Podcast: 35% of banks using AI, Hapax COO says

Whitney McDonald 11:53:30
Hello, and welcome to the buzz of bank automation news podcast. My name is Whitney McDonald and I’m the editor of bank automation News. Today is May 21 2024. Joining me is Kevin green. He’s the chief operating officer of haptics. He is here to discuss how AI will unlock the availability of knowledge for financial institutions. Thanks for joining us, Kevin. Great.

Kevin Green 11:53:49
Yeah. I’m Kevin green. I’m the Chief Operating Officer of haptics. I’m a 20 year marketing veteran, both at startups and enterprise global organizations. I’m a 10 year veteran around artificial intelligence, specifically artificial intelligence utilization inside enterprise organizations. So I’ve been around the space for a very long time, and really kind of watched it evolve, and excited to share a little bit about haptics and kind of where AI is today.

Whitney McDonald 11:54:19
So haptics is a new company, you guys just recently launched? Maybe tell me a little bit about why now, what was the need behind this innovation?

Kevin Green 11:54:29
Yeah, great question. You know, knowledge is power. It levels the playing field. And that’s really what AI is bringing to to businesses today, is the ability for people to have access to information at a speed with which was traditionally unheard of. So for us, one of the things that we believe in strongly is that community banks and credit unions provide a tremendous amount of value to the communities they serve. But as time has gone on, it’s become increasingly difficult for them to compete as they don’t have the resources or the budgets to compete against larger multinationals. And that’s really what we felt like, you know, our mission has always been to solve is how do you bring parity into the industry so that every credit union and every community bank can compete on the same level as these largest organizations. And haptics is designed specifically, to bring that information to a bank have, you know, 100 million in assets to 20 billion in assets, we believe that everybody should be able to operate with the same speed and efficiency to bring customers basically the level of support that they expect today. And that’s really why we felt now it was a critical time to do it, because the technology has evolved so significantly, but the benefits are just too immense to wait for. So

Whitney McDonald 11:55:51
let’s talk about the technology that’s available today. We can’t have a conversation like this without talking about AI. Right. So let’s talk through the evolution of AI and banking. What are faiz really tapping into with AI? And how is AI? Or how is haptics accomplishing that.

Kevin Green 11:56:10
So banks have actually embraced AI, I think at a speed much faster than traditional, traditionally, we see with within the industry. So a lot of technology, you know, banks are hesitant, they have to go through very long due diligence processes. I think that after we had gone through the pandemic, banks felt like they were a little bit behind the curve. And with AI, they just do not want to risk being behind. So they have really kind of embraced the technology and jumped headlong into thinking about what are the different use cases. And so the early adoption really has been around that kind of customer service chatbot, you know, how do I provide AI to my customers in order to better serve their needs, provide them with immediate insights and information to solve whatever challenges they may be faced with? And that’s the initial traction? How do we reduce call center volume, but everything is really that relationship between the bank and the customer. But the technology is far more superior than that, you know, it actually can impact every functional area of the bank. So what we’re seeing now is as use cases are starting to emerge, banks are realizing that the true value is really what’s happening behind the scenes, how do I better enable my employees? How do I make sure that my employees are efficient and effective as possible? And that’s really what kind of what we’ve focused on. So we’re finding that I think the last that I saw was something that 35% of banks are already utilizing AI in some way, shape or form. And that is expected to double, you know, before the end of 2024. So we’re seeing just an immense amount of interest in it. The technology is accessible now, it’s, you know, does it require significant amounts of resources in order to implement? So because we’ve seen such advancement, and, you know, affordability, it’s creating more opportunity for banks to experiment and then identify the specific use cases that you know, it’ll have the most value for their institution.

Whitney McDonald 11:58:16
Yes, so many things that you just mentioned that we can break into a little bit further. One thing that really stands out is this idea of community banks and credit unions having the same opportunity as as larger financial institutions. And it’s I don’t necessarily want to say an even playing field, but it does help even the playing field because you have access to more affordable technology, you have access to AI. And I think that that’s really important to talk through the affordability, we cover tech spend really closely who’s investing in what and of course, when you follow a major financial institution, the tech spend, obviously outweighs what a community bank or regional bank might be spending. So maybe we can talk about that a little bit further this opportunity that AI brings to smaller institutions to be able to implement technology that may not have been accessible before.

