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Episode 17: Vectari’s Vision: AI-Powered Solutions for Next-Gen Banking Compliance

btb-ep17-vectari

   Kingsmen Beyond the Build YouTube Channel

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About the Episode:

This episode of Kingsmen Software’s “Beyond the Build” podcast features a conversation with Vectari co-founders Alexandria Villarreal O'Rourke and Chris Hart. They discuss their journey in using AI within the financial sector, emphasizing the transformative impacts of their AI-driven complaint analysis and regulatory compliance tools. The co-founders share insights into their personal experiences and the foundational principles of Vectari, illustrating how AI can revolutionize customer service and compliance in banking. 

  

Key Takeaways: 

  • AI-Driven Complaint Analysis: Vectari has developed tools that leverage AI to analyze customer complaints, identifying patterns and underlying issues rapidly, which can drastically improve resolution times and compliance. 
  • Regulatory Experience and AI Application: Both founders draw on their deep regulatory and financial services backgrounds, using their expertise to navigate the complex landscape of AI applications within stringent regulatory frameworks. 
  • Bilingual AI Solutions: Emphasizing inclusivity, Vectari has also pioneered AI solutions that operate effectively in both Spanish and English, ensuring broader accessibility and compliance in diverse linguistic contexts. 

  

Learn more about our guests:  

Alex has spent the last decade working on legal, risk, and compliance issues at the intersection of consumer financial services and emerging technology, including artificial intelligence. She began this work at the Consumer Financial Protection Bureau where she served as Senior Counsel in the office of Law and Policy and led the legal department’s work on Artificial Intelligence. She advised Senior Leadership, including the Director, on establishing guidance for governing AI models in a way that facilitated their use to enhance access to credit and consumer protection. Alex’s work led to the Bureau’s first ever No Action Letter, in favor of a company using AI models to enhance underwriting for non-traditional loan applicants. Alexandra was also the Bureau’s lead attorney on FinTech, regularly engaging with some of the county’s leading fintech companies on novel regulatory questions. She also specialized in Fair Lending and issues relating to customers with Limited English Proficiency. As part of that work, Alex led the legal department’s work on Spanish-language financial services. Following her work at the Bureau, Alex was a partner at two national law firms, where she led the FinTech groups and advised a variety of banks, leading fintech companies, fintech trade organizations, and Artificial Intelligence clients on regulatory issues, litigation, and product development. She also helped several of the country’s largest banks expand their Spanish language offerings and expand their language strategies. ‍ Alex then joined Bank of America, where she led the Bank’s Digital Banking legal work and advised the Head of Digital on the full range of legal and regulatory issues. Alex worked closely with Digital on expanding and refining the Erica artificial intelligence tool, the banking app, the Zelle product, and the Bank’s Spanish language offerings, among many other issues. Alex also represented the Bank in several trade groups of the largest banks in the country, helping to set the groups’ strategies on issues related to novel technology and regulation. ‍ Alex graduated from Harvard Law School and the University of Texas. She is originally from Monterrey, Mexico and lives in Charlotte, NC, with her three daughters and her husband.  

Chris has 25 years of business and technology leadership experience in both early-stage, high-growth startups and large, global banks. He developed expertise in digital banking and consumer payment systems with over 15 years of leading distributed technology and product teams. His transition to entrepreneurship most recently included co-founding and leading startups over the last decade. Chris was the CEO and co-founder of Levvel, a US-based technology consulting firm focused on helping transform their clients’ businesses with strategic consulting and technical execution services. Endava acquired Levvel in 2021 to complement and accelerate Endava’s existing business in payments, banking, and mobility. Chris joined Endava as EVP in North America as part of the acquisition, leading one of Endava’s global client delivery organizations. He was responsible for the company’s engagements with financial services, banking, insurance, and payments clients in the region. Chris and his team helped their clients adopt next-generation technologies with Endava’s agile, multi-disciplinary capabilities to accelerate digital business initiatives. Before co-founding Levvel and joining Endava, Chris primarily focused on consumer banking and payments technology leadership at companies like Bank of America, TIAA, and FleetBoston Financial. 

https://www.vectari.ai 

 

Production Credits: 

Produced in partnership with Mistry Projects: https://mistryprojects.com/ 

Connect With Us on LinkedIn  

 

About the Host: Kevin Carney, Managing Partner of Kingsmen Software

Kevin Carney is a Managing Partner at Kingsmen Software. As Client Partner, Kevin assists clients in their transition from Sales to Delivery and then maintains a relationship to ensure successful completion. A Finance major by training, Kevin bridges the gap between business and technology, especially for Kingsmen’s banking and capital markets clients. Kevin has 30 years of experience in consulting to financial services institutions. 

 

About Kingsmen Software:

We are dedicated, experienced practitioners of software development. We grow software iteratively and adapt quickly to changing business objectives, so we deliver the right software at the right time. Our proven approach combines processes, tooling, automation, and frameworks to enable scalability, efficiency, and business agility. Meanwhile, our advisory & coaching services enable technology leaders, business partners and their teams to learn and adapt their way to sustainable, collaborative, and data-driven operating models.

Kevin Carney  00:10 

Welcome to Kingsmen Software's Beyond the Build podcast. At Kingsmen, we pride ourselves on building enterprise quality software and we have the privilege of meeting some pretty interesting people along the way. Come join us to meet the visionaries, the disruptors, the entrepreneurs and the innovators that envision new technology solutions and then bring them to life. Come join us to hear what happens beyond the build. Welcome to Kingsmen software beyond the build Podcast. I'm Kevin Carney, one of the managing partners here at Kingsmen software. And we are in the Kingsmen bourbon studio at beautiful camp north end. With us today we have Bill Clerici, our intrepid CEO and sound and video engineer today. Welcome bill. Welcome, Kevin. So you had some issues earlier this week with a power outage, didn't you? Yes, we got. We got through it though. Did you? Did you have to bury that switch in the backyard? Yeah, we're going back to analog. Everything's analog now. Okay. No more digital. Your your back up and running now? Yes. Okay. That's good. Yeah. Also, joining us is Rashmili Vemula, our head of marketing. Welcome. Are you gonna say loud? Yeah. And you've been gallivanting around Europe for the past couple weeks, haven't you? We can have you recovered? Yeah. Late honeymoon. Good for you. All right. All right. Well, joining us today we have from Vectari Alexandria Villarreal O'Rourke. Like is that right? Yep. Okay. And Chris Hart, both co founders and CO CEOs of victory, right? That's right. Okay. So Chris, I've known you for a couple of years now. Right? You were with level kind of friendly competitors in the Charlotte tech market. And Alex, I've seen you and I can't count how many events I've seen you at in Charlotte throughout the years. But most recently, I started with Vectari and I saw you at the insider's award. You guys refer an award there? Right? Yeah. The risk takers award.  

