4-5M annually on operational friction caused by bias in customer interactions

• European AI Act requires independent third-party auditing of high-risk AI systems

• Inclusive lending models can increase profitability while ensuring regulatory compliance

We also go personal, and Carla shares her journey from payments expert to solo founder, explaining how her experience as a disabled immigrant showed her gaps in financial services that inspired her to start a company that solves for these.

She demonstrates why reducing bias isn't just about social responsibility - it's about capturing enormous untapped market opportunities while maintaining risk standards and regulatory compliance.

Follow for more discussions on building FinTech products with customer and commercial impact and to stay updated on the latest episodes.

Socials:

👉 Follow Carla Canino:

LinkedIn: https://www.linkedin.com/in/carlakerstenscanino/

Website: https://www.kindlee.ai/

👉 Follow Monica:

LinkedIn: https://www.linkedin.com/in/monicamillares/

YouTube: https://www.youtube.com/@moni_millares

TikTok: https://www.tiktok.com/@moni_millares

We cover:

[00:00:00] The $443 billion lending opportunity being missed

[00:28:00] Core question: How to lend more without losing money

[02:21:00] Why 65% of good customers get declined by AI

[04:46:00] The invisible customers AI models ignore

[08:07:00] Personal banking discrimination story

[10:17:00] Who exactly are these declined customers

[13:24:00] Biometric authentication bias problems

[18:21:00] What Kindly AI does for financial institutions

[23:43:00] Zero integration implementation approach

[28:50:00] European AI Act compliance implications

[33:02:00] Quantifying operational waste from bias

[37:54:00] Build versus buy decision framework

[45:16:00] Career pivot to solo founder journey

[55:14:00] Key startup pivot moments and lessons

SEARCH QUESTIONS

How to reduce AI bias in lending decisions

What is the cost of algorithmic bias in finance

How to approve more customers without increasing risk

European AI Act compliance for banks

Why do good loan applications get rejected

How to measure bias in credit scoring

What causes AI lending discrimination

How to build inclusive financial products

AI bias detection platforms for fintech

How to reduce false loan declines

What is systematic bias in banking

How to serve disabled customers in fintech

Solo founder challenges in AI startups

How to comply with new AI regulations

What is ROI of bias reduction initiatives

How to audit AI models for fairness

Why biometric authentication fails minorities

How to calculate operational costs of bias

What is Kindly AI technology

How to retrain discriminatory AI models

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Production and marketing by Monica Millares. For inquiries about sponsoring the podcast, email Monica at fintechwithmoni@gmail.com

Disclaimer:This episode does not constitute professional nor financial advice and does not represent the opinion nor views of my current, past or future employers. The guest has agreed to record and release our conversation for the use of this podcast and promotion in social media.

Carla Canino. Pod

Carla Canino: The bank flagged me as high risk, and it required me to physically go authenticate myself in the branch. But at the time, I was unfortunately in a wheelchair. I have a disability and I could not physically go authenticate myself, which meant their KYC system and their fraud system.

Again, blocked my account and I could not access my account for months.

To a certain degree, bots will be our customers.

We're thinking just from an operational cost perspective, a small institution say of 2 million customers will have an operational waste of between four to 5 million annually just on operational mismanagement, which is crazy.

resilience, it's not that I decided to be a solo founder, it is that I'm the last man standing all the time.

Nobody's gonna give you permission. No one, like truly no one's gonna say to you, yeah you should be a solo founder.

Right? It's like the kind of thing where you do it because you have to do it.

But the truth is, you just need to go. And the recognition comes and the respect and visibility and acknowledgement comes, once you create value.

And sometimes you have to toot your own horn.

So Cara, welcome to the show.

Carla Canino: Thank you so much for having me. So excited to be here. Happy New Year. Wishing you also health, prosperity and tons of positive impact.

Monica Millares: Thank you. Yes. That's a good one. Tons of positive impact for everyone. Yeah, exactly. And talking about impact.