Kevin Green 11:59:12
Yeah, you know, it’s, you know, when you think about just FinTech in general, FinTech was supposed to be the great equalizer, you know, it was supposed to everybody’s gonna be able to be digital. But that really didn’t play out the way I think everybody was hoping it would, you know, a lot of these cores and technology platforms are prohibitively expensive. So FinTech itself hasn’t been able to kind of solve those challenges. But with AI, the cost to implement is significantly lower. Just because there are so many different sources and so many different solutions that you can start to experiment, I think the big issue is that you have to look at the resource costs. So you know, larger institutions can go and build their own custom large language models, they can iterate and they can kind of deploy their army of 500 to 1000 IT resources to develop something internal. But 96% of the banks in this country do not have those resources, they need an off the shelf solution that is user ready, friendly, Low risk, low maintenance, and the total cost of ownership needs to be needs to be reasonable. And I think that’s what we’re going to see people gravitate towards is, you know, as you look at kind of the generalized AI solutions that are out there, those are easily to easily, easily accessible. But they’re very difficult to customize or to fine tune to your specific institution, your policies, your procedures, how you want your employees to respond or react, your brand. All of those specific customizations require additional resources to implement and manage. What we’ve done with habit X is remove all of that, you know, our goal was how do we create an AI solution that is unique for every single financial institution, but doesn’t require those overhead costs. And that’s really where it becomes an affordable mentor for every employee that one of our customers described. It’s like having a banking Professor available to you 24 hours a day. And that’s really kind of what we’re focused on. So the cost is going to come down. But there’s, you know, obviously, with all technology, there’s no custom solutions, build it yourself. But like I said, 96% of banks are, they can’t invest more in resources, they need to look for solutions that are easy to implement, and deliver value instantly. Yeah,

Whitney McDonald 12:01:45
I mean, all you see during the the latest earnings is we need to save time, and we need to save money. So those are two things that are not necessarily that we don’t necessarily have access to right now extra time and extra money. So I know that you’ve talked a little bit, what happens is solving for maybe we can talk through how adoption is going and really how FIS are using the platform. I know that you just mentioned it’s like having having access to it to a bank Professor right there. So how, how really is adoption going? How’s it being used? Maybe you can talk to talk us through some examples.

Kevin Green 12:02:20
Sure. So right now we have over 30 unique financial institutions that are utilizing haptics on a daily basis. Those financial institutions range from assets sizes of 100 million to 20 billion. So it’s a broad scale of users. And they are using it every single day to help with daily tasks. You know, really just to solve those common challenges that come up. More often than not, we’re seeing compliance being the initial driver, just because of the sheer amount of questions that come in from a compliance standpoint. So they are the early adopters. But we are seeing marketers, we’re seeing risk tellers, everybody you can think of inside and inside a bank is absolutely using it to to solve the daily challenges. Some of the use cases that we see that are most common are specifically around policy creation. So I’ll give you a small example. We had one bank that was utilizing haptics at the time that they had examiner’s in their branch. And one of the challenges This are one of the questions the examiner asked was, Do you have a digital banking policy and this is a small rural community bank. And they didn’t they didn’t have a digital banking policy. So she went back to haptics and she said, Hey, can you create a digital banking policy for my bank, in just a matter of seconds, topics created or a digital banking policy, and she printed it out, handed it to the regulator and said, you know, hey, here you go. And he said, Hey, this looks great. You’re just missing these two things. So she left the room again, went back and said, Can you add these two things to my digital banking policy, it added those two things, she walked back to the examiner handed in the new version, he said, perfect, this is great, good to go. So, you know, those types of things. Specifically, when you think about some of these smaller banks that are missing these resources, or, you know, in an instant like that, you would have to say, you know, oh, no, we don’t have that. And then you might get dinged for it, it might take a couple of weeks, you might have to pay a consultant to create it for you, you might have to pay additional lawyers to review it, you know, she was able to solve all of that in less than five minutes. So that’s one use case. Others are really around, we have one user who brings it with him to every single meeting. He says, you know, there are so many different regulatory updates and changes, it’s impossible to keep track of everything that happens. I think last year, there were over 5000 pages of updates, related to one regulation issued in normally smaller banks, it takes them two weeks to read through that document, you know, if lucky, another two weeks to figure out what is the impact on their operations, another two weeks to figure out how they’re going to train their staff. Before you know it, it’s two months, Bank of America rolls out those changes in you know, three days. So that type of speed and being able to ask those questions and and know what those changes are in real time. And that’s the beauty of it is haptics is updated every day. So if there’s a proposed rule change, or an actual, you know, rule goes into effect, you know, haptics knows about it instantly, and you’re able to very quickly react and respond to those changes.

