AI Governance and Consumer Protection in Modern Collections | Ep. 4

AI compliance in debt collection is moving from theory to operational reality.

In this episode of Applying AI, Sara Burton (Woggerman) of ARM Compliance Business Solutions breaks down agentic AI in collections, human supervision for AI-powered collections, and why AI governance will define the future of consumer protection in financial services.

We also discuss AI audit trails for financial services, bot-to-bot debt collection interactions, and how organizations can explain AI-driven decisions to regulators before compliance risks become enforcement actions.

Listen now and subscribe for more practical artificial intelligence strategies for receivables professionals.

Applying AI Podcast:
https://receivablesinfo.com/applying-ai/ai-compliance-debt-collection-burton

ARM Compliance Business Solutions:
https://armcbs.com/

Sara Burton (Woggerman) LinkedIn:
https://www.linkedin.com/in/sara-burton-crcp-173b042b/

EXL Website:
https://www.exlservice.com/

Mike Walsh LinkedIn:
https://www.linkedin.com/in/mike-walsh-b88b271/

AI compliance in debt collection,
agentic AI in collections,
consumer protection and AI governance,
human supervision for AI collections,
AI audit trails for financial services,
bot to bot debt collection interactions,
AI quality assurance for collection agencies,
financial services compliance,
debt collection technology,
receivables management

#AICompliance #SaraBurton #AgenticAIInCollections

Transcript

SPEAKER_02 0:06

Hello, everybody, and welcome to Applying AI, the show where we cut through the hype and get real about using artificial intelligence in regulated industries. Season one of Applying AI is sponsored by EXL. Let's dive in. So today I've got two great guests with me. I've got Sarah Wagerman and my co-host, Mr. Mike Walsh, joining us from EXL. And we talk a lot about artificial intelligence and the practical application of it to debt collection organizations. And I thought as we kicked off today's discussion, I wanted to bring something up that had come up in a call recently, which was organizations that are using Agenic agents for outbound calls may not be using the same compliance listening tools that they would be using for their traditional collection methods. And I thought that was interesting whether they were viewing the AI agents as a judge LLM opportunity, so the models judging the models versus running it through those standard channels. So I thought I would kick this off talking a little about how we're looking at AI compliance. But Sarah, is this something that you've ever seen in the wild?

SPEAKER_00 1:22

Well, I mean, I've definitely seen that. What's interesting is that you have to be able to model those AI tools that are, let's say, judging or reviewing the calls, they need to, they need to sort of understand what they're listening for, right? So are they, let's say you have speech analytics or you've, you know, a traditional tool that we've been using for a long period of time, or you know, human listeners, there's very specific things you're listening for, right? And that is not always a black and white thing. That's sometimes subjective, right? Given um the you know, the nature of the call, how the how you know what unfolds during the call. So what makes me nervous about AI tools sort of judging other AI tools is I think you could do that for certain elements of a call, like your call opening, maybe even your call closing, maybe you're taking a payment. But everything that happens in the middle, I don't know that it's ready to judge it, right? So I'm I'm not sure why we wouldn't maybe test both of those tools because those those LLMs need to be as good as a human listener, ultimately, right? Um, Mike, I'm curious what your thoughts are on this from your perspective.