Carla Canino: Yes.

Monica Millares: Basically there's a stat that you've shared before that it's basically lenders across the world.

how should we as managers basically. Tackle this question of there's this opportunity, we want to lend more people to lend to more people, but if we do, basically the portfolio becomes more risky and we lose money. So what's your take on how do we reduce false declines to expand the pie?

Carla Canino: It's a big question and it's an excellent it's an excellent position to be in.

If you were talking about. Biases and the decline of good customers is going to exponentially worsen. So when we're talking about a 65% of customers is good to understand that it's 65% of declines of the current customer declines are actually worthwhile customers. What does this mean? This means that we have an issue with misclassification of risk and we have an issue also of understanding who is a good customer.

So when we take a step back and we look at customer segments and the reality, the human reality of our customers, most models are designed, or most models are actually evaluating for customers that don't really exist. If we look at, I personally work with understanding the prevalence of what we would call underrepresented populations.

So

Monica Millares: mm-hmm.

So all of these, when you aggregate these customer segments and, and, and more, you actually understand that your current customers and your future customers are. Vulnerable populations are underrepresented in these models, which means, again, the models that we have created are simply mis misclassifying risk.

Monica Millares: So is this what you mean with the 443 billion stat, or is that different?

Carla Canino: Yes. So the 443 billion is the representation of the missed opportunity in both. Lending and the total addressable market, right? But the problem is actually broader. So when we are designing product experiences and we are making, again, product assumptions and risk assumptions about customers, this doesn't just affect lending, which is the funny thing.

Most in fact, financial technology firms are under serving these customers and creating friction that costs in terms of operational costs, in terms of escalations, in terms of manual escalations, in terms of, of course, things like refunds, and of course, in customer trust, which reduces the share of wallet of future, of, of current and future customer.

Carla Canino: Yes.

Monica Millares: But we are not designing for all these subcategories of customers.

Hence we're living money on the table.

Carla Canino: Absolutely. We are not only leaving money on the table, it's costing us more already in operational experience. So again. The path that we have chosen, or the design that we have is not only misinforming our data banks, right, and it's mis flagging customers, but it's also already costing in manual escalations in unhappy customers, sometimes canceling their accounts or chargebacks or refunds.

I think it's just for context, it's, it's good to explore The reason why I ended up creating kindly. My background is, of course, in financial technology. I'm, I come from the payment side and the fraud prevention side, but when I first moved to the Netherlands, I opened a bank account with a local bank.

And funny enough at the time my, my income signal was at least twice what the. Local average is so it wasn't an income problem. The bank flagged me as high risk, and it required me to physically go authenticate myself in the branch. But at the time, I was unfortunately in a wheelchair. I have a disability and I could not physically go authenticate myself, which meant their KYC system and their fraud system.

I, of course closed my account eventually. I was so frustrated with that bank that I took my money elsewhere. And so this bank will very likely never see me, not only open another account, but of course, you know, capture any kind of other share of wallet that I could have potentially had, including lending.

So I think this is just one of the many different experiences that what we would call underrepresented populations might have.

Monica Millares: Yeah. And then you work. A lot within the lending space, like your customers work within the lending space. So who aren't these customers that are being declined?

Carla Canino: That's a great question.

We tend to think you know, either about gender or about race, but actually models misrepresent a variety of different customer segments. So again, we talk about disabled people. They might have a visual impairment or they might have a. A mobility issue, they might be also elderly. And so elderly people interact with technology very differently.

Right? And so all of these segments will interact in the financial system a little bit differently than locals, for example, right? And they might have also different access to identification or might have a. Thinner customer file or might simply not understand the right steps or provide different data or documentation that required.

And a lot of times, even if you're, if we're talking about things like. The onboarding process and the biometric authentication required for a lot of these things. Some customers might have either actual disfigurements or again, their skin tone might not be as well recognized by a biometric authentication or in my case, and some people, or a lot of people have little fingers or renal syndrome or a worn, so that means that the.