 

Chris Hart  02:12 

That that is one of the names of it. Yes. Okay. It's the name we like better.  

 

Kevin Carney  02:17 

You like better? Okay. What was it? What else is it called?  

 

Chris Hart  02:20 

They have I think it's a slash. It's like, fail fast. Oh, slash risk takers. And it's like, I think risk taking is probably better than failing fast. Yeah. I'm not sure failing fast. I mean, yes, it's a good thing to do. But I'm not sure there's an award and want to win. Well, you don't have to worry because we did not. We did. Okay. Well, yeah, we refer to War Two. We didn't win, either. But when Chris, you and I were on a panel conversation here at Kingsmen software, pay CLT. about AI in the payments space. And then Alex, you just presented at sea, the South last week, right? So you guys are everywhere. So we had to have you on the podcast. So give us a little bit of background about Victoria. Give us some the stats. When you started, how many people you have? How you're doing your product development things? Awards, you've won things along those lines? Sure. I'll start. So we're coming up actually, on the year mark. I was just noticing this the other day, it feels like I don't know, for I think most people who have started companies before you're in the situation where you feel like it's either taken forever, or it was yesterday, but there's no middle ground. So we're both at the same time. Yeah, it does not feel like a year to me. It feels like either yesterday or like a decade. Right? Exactly. It's like kids, right? The days are long, but the years are short. Exactly. Yeah, exactly. So yeah. So we literally started the company at the end of May last year. And also you're just at a year. Yeah. It's literally like, you know, we're recording this on May 20. It's, I think, May 29. Or may 30 was our incorporation aren't we the cake. Sounds great. We'll come back in a week. So yeah, so we started a year ago. And, you know, as you kind of mentioned, Alex and I both have been involved in a lot of different activities and efforts around FinTech in the Charlotte community for for years now. And Alex and I got to know each other, actually, because of one of those back in the Carolina FinTech hub earlier. Sure. With dark. Yeah, so we were we were both working on the Carolina FinTech hub early on in that so that was really what what was the genesis of us meeting and really starting to talk about the opportunity that exists within financial services and financial institutions in general looking to adopt AI. We were talking about this, literally back in March, April of last year, and that was really what what got us started. So that was when we started and today we are sitting at five full time employees. We've got a couple of full time contractors and host of experts that we call on on a consultative basis and still very much in the the early days of working through Through pilots and proofs of concept, but that's, you know, that's kind of the beginnings of the origin story. Yeah. And I will say, so when Chris first so level, what, two years ago? Geez, that time also died after the sale of a company. So we sold that we announced the sale in at the beginning of April of 2021. And we had coffee right after and I said, whatever you're doing next you're doing with me. So you don't have to tell me what it is. But we're doing it together. So So you've been bugged for a while. 

 

Alexandria Villarreal O'Rourke  05:28 

You know, Chris, and I have just always had very, like minded ways of approaching the world. And, you know, they say, you know, one of the most important relationships you're gonna have is with your co founder, right. And so that's been, it's been perfect. And I've been, you know, I started my career as a regulatory lawyer. I started at the CFPB. I left there and 2017. And so can you spell it out? Where the Consumer Financial Protection Bureau? Yep, everybody's favorite. I was. I was a lawyer there. So I worked on everything from enforcement and supervision to collaborations with FinTech companies. And so one of the things we're working on is, you know, can you use AI? How can you use AI to responsibly, you know, make consumer financial services better? So, more access, more accuracy? Whatever it is. And that was a while back, right? Well, yeah, it was, I mean, this was, you know, we worked on the first no action letter for AI company 2016. I think it was published in 2018. But, yeah, so we were you know, we were doing AI before it was cool. But I've been doing it sense, right. So I wanted my areas of expertise became the responsible use of AI, model management, risk reviews, all those things, but always with an eye towards not, you know, we would always say like, you know, not not trying to have it be a hammer looking for a nail, but really starting from the problem and seeing whether AI could help. And so, you know, they did a little bit of work for the law firm, I advise a bunch of AI companies, and then at Bank of America, obviously, Erica was my client. She's an excellent client. Never complaints always a claim. So explain that as well, to everyone make sure you know, Erica is Erica is Bank of America is AI powered Chatbot. And she is a dear friend, 

 

Kevin Carney  07:09 

and colleague. So you gotta make sure that everything that Erica said was, was legit, like, like it followed all the compliance rules. Right, right. So, you know, 

 