Whitney McDonald 12:05:37
Those examples are awesome, thank you so much for for sharing, and you can already kind of see, and you can already quantify some of those savings and the times and when you put the dates to it, or, or the amount of time that it would take to whatever read a new regulation or create a digital banking policy and being able to have it and adjust it right to kind of fit your I know that you were talking about customization a little bit earlier during our talk, but being able to, you can customize it a little bit to to meet this need, or we don’t really need that here. But we do need this and kind of being able to work with the technology in that way. Maybe we can get into a little bit of the house. So if you’re a financial institution that wanted to leverage this, what technology do you need to have in place? How much time do you need to a lot in order to start tapping into it? Yeah, it’s

Kevin Green 12:06:30
instant. So it’s a web based solution. And anybody can go to as haptics.ai. And we actually offer a free version. We believe strongly that it’s important that people be exposed to this technology and start to utilize it educate themselves. But we also believe that it’s important that they’re using tools that are purpose built for this industry. So haptics is built off of an enormous amount of data. So you know, we partner with C Bank, which is the largest online community for verified banking professionals. And through that, we were able to identify 230,000 conversations between verified bankers. And they represent 96% of all financial institutions in the country. So basically, for the last 13 years, 96% of banks in this country have been sharing their biggest challenges, what they struggle with, you know, all of the issues that they face. And we utilize that information, as well as the 20,000 unique documents they’ve uploaded from proposals to policies, to procedures to risk assessments, all the things that they’ve been sharing for 13 years, in an effort to help them compete. You know, these banks and credit unions don’t necessarily compete against each other. So C bank was designed to help them collaborate. And now we’ve taken all of that information along with all this table stakes data from the FDIC, FCC FinCEN. But we’ve brought that into this customized solution that really is unparalleled in the industry, and would be very difficult for anybody to replicate, you know, and probably more impossible than anything.

Whitney McDonald 12:08:14
So now that you have users live on the platform, how often will it be updated? Or how do you kind of build off of those new users and what questions they’re asked screen. So

Kevin Green 12:08:28
it’s, like I said, it’s updated daily. So it pulls in about 100 Different sources every day. So it is always, always has the latest information. So there’s really no single source that is going to have the latest information for the entire industry. So that’s pretty much how it’s updated. In terms of customization, you know, there are ways where you can, you know, when you add new documents to it, and you ask for edits and adjustments, that information is all taken into account, you know, it continues to make it smarter and refine it based on what trends we’re seeing in inside the free version. So if somebody comes into a free version, and is asking you about a specific document or regulation, what happens can do if somebody else asked that same question, they say, Hey, what what other banks are doing this, so you start to get that peer element inside it as well. So it’ll give you a recommendation on how to solve that problem. But it’ll also tell you what other banks are doing as a point of validation. So it all gets smarter continues to pull in, you know, everything from seed bank, and all of those other resources. And that’s really kind of the entire model on the kind of the open version.

Whitney McDonald 12:09:44
Yeah, and being able to see best practices who else had success with, okay, this was a response that this institution received, and here’s how they implemented it, or whatever it may be, but kind of seeing those in real time those those uses of responses. So that’s exciting. Now, you’re seeing more and more, and it kind of goes back to what you were mentioning earlier, about 35% of a Pfizer already investing in AI that’s likely to double, which is not surprising. I mean, everyone’s talking about AI, we’ve seen the conversations shift, or over the past couple of years, as we’re keeping this at an arm’s length, or we’re waiting for this regulation. But now it’s kind of like you have to hop on the AI train everyone’s doing it, you have to have these capabilities. And this is just kind of one example of what could be at the fingertips of financial institutions that are interested in tapping into AI. I’m going to ask you kind of a forward looking question of what’s next for AI? What are you keeping a close eye on? Whether specific to haptics or even just industry? Industry wide? What are you watching? For the tech?