Monica Millares: Yeah. That's very interesting because as you, as you were describing these customers and, and specifically the example about the hands, I was like, yes, I never think about that. Like, right. Embarrassing, but, but that's the reality, right? It's like, I don't, but then I was, then my BC product mind came in and I'm like, no, because even if we did, it's not the top priority.

Hence we couldn't allocate, basically resource to fix that.

Carla Canino: Yeah.

Monica Millares: Because of a big backtalk that we have. So how do we, I guess like if I'm a neobank, how do we measure the impact of not, not serving these customers? Well,

So our thesis is that by reducing some of these disparities and some of these points of friction, and by shedding light, we can not only, of course, help. Customers be better represented, but also create a higher profitability for, for fintechs and also obviously be able to create a clear path of higher revenue.

There's a couple of different cost aspects that are important to, to acknowledge here. As we were discussing before. The operational cost aspect. So it might be very much escalations or might be require or requests for accessibility that quite frankly a lot of financial technology firms are falling short in that aspect.

Or as you're developing them, I think there's plenty of responsible AI solutions that provide a certain degree of help in that, but the reality is that you want to actually measure how a lot of these models are performing to real customers. The other aspect of this is that as a bank is developing their solutions and their risk models.

Most banks are also depending on foundational models for their, their, their LLM base, right? So they really cannot control not only the guardrails, but the performance or whatever security or data injections might happen on that side. And so it's extremely important to be able to look at how the. Models are performing on the other end, right on, on the model in perfor, in in production, and that's exactly what we are focusing on at Kindly.

Retrain their models and improve their representation towards these population segments. And so again, that not only creates a clear visibility on the cost aspect and the potential uplift that they could gain, but also it creates a path towards improvement and towards representation, which gives them hopefully also a competitive advantage.

Carla Canino: Yeah, that's a good question. So again, there's, there's a lot of solutions right now that can, at least on a high end, evaluate or help build you know, a certain level of.

Representation for customers. But what we do is we take it a step further. We check how the, again, how models are performing in production. And we have specialized models that can better represent and measure how, how the financial technology firm is actually providing full access and representation to a lot of these groups that are underrepresented.

And we provide cost visibility. We co, we also provide a full compliance measurement, so. We care about not only how, you know, the financial technology firm is, is performing from a profitability perspective, but also how they're performing from a compliance perspective as the AI Act is coming. And also the European Accessibility Act, at least here on the European side, that's, that's something that was enacted recently.

Monica Millares: Okay, so if we were to summarize and if I am a new bank and I run ai, AI powered credit decisioning, how do you help me?

Carla Canino: Yeah, we provide a full suite of analysis from, again, from a bias perspective, from a compliance perspective, and from a cost perspective as well as the full suite of recommendations.

Monica Millares: Okay. And then you also say, this is like the skeptical me.

Carla Canino: Yes.

Monica Millares: Speaking. You talk about, you have a core promise that it's zero integration.

Carla Canino: Yes.

Monica Millares: I'm like, no, come on.

Carla Canino: That's a,

Monica Millares: how does this work?

Carla Canino: That's a, it's important to say that we are, first of all, an early stage company and our first. Use case evaluation. Use case is actually looking at how companies are performing from a customer service experience. 'cause that's the first point of interaction, right?

From there, we will be expanding into other use cases that are relevant across the industry and that are part of the evaluation ultimately, not only for lending, but for different core needs of the, or core uses for, for, for banking. Such as you know. Money movement. And so what we are able to do is do what's called adversarial de biasing through our own models without the need to integrate.

And, and give at least a first view in the future. We are hoping to expand the use cases, as I mentioned before, into KYC and other use cases that are a priority in the industry. And I don't know if we can maintain that promise of no integration, but we do understand that low lift is something that is required.

So fair, fair.