Alexandria Villarreal O'Rourke  07:20 

basically making sure that as we developed her, we, you know, had an eye out for anything that could go wrong and prevented it. And then I went to be the general counsel at a tech company called Balan. It's a mortgage servicing company in New York. And I started, I guess, a year before we left. But right kind of towards the end of a is when Ginny I came out and I've been doing AI forever. And I was so excited. And I the very first salmon had just come out had just been in the New York Times, I think the week before. And I had just paid $70,000 for a compliance review of something. And I without using any client data, I replicated the review on the old one, I'm in charge of PT, two or whatever it was three, three. And it was perfect. It was literally super accurate, like down to the point. And I had just paid so much money. And I thought, okay, like there's something there's something here. My CEO at the time was very tech forward, as you can imagine, it's a startup. And so we started thinking through, you know, because I kind of knew where all the bodies were buried in terms of risk, I knew what regulators were worried about how to prevent it, how to build a model that they would be happy with. We were pretty confident using it early, we were one of the early commercial contracts for opening it, that was a fun experience. And really, from there, sort of my calling, right, I've been doing this forever, I'd always wanted to do more of it. And as soon as I started seeing how we were basically going from like, you know, horse and carriage to Tesla, like, you know that, that kind of level of jump in both like the accuracy, the speed and just the ability to analyze text, which pre compliance data, what you're doing is analyzing text. So that's around the time, Chris and I started talking, I did give my my company the right of first refusal, I said, you know, can we turn it into an AI company? And they said, Okay, we can do mortgage AI. And I was like, Okay, so that's when Chris and I started thinking through some of the issues. And you know, our goal, basically, is to take a lot of the work that we've done in the past, you know, such as the compliance reviews, and of course, on defense and all those things and do the things that were really manual and what were, you know, I was hiring 30 consultants at a time to go look through, you know, complaints at the request of a regulator, doing all these things that were really manual and really inefficient and training an AI model to do it with the training of an expert, right, because I think an AI model basically can replicate the expertise of an individual but it's only as strong as individual. Right. So everybody we will work with, to train the models has been the Chief Compliance Officer head of risk. You know, these aren't financial institutions. We're getting a lot of, you know, friends with a company that are kind of late career compliance and risk executives who are really excited about AI but don't don't quite want to launch a startup. And they've been just incredibly helpful, because it's basically like being able to replicate the brain of a Chief Compliance Officer for bank and just create 1000 of them and just, you know, set them loose. It's magical. Yeah, so it's been a really exciting path. The other thing that I'm really proud of, it's sort of a core to what we're doing is everything we do in Spanish and English. I'm a native Spanish speaker. So the speed of my speech. And that was something that's, throughout my career been an issue, right? So the CFPB, there, I worked on how to get Spanish speakers to better have better consumer education and better able to file complaints to date, the CFPB takes complaints online for English speakers, but if you're a Spanish speaker, you have to call in even the Spanish speakers are a giant number of the complaints. At the bank, I also worked on, you know, making Spanish services more accessible. And so it was very important to me that anything we did had that angle. And so one of the very first things we did is we built the regulator quality AI translation model focused on financial services. So we taught the model to speak the way the CFPB speak Spanish down to, you know, turns of phrase, but specifically the vocabulary that they want banks to use, like the financial services, vocabulary, right? So like, how do you translate interest rate? How do you translate, you know, whatever it is? Amortization Schedule? Exactly. Right. And we did you know, we reviewed 1000s and 1000s of translations, I reviewed 1000s of translations. And you know, what was funny is there were there were some errors, there were some errors even in their published content, because you know, Spanish recisions hard, it's a, it's sort of an art. And so being able to train the model to replicate that art has been really incredible. I mean, that the Spanish that it translates is better than anything, any lawyer that I've ever met could do. It's very clear. It's very accurate to be conversational. But it's also just right. It's exactly kind of how you would say it. And so that was I think the Yeah, the very first thing we kind of launch with, did we talk about David? No, yeah. Okay. Well, let's just talk about the three products, you guys have listened your website. So you had mentioned the translation starts. Right. And that's transformer, that's the model I was just talking about. Yeah. And then kind of our bread and butter, it's just become because of something's going on in the market that's become our biggest kind of source of interest is complaints analysis platform. So it basically, it's mostly used for oversight. So it allows you to look through all your complaints for hidden trends and issues that might look like nothing, but turned out to be vendor error, bugs, whatever it is. That's a lot of the work I did for all of my clients as a lawyer, there will always enforcement was always not something that the bank or whoever it was, they don't purpose, it was always something they hadn't caught in time. Right? Where they were saying, like, oh, we thought because consumers were confused. But actually, it was a bug. And so we have trained a model to categorize and identify complaints that are reflecting a pattern that indicates above or another gap, we've been very successful in identifying some, some niches for our clients. And obviously, the the idea is you identify the issue, you corrected, and then you prevent it from happening again. And so then you're sort of saving yourself the risk of enforcement and the money that it costs to resolve it on the back end. Does that product have a specific name, or we call it like Tara insights, you know, TBD? Yeah. And so that's been I think, what's been fun about that product is everybody that we've used it with that we've done a bit of concept or, or pilot has a lot of ideas for what else we could do with it, right? We could do fraud claims we could do vendormatch. You know, they're sort of, it's been really fun to see our clients kind of fall in love with something and then say, well, we could also like, It's fun when they start to think of it as like, their, their project. So that's, I think what we were, we probably are focusing most of our time, 

 

Chris Hart  13:56 

we've definitely gotten the most kind of market signal around the policy insights, or the complaintant side specifically, right. Okay. Okay, so so we've got some great background here. We've got what I mean by that is like your personal journey, you got your an attorney, Alex, and you've got a lot of banking, financial services and regulatory background. Chris, you're also in financial services, law, heavy payments and consumer. And you've got the technology background, but it sounds like the missing missing gap there. Right. You need to bring in another person to help you out. Yeah, there was the third leg of the stool is a guy named David dirt. And so David's background, he also started in financial services. So computer scientist by training, and started at the Federal Reserve Bank in Richmond, and eventually ended up starting a company called notch and Notch was and an AI and ML consulting firm based in Richmond before AI and ML was super cool like it is now but they were doing super cool stuff back then. And that led to Capital One actually. firing them back in 2018. And so, as a lot of people know, Capital One that's made significant investments in AI. And one of those investments was the acquisition of knotch. And it became a big part of their center for machine learning. And so David went on to be a director in that team. And you know, was at Capital One, when a lot of really exciting stuff was being built. After Capital One, he went on to run AI teams at meta, and at Springboks capital, a FinTech company, and ultimately decided to join us, I had the good fortune of getting introduced to David through a network that existed because of level. So have some folks that we have on the level team that have worked with David before, and also one of levels investors who had also been involved with David and some of his earlier companies put us together, and actually, I met David, pretty much the beginning of the pandemic time. And there just wasn't quite the right thing for us to work on together in q1 of 2020, because the world was on fire, but he was one of those people that was just like, oh, my gosh, I, you know, I'm gonna keep in touch with this guy forever. And literally over the course of a couple of years, you know, every once in a while we, you know, touch base or end up talking about something. And then when Alex and I started talking about all of these opportunities that existed in financial services, David and I were chatting to, and just, it was the perfect complement to what Alex and I could bring to the table. So it's been great to have him be part of the company. Now, it's great way to hear the band get together. You also some funding to write it sounds like you got funding very early on before you even potentially even before you quit your day job. Right. Yeah. So we raised in October, September, October of last year. And, you know, by that point, we were still we I think we had the beginning kind of inclination around what we wanted to focus on, clearly, because we had to talk to prospective investors about it. But we knew that there was, you know, selling to financial services, regulated industry, you know, just bid big or even medium sized banks is a long and complicated process. We get it. Yeah. So, you know, there's a certain amount of, you know, chicken or egg, kind of, you know, thought process that goes into like, well, you know, where do you start? And how do you feel about the market, and, you know, anything that you sell has to be developed to a certain point before you can even have any chance at all of being able to, to monetize it. But yeah, as a result, you know, we did go out to raise money. And, you know, was very fortunate that we had a network of folks in large part from the X level days, who were big believers in what we brought to the table and the opportunity that we saw in the market. So, yeah, we raised about 1.7 million back in October last year. Well, that's, I mean, that's a large amount of money for I mean, what did you have at that point, like in articles of corporation, like you didn't have a profit at that point in time, right. Just division? Yeah. Yeah. I mean, we obviously we had the company formed by them. We had the beginnings of a team put together Sure. We were actively building things and doing r&d work. We were in early conversations with both an early large bank design partner and some folks who would ultimately become an early pilot customer early proof of concept. But yeah, it was it was super early days. And, you know, I think it's the good fortune of having people who believe in you, and also having been through it once before with the level experience. I think that that certainly made it a lot easier. I would not have wanted to do what we were doing from a racing perspective. As brand new entrepreneurs, I think that that would have been way more challenging, right. Yeah. So you guys come from a great background. You've had a couple of exits, too, as a track record. You're not 25 year old kids. I think he knows something about regulation. 