SPEAKER_01 2:43

Yeah, so I'll I'll kind of go into our process for this, right? Like so compliance is built into our tool, but we still so when you deploy AI voice, especially, um you're going to get the recording, the transcript, any a lot of the exceptions, especially early on, as you build more journeys. So if you look at this as the first thing I'm gonna deploy and test isn't gonna be broken promise, right? Because you can't get to that to get to the promise, right? Like so I I always tell people that it's like baby steps from uh what about Bob onto the bus, baby steps onto the boat. Um, and then so you're gonna and before you deploy, you should test this a thousand times. Like we we usually do a lot of testing prior with fake calls. So you're gonna do it now. The calls recorded, that's available to you. The transcript is available to you. We do have guardrails like guardrails monitoring and not it's almost restricting bad speech for so that's all there, but you're right, Sarah. Like, so how do how do I, as a someone using our tool, for example, audit it? You still can audit it. It still can be on that QA call sheet to listen to. In fact, all those recordings would be lined up for Mike Walsh, and like you just pull them up and go. So you can do it manually. Um but I don't know if I would get a different tool because there's really a genic AI is not one LLM watching. You're like it's multiple products, not just not just the LLM you're using to understand the speech and the there are all these different things working there. So there's we have an orchestrator. If you like if you look at all those different parts you just mentioned, Sarah, the orchestrator controls them and says, wait a minute, they just ask for their statement balance, not in the middle of negotiation, pull out of that and go to that, because that's the most important thing based on the call. So you can definitely audit them. I don't know if we're I don't think we're using a separate tool to audit them. I mean, I guess you could.

SPEAKER_00 5:06

Well, here, let me give you this scenario, Mike, um, that might kind of just help us sort of paint the picture for our audience here is um one of the things I talk about a lot when I'm talking about AI is kind of like what the myths are versus reality. And there was a lot of compliance concern and just fear around hallucinations, right? And I have I've said to a lot of people, I have not experienced hallucinations once with any of the clients that I'm working with with AI. Um, and I have not heard a single horror story about hallucinations in a conversation with a consumer. I just haven't that hasn't happened to my knowledge, right? And it's because of what you just talked about, it's multiple, right? It's multiple bots doing their job and then moving to the next phase of the call. Um but what I have seen, which isn't it going off the rails or hallucinating, is it's it learns slang from the consumer base, right? And so if a bot is is auditing the bot, it's going to just learn the same words and maybe not say, hey, we don't want it to say bro to our consumer base, right? Like we don't want that word to be broken, or we don't want it to say amaze balls, right? Like we don't we don't want it to say those things. Um and so that's where the oversight piece still needs to sort of be independent from the whole world of AI, right? Is I'm not worried um or my fear of it going completely off the rails and doing something crazy um risky. It's kind of like those little nuances that it can learn um because it is generative, right?

SPEAKER_01 6:57

So and there's there's a solve for that, right? Like so we call those utterances that it will understand, but it will never respond with, right? Like even if you think of text, it's the easiest example is I hit S, but I or STP and no O. Like we would give that to a client and say, We think this means stop. Is this stop for you? And they say yes, that's stop, right? Like, or uh, you know, because people don't talk like even RAI, we make it sound more human by putting ums in there and things like that. Some slang, it's not proper English, but if it gets too robotic, no one's gonna talk to it. So we don't let it utilize slang. So there is a place for that. And there is a model that is, you know, um, it's like a sentiment model almost where it's it's moving the pitch of the speech, it's going, it's like I can't work because my you know, someone in my family died, right? Like it's not gonna say, Great, send your payment in two months ago, like it's gotta match. So it's getting very, very complex. But it's interesting talking about these little details, and then you know, the devil's in the details, right? Like, so it's a great thing to test, right? You should I love when people like at Arm AI, someone grabbed my phone testing my my AI and they said, I gotta get my wallet, hold on a second, and wait it. And it said, take your time, right? Like a human would. But he said that's broken like three others. Those little details how humans really speech, like do it, do want a call outside, do a call uh in a car, do a call where people talk with kids in the background, right? Like dogs, you know. Um, and then you'll see, you see what you're you're getting. But but it is interesting, it's an interesting concept, but you should always have some human oversight. Never let never trust machines to govern machines total, not yet.

SPEAKER_02 9:06

We've spent millions of dollars and thousands of hours developing our call analytics tools for the live person. I don't understand why we wouldn't use that too. I think there's value in using some of these LLMs to watch LLMs and to start to refine that. But to Sarah's original point, I don't think we're there yet to where we have that level of trust in these tools. But if we're spent if we've spent all of this money and time and energy to be able to filter up those specific calls or instances within a call that need human ears and human evaluation, why not continue to use those same things and develop that comfort level? I think from a regulatory standpoint, it also demonstrates how we're treating these tools. Because as Mike and I have said a few times now in our conversations, we're all responsible for the output of the AI. And we're just like we're responsible for the output of the people that work for us. And if we're able to start looking at it that way and leveraging those tools in new ways, I think that's where we're gonna find uh our stride.