Monica Millares: Okay. Cool. Good. I'm like, nah,

Whether the bank is, is truly treating customers fairly and and understanding the needs of what we would call, we used to call outliers on our under represented customers, but we actually realize that they're actually the majority of the customer base.

Monica Millares: Okay, so then it's easy to integrate.

Carla Canino: Yes, it's

Monica Millares: easy to implement.

So then let's go a little bit deeper, coming back to the use case of lending. How do you exactly help us basically approve more customers without being more risky?

Carla Canino: Yeah, so when we, when we're thinking about lending, first of all, this is a, this is a complex answer because the approval models vary depending on country, right?

We don't have the full visibility of true customer experience. And so what we are doing is helping contextualize the needs and the customer journeys and the real life experience of a lot of these underrepresented segments precisely so you can better represent them and have models that are next generation, have models that are actually accurately representing the experience of a lot more customers while also.

Understanding that, and this is important. There is no perfect scoring meaning. I think it's unrealistic to think that we will solve. You know, and, and have a, a, a zero false declines. I think that we can have improvements by better representing some of these journeys, as I mentioned, right? Like such as my own journey.

So there's a lot of pieces in here that can be better contextualized, and that's exactly what we're aiming to do. We think that in order to solve this problem is not a, how do you call it, a a zero to a hundred solution. It's very much about incremental improvements that better represent and better contextualize real user experience.

So right now, one of the things that we are able to view that most companies can't do or can't view, is the performance across the industry. Right? So we launched the kindly. Cost of trust benchmark, precisely because we want to be able to have a, a true temp temperature of how not only the different financial institutions are performing, but also how are they performing vis-a-vis the, the LLMs or the, the, you know, the external tech stack that they're utilizing.

Monica Millares: Interesting. Yeah.

Carla Canino: Is that helpful?

Monica Millares: Yeah. It's like, I hadn't thought about that, that it's like Yeah. The, the benchmarking against the industry. Yeah.

Carla Canino: I think ROI is one of those questions that are right now very important in the industry, right? Like a lot of, as we said, like a lot of companies are investing in in ai, but what is the ROI and how do you measure for that?

So that's one of the core questions that we have in mind is not only how do we help the population have a fair access to, to financial tools, but also if the financial world is essentially moving to be as AI driven as possible, essentially in the future. To a certain degree, bots will be our customers.

So how do you know what is the effectiveness? What is the reliability and how, and what is the drift and what is the true performance in production of your tech stack vis-a-vis the customers that you're trying to serve?

Carla Canino: Yep. Yes.

Monica Millares: So. That takes us back to compliance. You spoke about the European AI Act as such. What's the relevance here?

Carla Canino: That's a, yeah, it's a tricky one, right? Because we've seen, let's take a step back and, and this is a tricky one because of geopolitics and because of, you know, sometimes what can be described as the over reliance of certain industries in US tech and vice versa and, and not vice versa.

Sorry about that. An overreliance of certain industries in US Tech. The AI Act was very much developed to both protect the sovereignty and European values and European priorities towards European customers, but it also is providing. A certain degree of transparency around how US technology is being leveraged and used and how it's performing exactly in those terms, right?

So if a high degree of the clients of US technology outside of the us, then it begs the question, how is US technology serving the citizens of other countries, especially in highly regulated industries such as financial, te. And so and, and, and I think we all are aware of the Brussels effect, right?

Like it's, it has a fighting chance. And so I think that kindly provides a lot of this transparency and provides you know, a very unique value, which is. The specific specialization on ISO 2 4 0 2 7, which is again for bias reduction, but it also, I think it's providing a very clear. Transparency around economic access for populations that are typically not served.

Okay.

Monica Millares: So now it's like that's the money question.

Carla Canino: Yes.

Monica Millares: I'm like, okay, tell us more. Tell us more.

Carla Canino: Yeah. Yeah.

Monica Millares: So yeah, what are the, what are the economics? Is it a SA model, like revenue share? Like

Carla Canino: how do we charge it?