 

Kevin Carney  19:09 

You wear it well. Okay, so let me let me let me jump into a sidebar here. So you guys are you know, clearly you guys are experts on complaints. So a couple complaints statistics that let's see how well you guys know, inside financial services and outside, so if all right, so let's figure this out. According to the Consumer Federation of America, I'm guessing that's legit organization. It looks like a legit website. So is it an AI created? It's not at least I saw what are the top 10 consumer complaints and this goes beyond. Like these are industries beyond just like financial services. 

 

Alexandria Villarreal O'Rourke  19:52 

So I can tell you the number one complaint for financial services is credit reporting. Oh, that's the question to 

 

Kevin Carney  19:59 

a second. Hmm. So what industry? Like? What is waiting? What industry? I'll give you a perspective like number seven is healthcare. Really? Yeah. 

 

20:11 

What 

 

20:13 

will be the highest? 

 

Kevin Carney  20:15 

Good cost? It would actually make less sense to you. I mean, I assume financial services, but maybe not. What do you think if it's not number one, where do you think financial services? Oh, consumer debt? And I would say it's top three. You're right. It's top three. Is it? One? No. It's three. Okay. What's number one? You know it. You do know it? Like, intuitively you know it? Where do people the DND? Yeah. Yeah, sounds repairs. Right. Okay. I can see that. Yeah. Number two is home improvement. People come into your house and contractors and yeah, that that roofer that knocks on your door says, Hey, I happen to be driving by and I can help you fix your roof and see this out that one of the pictures says something like 54% of all remodeling projects end up in a dispute. Really? Yeah. All right. Makes sense? Right. Landlord is number five, landlord and tenant. 

 

21:12 

Okay, how many people complain about like, or do they complain too, if you have a problem with your landlord. Anyway, small things? Yeah, but Better Business Bureau? I don't know. Landlord. 

 

21:23 

Okay. 

 

Kevin Carney  21:25 

Okay, now let's go back into financial services. Right? This is your bread and butter. So according to the CFPB Consumer Financial Protection Board? Yeah. Over the past three years, what other percent complaint by type? Oh, boy. Okay. You're sure to do this one, though. Credit reporting? I wouldn't say it's like, it's large. It's like 35%. You read all your stats? It's 47. Okay, I think they changed the categorization because I noticed that like, like a when there was there was credit reporting, and then like credit services that went down to 01. But up to a taxonomy change. Yeah. Yeah. Yeah. So 47%, the next highest is 1/10. At 4%. 

 

Alexandria Villarreal O'Rourke  22:20 

So amazing. Debt collection. Yeah, I would have thought that cholesterol is much higher. You know, what I think it is, is that the consumer group community has done a really good job of educating people on credit disputes. And there's, you know, they have a forum, like if you walk into any consumer group, they'll help you file your, your dispute. So there's just it's, there's more people know about it. There's, there's kind of an automated way to do it. Okay. Okay. 

 

Kevin Carney  22:46 

So yeah, everything else here like credit card, or prepaid card, checking and savings, mortgage, money transfer, car loans, student loans, with a car loan student a little bit higher up in the list. And then payday loans a very bottom, huh, yeah, only point 3%. 

 

Alexandria Villarreal O'Rourke  23:05 

That's weird. I wonder whether so, you know, the CFPB is 

 

Chris Hart  23:09 

purview is large banks are another very large financial services company. It's I wonder whether they don't count complaints that are not within their purview? Oh, I see. And I think a lot of those complaints also end up with state regulators, in many cases, because the, that kind of debt is often regulated on a state by state basis, and the state regulators, most of them don't publicize the data the same way that the CFPB does. So there's a lot of like blind spots, if you if it's a type of complaint, that doesn't end up with the CFPB, it's very hard to get public data. This is where knowing the data is really helpful as well. The other thing that is interesting that gets masked in this categorization is that you don't know how much of that is actually about fees. So that this whole like matrix of what complaints are about like there's a product level kind of analysis, which is what you're talking about. But then there's also like, what is the nature of the complaint within that category? And so, you know, 50% of the people across those categories could have been complaining about CS, but you wouldn't know it from the way that the data is kept. Right? Right. Yeah. And payments is like, sort of topic wise, it cuts across all of them. It's one of the most common because, you know, my payment didn't make it on time, you didn't mark it as paid, whatever it is, payments today on the slide 5 million complaints for the past three years. But I read a stat somewhere true or not, I don't know. But it said something like, you know, for every 25 complaints, only one is actually captured. So the depth is probably much better. Yeah. Well, it's also worth noting that these the complaints that are on the database that you're looking at are the complaints that ended up with the CFPB. But not all complaints about banks obviously end up with the CFPB. So you know, there's the number of complaints that the banks receive on their own. Some of them end up with the better business bureau or the FTC or somebody else. So the CFPB again, is like this very rich set of data, but it's only one of them. Right? All the written complaints are only in English, so we'd love to see but all the Spanish. Okay, good. 

 

Kevin Carney  25:10 

All right, third one. We all know that pop culture notion that Karen Karen's complaint a lot, right. But as reported by Forbes, there is a humorous study by a company named bionic. And Karen's are not the most prolific complainers. In fact, they only ranked number eight. So what female name is the highest? Don't worry, we'll get to male names later. As built. Careful, careful. That's probably true, but so let's just listen women's names. Let's see where they found the list. Alex is a big complainer. 

 

25:45 

There's no Alex. There's no Alex. I'm trying to think of like the demographic that would be most likely to complain. donna, donna Donna at the top 20 Here. 

 

Kevin Carney  25:58 

Jane Jane's number three. Yeah, Jane. What why'd you COPPA chain? So yeah. 

 

Chris Hart  26:07 

Actually, I don't know any Jane's I don't think that have complained at all. But if you think about the demographics, right, so to Alex's point, you start thinking about well, who are people who have enough financial products and have the types of relationships that would generate complaints? So that's people who were probably born in 1990 or earlier and then you start thinking about what are popular names? Yeah, there's no Beatrice on here. So you know, you'd like James Jennifer's Linda's Stephanie. 

 

Kevin Carney  26:39 

There's no Jennifer strangely enough. There's a Linda number 17 And Linda. Yeah. 

 

Alexandria Villarreal O'Rourke  26:46 

Mary. There's got to be married. Come on. 

 

Kevin Carney  26:50 

There's a Marie. Maria. 

 

26:57 

Yeah. 