SPEAKER_00 10:16

No, I completely agree with everything you just said there, Adam. Um, and again, you know, it's important to think there's going there has to be checkpoints throughout all this automatic decisioning, right? So compliance doesn't go away just because we built something and we said it should never happen, but exceptions happen, right? So when I think about what Mike's talking about, there's your pro your your proactive and your reactive controls. Those still exist in the world of AI. Not it's not all 100% proactive, perfect. We would love to think it is. Um, but things still, there's still exceptions to everything that we do. And so I I look at the the LLMs that are testing the LLMs as another tool in your toolkit to get to your potential issues faster, right? This should make us more compliant, more operationally sound. We should be able to turn over things quicker. Like we should be able to be maximum efficiency because we're finding anything that a potential breakdown faster, which we haven't we've never been able to get to these things fast. We've been needle in a haste at finding these things. Um, and so that is nothing but positive, in my opinion, for um the industry from a regulatory testing standpoint. I think it's those are all positive things. And that's I think we need to make sure that we're thinking about all these things. How do they all work together for you to find those things quicker?

SPEAKER_02 11:51

Have you seen, have either of you seen clients coming to the table with measurable, let's say, compliance KPI expectations, or is it still an evolving art form as we start to deploy this type of technology?

SPEAKER_01 12:06

I I will tell you this, it's changing fast, right? As Agenic's taken off, um you see the questions now around like latency, uh disconnections, uh hallucination percentage, things like that are gonna be key KPIs, and there's more. There's there's a ton more that you can imagine, like just think of calling the airline. Everybody's called the airline. Like, think of those frustrated, like the death loop, like how long, and those are like the old kind. The new that you would deploy to collect money in in this type of environment, they have to be spot on, right? Like they have to be primo, so everything matters. Uh, speed, under the ability to understand, the misunderstandings, the repetitions, that's all getting measured. And when you think about deploying these solutions, think about measuring those, right? Like the think about a cheat sheet of what you've listened to and and hated, what went wrong, and then apply that and make sure your customers, you know, and your clients are getting a tool that's satisfactory. And then when it can't do the job, like because of you know, your you somebody has a utility debt and you ran over their flowers, utility company, and they're not gonna pay until they come back and fix like that's not gonna be an AI call, right? Like, that's the exception it should get rid of. It should recognize it can't handle this call and get that to a person and the right person. Uh, you know, if someone says bankruptcy and you haven't programmed that journey yet, you want to get them to your bankruptcy team, right? Like, so measuring those type of KPIs. So I think KPIs, I mean, you think about as score, it's changing. It's definitely changing. You know, the the warm transfers like now a KPI from from how did that go from AI to human or human to AI? Like if you're gonna take a payment, you don't want your people taking payments anymore over the phone, send it to the AI and make sure it's capturing and it's going quickly and everything goes smoothly. So, yeah, I think Sarah, I don't know what compliance KPIs you're seeing, but I think they all are really like almost all KPIs are compliant.

SPEAKER_02 14:25

I think from a Sarah perspective, like, have you seen any evolution of the scorecard, for example, as we've started to deploy this and as we're listening to those calls in real time?

SPEAKER_00 14:34

I'm I'm surprised I haven't seen the evolution of the scorecard. And I think the reason why is probably because my client base is oftentimes debt buyers and the third-party collection agencies and the AI folks I'm working with, some are just tech um vendors and some do third-party and have a SaaS thing, right? So I I'm seeing it from a little bit different perspective. So I'm actually glad to hear Mike saying at least he's starting to get those KPI questions coming in. Um, I think it's not as widespread being used, or it's being used for pockets of accounts, or you know, very well defined, we're gonna try this low balance portfolio, or we're going to try this because we, you know, we feel confident and the you know, pass-through consent, or they're still not deploying it um at a large enough scale to compare it to other performance, I think, yet, but I think we're close. I think we're getting close to seeing that evolution. Um, I think we when I start seeing it on debt buyer scorecards is where I'll say, okay, we've sort of made that change, right? Because they use oftentimes they they they use large networks, um, and you've got an array of of different stick strategies being deployed within those networks. And I I I look forward to to sort of seeing that evolution because I think um, especially some of the bigger players out there, um, I would suspect that those will start to evolve pretty soon.