Monica Millares: How does it work? Yeah.

Carla Canino: Lemme backtrack a little bit. First, let me say why this matters. So, in the preliminary study that we created, and again, we are a very, very early stage.

Technology firm. We're thinking just from an operational cost perspective, a small institution, say I'm thinking about US decisis, but an institution say of 2 million customers will have an operational waste of between four to 5 million annually just on operational mismanagement, which is crazy. Right.

And so

Monica Millares: $5

Carla Canino: million? Yes. Just on operational mismatch, and this is the first time that we're doing these tests. We are not looking at KYC, we're not looking at again, lending yet. We're not, we're we're, we're truly looking at what this is credibly costing. Year on year to a small institution. So if you extrapolate that this is a, a huge cost, so why does this matter?

So we do have, yeah, an audit fee. And for certain types of financial technology providers. We do have a revenue share model, which we'll be introducing later on this year. But for again, a typical financial technology firm, when we're, when we're, when we're talking about the high risk models as per the AI act, the main quote unquote cost or the main investment would be [00:32:00] continuous audits.

In addition to that, we again provide. Their recommendations so they can retrain their models and thus reduce a lot of this friction and reduce a lot of these operational waste and hopefully, again, gain competitive advantage.

Monica Millares: So then to recap that, let's say if I'm a neobank and I'm assessing kindly, what are the leading indicators that I could have to track to then say, Hey, this is having impact on the p and l?

Carla Canino: Yeah,

I

Monica Millares: think working with kindly.

Carla Canino: Yeah, yeah, yeah. Kindly has a really unique position because we're able to provide visibility on essentially all, all quadrants of the p and l, right? So as I mentioned before, we have direct visibility at first, even without integration, we have direct visibility into the cost aspect of things.

So we provide full scoring. On these aspects of the AI Act as well as the EAA, as well as some aspects of GDPR that require, once again, fair treatment.

Monica Millares: Interesting. So then if I'm assessing kindly, there's always a question of, oh, can we build this ourselves? Oh yeah. As a product person.

Carla Canino: Yeah.

Monica Millares: It's like, oh, why should we, should we clean house or should we go for external?

Yeah. What's your logic behind it?

So we once again evaluate. In production performance. The other aspect of this is that we have aggregate data and we have benchmarking that gives us visibility that obviously, you know, even if you do build your, your own tech stack, you wouldn't have. And that's part of the value that we provide. And we have, of course, specialized models and specialized technology that does this.

That being said, what again, we go back to. How are you going to know what's a true ROI if you only depend on one particular tech stack, right? You don't really have visibility of how you're performing vis-a-vis other companies or how, what is the true ROI of your technology if you are reviewing all of these tools and you are also dependent on your very own evaluators.

They're, that that's basically what a lot of these companies use which ironically enough need to be evaluated afterwards because they simply, they're skinny, right? Like they do not truly. Have the, both the, the data, the data with, and the true representation that is needed to measure this. But also it's essentially impossible for them to be able to evaluate both, you know, their own data stack and have a true contextualization of what's happening across the industry.

Monica Millares: Of course. Yeah. So it's kind of like you cannot be the patient on the.

Carla Canino: You got it. Exactly. I'm, I'm thinking about the Spanish. Like, you, you cannot be Judge Verdugo. I don't know what verdu means, how you say that, but Yes, it's exactly that. It's exactly that. And that's exactly why we want to remain independent, right?

Monica Millares: Yes, exactly. Yeah. Which then that takes me to you as a founder.

Carla Canino: Oh my God.

Monica Millares: You know, like you had a thriving career in financial services. Yes. And then after 15 years, you were like, you know what? I'm going to go and become a FinTech founder. What was your thinking and decisioning framework? Can you guide us for that process?

Carla Canino: Yeah, so while I had a career or I still have a career in financial services, technically in parallel to that, I worked a lot in, in financial inclusion.