 

Alexandria Villarreal O'Rourke  26:59 

All right. How about men, John? John's a complainer. Nailed it. Is it number one number one by double the you know that they're more the fortune 500 companies more CEOs named John than women. So yes. Really? Yeah. 

 

Kevin Carney  27:16 

piece of information. Yeah. I think about it a lot. There's no There's no Chris on here. That's That's crazy. Right. Very easygoing. Chris. Is there a bill? There is a bill? There's 15. William? Oh, yeah. 15. 

 

27:30 

There's no Kevin's on there. Like Bob has to be up there. Right. Bob? Or Robert. Robert Roberts. Number six. Michael, David. David's number two, like Yeah, John and David. 

 

Kevin Carney  27:42 

You're like, I think I just become the AI No, ever. So it's no longer it's no longer Karen right that the number one female is according to bionic if you want to trust them, but Luis says 4.8% of the complaints are by Luis interested. And number two was an I want to put an In fact there's an with E and and without any. You add those together like Maria and Maria. 

 

Alexandria Villarreal O'Rourke  28:20 

Maybe it's what people say when they want to see that? 

 

Kevin Carney  28:23 

Maybe? All right, let's get back into it. So with your with your Victoria insights that you mentioned earlier, you had said, Alex that you can go through all sorts of complaints at a much faster rate. And I read somewhere out of use a quote that you said that used to take you and 15 helpers three months ago, they can do down a day and a half. So it sounds like efficiency is a huge play for this. This product. How does 

 

Alexandria Villarreal O'Rourke  28:57 

both customer satisfaction like just in general you want to do what better for your customer, you want to find out where the complaints are. And then also meeting your compliance goals like how do those play into the efficiency? And how does that play all the all those three of those play into your sales pitch to your or your so I think there's too many angles to it. Right? So right now, there's a huge emphasis on banks that are working with fintechs right? So a lot of these FinTech companies that you borrow from or deal with, have a bank backer that's, you know, you don't really see them. They're sort of on the on the back office. There's a giant regulatory emphasis right now on the failure to monitor your bank, your FinTech partner, right. So the bank owns that relationship. Regulators have said you bank are on the hook. I don't care who your partner is, if they get a complaint, you get a complaint. Traditionally, that's not really been the model. It really has been, you know, typically, a little bit more hands off, or the FinTech company handles their own complaints. And so there's a lot of emphasis on that. And I read this morning that 35% Of all the enforcement actions last year We had a component of the oversight department oversight. And so I think, you know, showing that you're overseeing your complaints correctly, I think is that they want. The other kind of trend that's affecting this is both the CFPB. And state regulators have taken the view that if you knew or should have known that something was broken, then you're on the hook, and you're on the hook, as if you knew, right, you're on the hook, kind of like a reckless disregard kind of flavor. And so there's a lot of emphasis now on what does it mean, that you should have known? Right, right. And so a lot of banks will say, Well, I did it look like a customer error. Right. And so, but now, regulators themselves are starting to use technology, right? And so their view is, if I can, no, you must know, right? So if I have the tools that will tell me that you, you know, 

 

Chris Hart  30:48 

maybe apply the payment on the wrong date, or whatever it is. And then I'm going to assume that if I have the tools, you have the tools, and so there's a lot more emphasis on analytics, and like really kind of deep diving into common consumer problems. One kind of advantage that we have is we see them across, right? So we're able to look at, you know, Bank A, and say, you know, banks, B, C, and D had this issue, do you have it, and I can tell you most of the time they do, right, because a lot of things isn't the same vendors, there's a lot of indoor issues, vendors will always tell you that they're not the problem, right, you're always playing the customer. So there's a lot of that there's a lot of pressure, upward pressure to be more accurate, catch things sooner, you know, detection being a big one. And obviously, cost, right, it's very costly to fix this complaint from the back end, because you're you know, you're getting refunds, you're, you know, paying an analyst to go research it and you're still nobody, like the bank doesn't want complaints, the customer doesn't want a complaint, like nobody wins when something gets broken, I think there's a misperception that banks are sort of trying to trick people into making a mistake, so that they can get like, that's actually really costly. So be a terrible business model. So I think there is an actual, you know, downright savings in preventing a lot of these complaints. And obviously, that risks it. But I think the other point of pressure is there's a lot of competition today, between banks and fintechs. And hope fintechs whole vibe is like, you know, we're friendlier, and we're easier to use, and we understand you better whatever, right. And so there's a lot of emphasis on trying to keep up with that narrative and be be able to sort of serve the customer where they are, and really care about the customer. And you know, use the tools available to them to have that relationship be better. So it's kind of a dual pressure between cost and risk on one hand, and then competition on the other. And so, for all that, I mean, there's really now I think, the ability to prevent unnecessary breaks, gaps, vendor issues is kind of a business imperative across the board. Now, one of the benefits of solving these types of problems is that you can make the business case a bunch of different ways. So if you're an organization that's very compliance focused, and you want to build a business case around reducing regulatory risk, great, like we can help you reduce regulatory risk. But if you're an organization that's motivated by operational efficiency, okay, great. Like we can talk about how to how to receiving fewer complaints factor into the efficiency of your call center, like the, you know, the best way to reduce costs in a call center, where you're taking complaints is to just get fewer complaints, right. And if you're gonna take the complaint, then resolve it faster. Like all those things make a huge bottom line contribution. Or if you're just really motivated by customer experience, which you should be, we would like all of our customers to be motivated that way, then great, like, you don't have to make the business case based on operational efficiency or risk avoidance, you're just going to say, well, our customer is going to be happier. And we'll measure that by NPS or whatever the ways that we measure it. But as a result, we can we can help our customers think about the problem through any one of those three lenses, depending on what their kind of orientation is, right? I know a lot of our big bank clients are very motivated by regulatory issues and compliance. So that would be the selling point. There it is. But it's interesting, because there's kind of like a tale of two cities in this. There's the banks that are very active in the embedded finance or banking as a service space, the kind of partner banks that Alex was mentioning. And because of the intense scrutiny that they've been getting recently, the FDIC has been going around one of the other big regulators of that size of bank handing out consent order after consent or, and all of them have to do with this regulatory oversight. So the banks that are in that category are very motivated by being able to be more tuned into how to handle these regulatory issues. And in some cases, the FDIC will literally tell them, like go spend X amount of money or go hire Y amount of people to deal with this problem. But for other organizations, it's really hard to know how much risk you're reducing, you know, like, how do you prove like, oh, you know, by spending, you know, $500,000 on this, I avoided you know, $30 million in fines, right. It's, it's impossible to know. And that's what makes the business case tricky in some cases is that you can't 

 