Kevin Green 12:10:55
Yeah, I mean, it’s obviously evolving so rapidly, you know, even when you think you’re ahead of the curve, more often than not, you’re, you’re behind it. So, so much is changing. For habits, our focus really is understanding what the nuances of this industry, so you know, while other people, you know, certainly are embracing voice and video and things like that, and you know, we have that capability as well, our bigger focus is on security. So how do we create secure AI solutions that will meet the needs of today’s financial institutions, we don’t know yet how you know, regulations are going to impact specifically for banking. So we have gone over above, to really make sure that we’re creating secure environment. So haptics, in the near future will be deployed individually for every single financial institution. And that will allow them to upload all of their own documents information. And essentially, it’s their own custom large language model without having to do any of the work, that’s really going to be the big kind of next evolution of this is, you’ve got to be able to protect that data, you’ve got to be able to operate ethically, that’s really important, I think we’re going to see even more of a movement towards ethical AI. To eliminate kind of hallucinations and things that come from some of these generalized models, banks need to know that the information that they’re getting from Ai they can trust. And the way to do that is by not putting a significant amount of data into the large language model. It’s about putting the right information into the large language model, and allowing them to see the sources of that information. So habits actually will cite the sources it used uses to develop its answers. So if you’re asking about a specific customer, so let’s say you have a customer who comes in and says, you know, hey, you know, my spouse passed away. I’m the beneficiary, but I’m not listed on their account, I need access. Well, the teller may not understand know exactly how to solve that problem. They can ask haptics, haptics will give them the answer on how what they should do in that scenario. But right there, it’ll tell you it’s referencing this regulation. It’s referencing this internal policy and these are the internal procedures. So you it’s validating and citing its work because bankers don’t want magic. You know, it’s not about you know, nobody’s looking for you know, that magically just appeared, now they need to have confidence in the information they’re getting. And that’s really what we’ve seen. So I think you’ll see even more of this specialized MLMs. Specifically on the enterprise side, not just for banking, we’ve already seeing it and legal, we’ll see it in several other industries, as well as specialized custom solutions are going to be more beneficial and impactful on the b2b side than then, you know, the generic versions that are out there today. Yeah, I mean, you know, the only other thing I would say is the challenge, I think, or where we’re at now, if I were to kind of identify the timeline of what we’re, where we are right now is really those use cases. And, you know, the promise of AI is, is obvious. And everyone knows that. And to your point, banks everywhere are saying, How are we going to use this. And the interesting thing for us is that, you know, when we come in, and we kind of share, what happens is capable of it’s a very practical implementation, it’s very easy to see the countless number of use cases, you know, so we’ll go in, and somebody will say, hey, you know, this is essentially replacing my knowledge management solution. This is replacing my policy management solution. This is replacing how I train my tellers. So you know, one of the biggest challenges that banking is faced with, and we don’t see going away anytime soon, is the talent shortage. So, you know, there’s high turnover, it’s very difficult to find resources, specifically, as you get into some of these smaller communities, it can be tough to find the skill sets that you need. And then to train them on all of the things they need to know the complexity that comes with this industry is very difficult. So we’re hearing a lot of people are saying, hey, you know, just being able to give this to new employees will reduce the amount of time I need to train them, reduce the nervousness, or the concern they have in that moment of interacting with a customer, you know, if they know that they have a resource right there that can give them an answer, they don’t have to worry about, you know, having to you know, tell the customer to wait, leave the room, go try to find an internal expert, ask those questions. You know, they can solve things on their own, it’s very empowering. And we hope that that’s going to enable existing employees to work more effectively. But also, as new employees come on, that confidence will kind of help them be more effective, and ideally, hopefully retain talent longer. But if we can eliminate through AI, the inefficiencies inside banking right now, which is, most of the time, all of those conversations rise up. So you know, if there’s a compliance issue, it starts, you know, on the front, Frontline, then it goes up to the director that goes up to the Chief Compliance Officer, and there’s a bottleneck, as your expertise lives in your most experienced employees. And they struggle with the fact that they need to provide they need to support the entire team, but they spend an exorbitant amount of time answering questions. So we’ll go into these conversations with these banks. And they’ll say, you know, right now, I’m the AI for my bank. Because that’s what it is, those questions are going to one individual, and you can hear it in their voice where they say, it’s so hard for me to get back to him, and I feel guilty when it’s a week before I can answer their question. And, you know, we show them this capability, and they say, you know, oh, my god, the things I’ll be able to do, you know, I’ll now be able to do these projects that you know, we haven’t been able to get to, we’ll be able to take on more, we’ll be able to move faster, we’ll be able to invest more in the customer experience. And for most of these banks and credit unions, those customer relationships are everything. But if all of this internal inefficiency is taking them away from interacting with the customer, they start to lose that competitive edge that’s so valuable. With habitats, we’re restoring that competitive edge, and we’re giving them an opportunity to engage with the communities where they are such a critical component. You know, we cannot afford to lose these banks, you know, to you know, and go into a system where we only have 10, you know, 1020 banks. Knowing the community, the role they play in the community is paramount. And that, again, is another reason why the timing was so critical. We can’t allow, you know, a lot of these smaller banks without the expertise to go in select, you know, inefficient solutions that aren’t purpose built in this industry. We needed to get something in their hands quickly before they invested in something that essentially would turn them off of AI.

Whitney McDonald 12:18:07
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