It's it's always been very much my desire and it's one of those things that I think is in your veins. When you see a problem, this is also why I want, you know, I've been both in the business development world and in the product world because you're able to, like, you know, if you identify a problem, you want to solve it.

And that's, I think the core of product is very much understanding friction, having empathy for customers, and trying to find a solution for that. And so I think it's just like a compulsion that I have and maybe a slight level of masochism. For, for kindly. You know, I think my personal experience, as I was mentioning when opening an account, but also every single conversation that I have around this topic, right?

They are constantly underestimated and they're exposed a lot of times to more fraud because they have dependency on third parties to be able to access financial tools. You know, obviously everybody has parents, so every time that I talk to, you know, anybody and, and you know, we're discussing how AI is now.

Being adopted across the board. Everybody's concerned about how their parents are going to interact with, with technology. Everybody's concerned about them having to help their parents or their parents being exposed to again, you know fraud. And so I think it's very much, for me, it's very much a mission.

Monica Millares: Hmm.

Like. Somebody has to do it and I, I feel like I'm in the privileged position of both having the experience and the perspective, a very unique perspective that I have gained both through being an immigrant, but also by being disabled, by being a woman. I mean, I'm obviously Venezuelan that, you know, it would almost be.

I think it would be criminal if I did it, if I didn't try to do something. I really do think I'm compelled to do it and I'm in a very unique position to, to be able to educate and be able to create value. Right. The other aspect of it is one of the most difficult. Paradigm shift and, and, and challenges to the industry is this concept of what are biases and are biases a cost?

Vast areas of the population not having access to, to the same access to financial tools, but it literally is costing the industry billions. And if you don't have a quantification, if you don't have measurable output, it just gets brushed under the table. So we need to speak the same language. We need to speak the language that matters, and that language is money.

Monica Millares: It's, yeah. And like the industry financial services as more, as much purpose driven that we want FinTech to be. It's at the end of the day, it's, it's we are businesses, money

I mean, they're not, they're not getting smaller. Right. Like in many, in many developed countries, the population is aging. So if you're not developing tools that take into account the needs of aging populations, then you're going to create even more friction and you're going to create even less trust, and that, again, has a quantifiable impact on the valve.

A financial services firm, and I mean, this is also a segmentation problem, right? It's very much a customer experience, a product, and a data impact problem, because if we are not able to truly understand who our real customer segments are, then we can't create products that are, that are actually serving the right.

Monica Millares: I love, I love, not only, I love how passionate you are about it, you know that it's like. Yes, and we're going to go and solve it. Which you are so passionate about it that you decided to become a solo founder.

Carla Canino: Yes. Yes.

Monica Millares: What was your, yeah. What made you be a solo founder? Most people are like, Hey, fundraising, I need you to three people.

You were like, no, forget about that. I'll just go solo.

Carla Canino: I think resilience, it's not that I decided to be a solo founder, it is that I'm the last man standing all the time. Like, again, I've had a couple of, I have had a couple of previous startups, right? And, and by virtue of many things and this sounds a little bit like tooting my own horn, but I have a very particular resilience profile and again, a, a very particular set of a, a very particular perspective that is able to inform both the product and the direction of the company.

Right. To me, this has to be solved, and that's a result that not a lot of people have. And also, again, like. I think that that question actually stopped me and slowed me down for a long, long time because I did not think, and this happens I think to a lot of women, but to a lot of people, right? Like it's very scary to go and be a founder.

And, and maybe they were a little bit too idealistic and not pragmatic enough. And so it's, I think it's very important to understand your own value and understand your own profile and how you can, you know, like truly. Value what you have and go with it. Nobody's gonna give you permission. No one, like truly no one's gonna say to you, yeah, you should be a solo founder.

Right? It's, it's like the kind of thing where you do it because you have to do it. And here we are and so far that, that essentially makes you what you need to be. Like. You just go and like approach the path and face whatever you need to face, and then you become the leader that you need to be.