35:00 

but you can't really quantify that until you, you know, until after the fact it's just too late. Right. So a more kind of practical measure across the board is CSAT score, right, the customer satisfaction score score. So if you fix a lot of the issues that are causing customers to complain, it would send a reason that you would have you no more satisfied customers. So that's one thing we look at. The other thing that's actually for what we're doing very relevant is first time call resolution. And so it's extremely expensive to have a complaint that takes several calls, because then you have your initial time spent, and then you have your research, and then they come back. And there's another right and there's more likelihood of a regulatory complaint. When you don't have first time common solutions. That first call resolution. It's one of our goals is one of our kind of measures. But I think between the CSAT score and first call resolution, I think those are kind of two measures that kind of make the abstract more concrete, right? Like those are numbers you can look at talk to the word about shoulder is working. Okay, so who are your target clients, banks, big banks, small banks, beauty banks, fintechs, all the above. So all the above in the kind of macro sense. But the focus that we have right now is on the what we consider to be the mid market banks and mid market banks for us is really anybody 5 billion in assets. And above is in the the mid market technically. So that's like the top 250 banks in the US by asset size. Where we see banks most interested in what we're doing are typically banks that are around eight or 9 billion, they're getting up till the 10 billion mark, which is where things tend to change from a regulatory oversight perspective. Yeah, yeah. And, and then all the way up to the kind of bigger mid sized banks, which are you think about, like super regional banks that are in four or five states, and they're typically anywhere from 25 to 50 billion in assets. But there's also this other kind of non bank, financial institution that's off the radar for a lot of folks. But is has this very same problem that we've been working on lately, which is in the mortgage asset owner space. So I don't know, Alex, we want to talk a little bit. And it's not your more just Morgan. So there's a lot of spaces where there's a lender, it's similar to like the bank FinTech partnership, right, there's a vendor who owns a loan, but they're not the ones you're actually going to deal with, right. So they generally have in mortgage and auto and a bunch of other places, student loans, they'll have a sub servicer, so the sub service areas who you think is your mortgage company, auto company, whatever it is, you have no idea who this, you get this. 

 

Alexandria Villarreal O'Rourke  37:32 

And you call them whatever. And so but it's the same construct, right, so the asset owner, so whoever actually owns the loan is on the hook for anything that happens to that loan. So if you have a sub servicer who is, you know, maybe has an error or a vendor issue or whatever that they don't know about, and it's causing a lot of complaints, you're really supposed to be catching that and addressing it. And so there's a real kind of oversight duty. And so that's one of the things we're working on is sort of making that process easier, not just detecting it. But okay, so you found it now what, right sort of really walking them through, here's how you'd run a risk issue. Here's how you would interact with your steps. I was there on it. That's been, I think, a really underserved part of the market. That's, that's ripe for disruption. So that's what we're 

 

Chris Hart  38:13 

giving to the AI models that you guys have. How did you train those you turn those on? The CFP, the data? Or did you train it based on some client data? Where's your where's your base? And as kind of a side question, is that? Do you customize that for each client? Or is it a is it broad based? Tell us about your how it's built? Yeah, it's a great question. So we do look at CFPB data. But we also look at proprietary internal customer data. One of the things that we touched on earlier when you were talking about the CFPB data is that really only a certain subset of complaints end up with the CFPB. And so for most organizations, they're sitting on a pile of complaints that they've taken from a variety of different channels. And the complexion of those complaints may be very different than what the CFPB data would represent. So the CFPB data is interesting and helpful, but it's not really the complete picture. So we do label and train based on a combination of proprietary complaint data that we get from customers and also using public data. And we also do a variety of techniques to be able to get out not just what is the complaint about but some of the other characteristics of a complaint. So some of this is like secret sauce. But to give you an example, one of the things that's really important to know, at the end of a complaint is we're at the end of a record about a complaint is was this resolved or not? And, you know, that actually is kind of a hard thing to figure out and not everybody has the same definition of whether it's resolved amicably or the regulators do. I mean, there is there is a right answer, but not everybody necessarily uses the answer that the regulators would consider to be Correct. So that's a part of what we've been doing to be able to analyze complaints. The other thing is that there's, you know, there's definitely the AI component of it that uses a combination of MLMs and other kind of generative AI techniques. There's other things that we do, to process the data to be able to cleanse it and do other types of analysis on it. So some of its generative AI some of its non generative AI types of of techniques. From a customer specific perspective, one of the things that's been really interesting is that different organizations have different ways of thinking about complaints and categorizing them. So while on the one hand, we do have our own proprietary taxonomy of complaints, in terms of like, how would you categorize the complaint, oftentimes, we'll talk to a bank that has their own categories, and they want to continue using their own categories for lots of very legitimate reasons. And so part of what we have to do is figure out how do we map their complaints onto their product categories, even if we might have a different way of looking at it? So it's also by product category, whether it's mortgage or auto loan or credit card? Or Yep, it basically depends on what the bank wants to do with it, like who's going to deal with this? Is there going to be the mortgage team or the payments team? Right? So we sort of work with them on what, what's the outcome of what we give you? Or who are you gonna send it to? Right. But one of the things that's been really interesting about large sandwich models in particular, so generative AI, is, you know, as you know, from your work here, dinner, Avi, the beauty of it is that it's not wordsearch, right? It's concept search. So it really kind of understands what the complaint is about, not just what they're saying. And so we're finding that, you know, surprisingly enough, there's a lot of complaints where the agent is telling the customer Oh, yeah, this isn't broken forever, right? They're not saying it's a known issue, right? They're saying like, Oh, this has been a headache since I started working here in 2017. Right, like, sort of, in many different ways. And like, that's a nightmare for the bank to to have, you know, an agency. This has been a problem since 2017. The agent has seen the pattern, but sometimes, yeah, and so I think that's been, that's kind of the beauty of being able to use a sort of concept search is that you don't need to know how somebody would say it. If you can explain the concept of the model, it will find all this though. So we have found a number of places where somebody will say, you know, apparently agents complain about vendors all the time to customers throughout. And so, you know, we've been able, they usually won't say the vendor name, though. They'll say like, oh, yeah, the company that does that work for us, blah, blah, blah, blah, blah, right? So it's been fascinating to see, you know, how much agents will tell about internal issues. But being able to detect this is huge, right? Because then you're able to say, like, Oh, tell me more about this thing that's been broken since 2017. The benefit of this generative AI and the the idea that it understands the concepts is that a lot of this kind of complaint analysis work that's gone on using non Gen AI techniques, is very quantitative in nature. So thanks, me know that they get 100 complaints about a mortgage servicing issue or 50 complaints about a wire transfer issue. And that might be where the reporting stops, and a lot of cases that is where it stops today. What hides in those numbers is where those 50 wire complaints 50 complaints about the same thing that have been going on for the last eight months, or the 50 new things, is it 25 and 25. Like all of that richness that exists it like it's in the complaint data, you're just not getting it today. And I think to Alex's point, the risk that organizations run as if you're sitting on a bunch of complaint data and you know, oh, yeah, like we you know, that's 50 complaints about wires is the baseline like there's nothing wrong with that like maybe but maybe not you know, maybe there's an issue lurking in there that actually you are you should be aware of, but the it's not going to stand out to you if you look at it through a purely quantitative lens of this many complaints in this category are reliant on the agent to tag those in the heating sadly correctly. Oh, well, if you think about you know, a few years ago, we had the account opening scandal remember all the banks got in trouble for essentially folks had perverse incentives to open fake accounts and then close up 