Monica Millares: Amazing.

Carla Canino: Yeah, for sure. Quite a few times. I think the first time was so kindly started as something completely different.

I was actually analyzing a, leadership journeys and the leadership acumen for HR recruitment, right? And then the AI Act was being developed and then I realized, well, of course, like my background is in financial technology. I took a step back and, and saw again what we're discussing today, like how many different points in the customer experience in, in.

And, and understanding that. There was not just one use case, but there were a plethora of use cases that impacted not only one segment of the population, but everybody, and, and this was exactly at the same time that the AI Act was being developed. So I decided to pivot in that direction. And the second one was more recently, deciding where to start was very difficult, right?

Like there's so many different use cases in financial technology where you can analyze biases, but how can we quantify this if our, if our thesis is. There are biases. We need to quantify them in a way that matters to financial services and that doesn't open a can of worms and like, make them retreat.

We started looking at how can we do this in a way that we don't have to touch PII in a way that we can easily quantify the points of friction in a way that we can actually create value for not only the consumers, but also obviously the financial technology firm. And that's when we decided, let's, let's look at how the the customer jour journey goes and, and how.

Are these customer service bots, which is like essentially the core investment in AI that is being driven right now in addition to internal efficiency. How is this the first line of interaction actually working? That gave us a very direct view on exactly this, on like how are these models and how are these banks actually representing customers and actually treating customers?

And that opens the gate to a much broader conversation. So those were like the core moments of like Aha, that I've had so far in this journey.

So for everyone listening, FinTech is working in startups that are like, I want to be one of those. What's your advice for people who are in the, I want to be a soda founder. I want to do something with ai. I'm not a, I'm not an.

Carla Canino: Mm.

If you have a growth mindset and if you have the ability to figure it out, you can do anything. If you have resilience and adaptability, you can do anything. So focus more on the how can I, and what did I learn and understand that there will be pain along the way. This is just a given. And try to convert that painting into lessons and try to convey that, convert that painting into strengths, and you will be able to do anything.

There are tools, and if there aren't, you can create them even if you don't know how. Being a founder is not the same as being a builder. You can hire builders, you can become a builder. You your core job is to figure it out. Your core responsibility is to figure it out, to create an amazing team around you, to be able to create the values and the culture that you need to be able to understand the problem, not the product.

And the reality is that a successful company is not the tech stack. Right. It's the ability to best solve a problem and to find a wedge and to find, you know, a positioning that actually solves a problem with a customer. And the ability to have a customer that was, that is willing to pay for it, right? So I think that's that's one of the core pieces of advice and just like, jump in the water and swim.

Monica Millares: Amazing. Cool. So you jumped in the water.

Carla Canino: Wait. I'm a decent swimmer. A decent swimmer.

Carla Canino: Oh man. There's so many. I think. I think I wasted a lot of time doubting myself and wanting people to be co-founders. Actually, I wasted a lot of time relying on other people to give value instead of me just going for it.

I've wasted a lot of. Time doubting myself and I mean, I think everybody goes through this, right? Like, like maybe investing in things that you didn't need to invest on and you could have like be been more frugal on one side and maybe invested on something else. Like these are just, everybody goes through this.

And, and I think this goes for a lot of people, like in my particular like skill stack is not only in financial technology, but again, inclusive financial access. Like I understand the behavior and the pain points of a variety of segments of the population. And I've lived not only in Europe, but obviously I, you know, I'm American, I'm Venezuelan, I've also worked across Asia.

So I feel like I was sitting there waiting to be recognized, especially as a woman. Or getting defensive and getting angry that people didn't see me. But the truth is, you don't see people. You're like, you just need to go. And the recognition comes and the respect and visibility and acknowledgement comes once you create value.

And sometimes you have to toot your own horn.

Monica Millares: Yes. Two. Two. Amazing. It's been. Great. Speaking with like, you've got, you've got like a full year ahead. We wish kindly like the very, very,