 

Alexandria Villarreal O'Rourke  44:16 

employees of the bank right and so you can imagine like those are technically account opening complaints, right? So somebody called in and said I didn't open an account but it looks like so they got grouped with like, you know, in some in some cases got group with all the other account opening issues, right? I couldn't I couldn't log into the website, it didn't connect my password have an apostrophe, my last name and right, so that's all account opening. And so you just had a lot of account opening complaints and so people would sort of think like, Okay, well, we got to improve our account opening process because people really hate it. Let's get better. Right? How do you think accounts? So it's frequently the topic they allege a topic or the complaint will hide some some meaning under it? Right. And so the number of complaints that go into the customer error bucket is is just giant right? Given, there's just a lot of places where the customer is saying like, No, I promise you that I put, you know, the 17th of every month and it's going on the 18th. And they, I mean, the agent can only see that one instance. Right. So, to them, it looks like the customer put 18. Right? What are you gonna do? But to us, we're able to see like, really like every customer, but at one day, that's very unlikely, right? We always joke that it's sort of like a, you know, you always know the person in college that like had, you know, one crazy ex girlfriend like, okay, fine, but like if they had like 17 delegates, we have a common denominator. 

 

45:34 

I have no recollection of that, Senator. 

 

Alexandria Villarreal O'Rourke  45:38 

But I think that the common denominator is where we shine, right? Where we're able to say like, it could be an error, it could be a mistake, or you know, 

 

Kevin Carney  45:46 

an accident, but it's just too common. When you're when you're in the trench, it's it's hard to see the forest for the trees. Right. So I probably mix my metaphors. So it sounds like AI is a great, this is a great use case for a first gen AI. 

 

Alexandria Villarreal O'Rourke  46:04 

As Chris, you and I saw it the PCL T right, the first thing people raise the question is it's a bank, it's how can you use Gen AI, all the risks and whatnot. But it sounds like the regulator's are, are hit to use this technology, right? Because it can improve customer satisfaction and customer protections. So are they I know that regulators never say yes, you can do it. But they, they're good at saying no, you can't do it, are they? Are they not saying no, you can't do it? Or not? So actually, I think it was today, Congress, Congress came out with their list of priorities for AI. And one of their priorities is to ensure that companies are able to use AI to prevent problems, like they're sort of going all in and saying, like, ship has sailed and whether or not it's going to be used, it is going to be used. And therefore, I did talk to, you know, one regulator off the record, I won't say what country or what state or federal, who said, basically, there was a window in which a regulator could have come out and said, Nope, you're not using it, you know, until we know it's safe. And that wouldn't have passed. And I think part of the reason it passed is because in that window, and this sort of like three months, right after you had all the articles about opening I, the White House actually worked with open AI and a bunch of AI companies on these, like, you know, AI Bill of Rights kind of, and my, my, this I don't I don't know this for a fact. But I sense that it would have been very awkward for the White House if a regulator came out right after that and said, No, you can't work with any of those companies that the White House literally just sat down with. And so I think that was a brilliant move on the part of the AI companies. But it also made it kind of an assumption that we were just all going to use it and just try to figure it out how you said, so I think where we are now is not can you use it, but just how are you using it? Right? And I think what we tell our clients is, you know, you can I can be a shield or a sword, right. And so a shield will be for like, marketing or, you know, loan underwriting whatever it is. I'm sorry, sir, would be a shield is to protect your customers, right? So anytime you're using it for the benefit of the customer, I feel like well, I know having been on that side, that the regulator will work with you a lot more than if you're using it for your own financial benefit. Now, it's all financial benefit, really. But if you're using it in a way that's only going to benefit the customer, they will work with you to figure out how to do that. Right, right. We did another podcast guests, like this was last month, I was using Gen AI for credit monitoring on the commercial side. So 

 

Kevin Carney  48:35 

it's called Trace. And if you have a portfolio of commercial loans, usually monitoring you have is really the financials of seven cents at the end of the month or end of the quarter. And you still have to go process those statements, spread them. And then you realize that they're doing poorly. But if you track some of the social media, or Yelp reviews, or newspaper articles, you'll see some of those things come at a time. So it's it's an additive way to to improve your risk management and really consumer appliances risk management aspects. So there's always there's positive right now maybe maybe people can bring up the data privacy. So that's, that's my next question is data privacy. If I'm a middle market bank, and I want to take your base 

 

Chris Hart  49:20 

taxonomy of data, and I want to include my data on that, what are my options? That's the first that's the second question you've lost. Yes. Besides regulations is data privacy. How do you how do you handle that? So there's two aspects to that that are important. So one is that the PII that can exist in complaint data as it hasn't been redacted? is usually not material or not important for the AI processing? Yeah. So you know, as an example, like if you look at unredacted complaint data, a lot of times what you'll find is you'll find account numbers or you'll find names or addresses or you know, whatever. And you know, whether An account is open at 123 Main Street are the loan number is, you know, 1234567 is not really relevant to the pattern that we're looking for. And so either the bank can redact that data before we even get it, which is fine because we don't need it. Even if they do send it to us, we don't need it for doing the processing that we're doing. So we have our own reduction processes that go further than not just PII, but also looking at things like access mentioning the vendor names. A lot of times vendor names, even though the bank may not tell the customer Oh, yeah, it's x vendor. Like they'll write that in the complaint data. Okay, the fact that it's excellent, or, you know, we can we have techniques for being able to remove the specific mention of that, we can always get back to it if we need to, but that can be removed from both the training data and from the inference data, because it ends up not really being important. What did we do tokenize it right. So we'll know if it's the same customer, we won't know who it is, because we don't need to, but we'll know that that customer had a auto loan issue and a mortgage issue. And you know, because that's important, was that Luis, that was probably Louise or John. So, so yeah, so that's, that's one factor to it. The other factor is, you know, we, when we talk to banks, if they have specific requirements around how to handle data, you know, we're happy to look at ways to meet them, where they where they need to be. So like, as an example, we were talking to a bank just in the last week or two, that one of the things that they wanted to do is not just do all the PII scrubbing that we were mentioning, but also once the categorization was done, they wanted to be able to remove the data that went into that complaint, so that it wasn't present anymore in the database. So there are things like that that we can do. The other thing is, you know, I think this is one of the elements of what we're doing that is very finely tuned to the needs of financial services is, we have a very robust way of tracking what data goes into training, because this is part of what the regulator's are expecting banks to be able to do. And so being able to say, literally down to like the row record level of training data, this is where the data came from, this was the legal foundation of why that data was able to be included in a training dataset. And if at some point in the future, that data can no longer be included, we know what training was performed with that data, we can remove it, we can retrain. So being able to have that level of lineage through sure the data is free. So I think you know, that's part of our selling point, is it like, you know, we're real adults, we've been doing this forever, we've been on the other side, right? So we'll never fight you on your NDA, we'll never try to use your data for anything fishy. Like, you know, the whole point is, we've been you and we know what you're concerned about. And you know, what regulators are gonna ask you about? And so we're just going to preempt the all those questions, right? Like, I always tell clients, like put me in front of a regulator, I love regulators, right, I'd love to tell them how the product works. So AI governance is actually a huge part of what we do, you know, testing, measurability, accuracy, like all the things that you have to show a regulator are things that we care a lot about, and that we pay a lot of attention to. 

 

Alexandria Villarreal O'Rourke  53:11 

That's great. Sounds so fun, how like, just real fun at parties? 

 

Chris Hart  53:15 

How far along are you with with building out the product? In terms of r&d? Yeah, so the the translation products, we had that product built at the end of last year, we continue to improve it. But we we did a proof of concept in early pilot with the customer. Literally, those translations are live on their website right now. And the complaints analysis tool we've done proof of concept with we're in active commercial discussions with customers right now again, continue to improve the product, but the kind of base foundation of being able to categorize complaints and do that work is working. Okay. Well, let's we missed most you want people to know? 

 

Kevin Carney  53:57 

Or did we hit it off? Do you think you've covered it? Yeah. You're just 

 

Chris Hart  54:02 

very thorough. The complaint quiz was definitely that was that was fun. Yeah. 

 

Alexandria Villarreal O'Rourke  54:08 

I guess the one thing I would say is one thing that's become very clear throughout the projects we've done is that we have a lot of like, deep intellectual prowess and thinking, like folks who are really smart, could have really good ideas. And it's, so what I what I love, and what I hope people will take away from this is that if you are at a bank, and you think AI is really cool, and you think there's like a really cool way you could use it, I think there are companies like ours, and probably yours, like that can make that happen, right. And so I hope that folks who have an interest in AI, and who think like, oh, you know, maybe this is a cool idea, but like, I don't really know anything about AI. It's not rocket science. So if you think that AI might be a good use for something like you should look into it, because he really might be right. Or clients have been just such a source of ideas, you know, feedback like product development. There have been I always joked that we should pay them because they're just so helpful to the build of the product, but I 

 

Kevin Carney  55:00 

I hope that AI will become kind of like a democratized, you know, tool that everybody can can use. And that's one of the things we focus on is we always say we want, we're not aiming to replace your staff, we're aiming to elevate their work, right. And so make it so that they have time to look at the harder things. So I hope that we can all you know, as an industry work together on making sure that people have that chance, you know, to learn AI and to sort of elevate the quality of their work. Yeah, we find a lot of our clients, the they, they want to get into AI, the first thing that's helped me is that, well, the Executive Suites have told me that, you know, we can't do AI just yet, we're waiting for governance to figure it out. And my response is, you can't think about it, like, maybe you can't go deploy some AI, but you could think about it. And usually when they come in, and we do like a brainstorming session, they have a few ideas, we map out their value chain, we find other areas, we could use AI and it's just the conversation just feeds off each other. And once once you can do this, and you can do that, then you do something else. And by the time you leave get 10 different places, you could use AI, and we'd give us some sort of a rating score of like, well, this one's probably easy to do, but a low ROI, this one is pretty harder to do. But the ROI can be huge. 

 

56:09 

And risks and other other dimensions. But then people come back with a plan. It's not when I do get the green light, they get the plan and execute on so well with your clients in the same way. I hope they you know, I think this is coming all the time. But if you're using one of like the top 15 tech companies, you know, the big kind of Silicon Valley companies, you're using AI like 

 

Alexandria Villarreal O'Rourke  56:31 

exactly, it's kind of like cloud, right? Where a lot of the banks are saying we're not going to use Cloud. And we're like, we don't know how to tell you this, but like you are right, every every one of your vendors, cloud, whether you know it or not. So we always encourage banks, like if you think that you're not using AI, it's only because your employees haven't told you like, you're definitely using it. It's just a matter of the unit how Alright, 

 

Chris Hart  56:50 

I think the the one other thing I would take away is that there's almost always a way to get started, there's a place that you can find that safe. And that will pass muster. As long as you're thoughtful about it. And you know, maybe it's complaint analysis, but maybe it's not like whatever it is, I do think that this is one of those technologies where every day that you're not getting started that you're not figuring out how to use it is a day that one of your competitors is Yep. And it the sooner that you can get started, the better it is there's not You're not going to wake up tomorrow and have it be a better day to get started with. Today's the day right? Even if you could just wrap your head around it, understand it if you're not like actively leveraging your company, but there are many ways to actually leverage the company. Well, Alex, Chris, thanks for joining us. If people want to get to know more about Victoria, where are they going to LinkedIn, Twitter, or x, you can review some conferences. Now like Twitter and X like you've had like it has two names always, you know, always know for us the best place to learn more, you can go to our website, it's vektory.ai VC ta ri.ai. You can also go to LinkedIn, feel free to look up Alex or me on LinkedIn. Connect with us happy to field questions. It doesn't have to be about complaint analysis either. 

 

Kevin Carney  58:04 

Is there a place where you can log complaints about Victori we're really gonna keep that one because, you know long term project for later on. Alright, well, thanks for thanks for joining us, Bill rush Amelia, thank you so much for actually giving me the heads up. Thanks for having the rapid upside. Or thank you for joining us for another episode of Kingston's beyond the build podcast where we showcase interesting innovators from the Charlotte area until the next podcast go build something awesome. Thanks to Bill claw receive as our sound and video engineer. Thank you to Rashmi lever moolah for our marketing support the podcast on LinkedIn and other channels. And thanks to you thanks for joining us for another episode of Kingston's beyond the build podcast where we showcase interesting innovators from the Charlotte area. Until the next podcast go build something awesome 

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