SPEAKER_02 16:12

Do you think it'll start to evolve as they deploy that technology internally? So as Depth buyer A or B or C starts to expand in that capability into it.

SPEAKER_00 16:23

Yeah, they're gonna start champion challenging um how people are deploying it, how aggressive they're being, um the the vendors are, you know, is this vendor working better than this vendor? I mean, all that's gonna start coming into play. That's gonna be a really exciting time. Um, and uh I do see that, yes.

SPEAKER_01 16:43

Yeah, absolutely. I agree. It's it I think it is happening on the creditor side. There's no doubt. Um and it's it's gonna, it's a lot of reporting, right? If you think of agency ABC out there, you're gonna need some more fields. Like there are, or you need a vendor who's gonna report all these KPIs back to you, right? Like that is a big part of not you know, not all AI is built the same, right? Like, so make sure think of what your clients are gonna look at, especially with phone calls. Um, you know, email two-way text, it's easy. It's standard, right? Delivery rates, all that. When you get into phone calls and you know, like you're looking at sentiment analysis versus the actual call, like I think that's what people are gonna be listening for. Did it match? Did it uh there's so much information that's gonna be utilized and can be measured. And you know, the tools are learning from them, but it can also be um, you know, in Sarah's uh original example, you can pull the bad stuff or stuff that doesn't make sense out, right? Like you can say, okay, kill this. So it's it it will be used to improve, and then it's gonna be part of your scorecard. Your your AI this is like an arms race to me. Like you're we're all gonna use AI eventually. 90% of agencies are gonna use them, so then you're gonna be measured on what you're using, and that's gonna be a big part of how your score is.

SPEAKER_00 18:23

So I really like what you pointed out there, Mike, about the reporting specifically. So that is um, it kind of pivots into another thought that I think about a lot, and I haven't quite seen um sort of a great sort of I I haven't seen any documentation that has quite satisfied my desire to see the decision chain. So when um so for example, I I recently had a conversation with somebody and said, I love that what I just told you, you went and you you fixed, and then we re-listened to a call and we fixed the problem. I love that. But in five years, or you how do you know that Sarah told you to do this, right? Or that there was a compliance review that was done and that you fixed something as a result of actual oversight, right? How are you documenting that? Because you know, it's great that you can go in and go, that's a that's a technical term, everyone.

SPEAKER_01 19:30

Um, you wouldn't believe it.

SPEAKER_00 19:36

So you know, so you do that, and that's great that it and now it's fixed, but like, how do we explain this to a regulator in five years? And that's those are the things that there's so much decision, and and it's the technology is moving so fast. Um, and you might have some really good um feedback on this, Mike. You might be um working on this, or you might have something already developed, but like, you know, there's a there's a Colorado State lumba that has a lot of reporting requirements, and I'm not exactly sure what those reports are supposed to look like yet, and I don't think they know, which is why they keep pushing out the effective date. But um that is something like that change management, that decision tree, how those things get reviewed, I think is really critical. I think your clients are going to um expect to see that, and I know regulators are gonna expect to see that. So I'd love to hear your thoughts around that.

SPEAKER_02 20:34

Yeah, that's documentation of the learning process, is what I'm hearing, right? How is it learning and how are we documenting the learning process? Go ahead, Mike.

SPEAKER_01 20:42

Yeah, and and how is this account auditable, right? Like, how do you audit? Like, where is the audit trail? And then it it gets slippery, right? Like, because you know, I don't want to show you how my tool works because it's patented and everything else, right? Like, but right, but I know that every client of mine needs to be is going to be audited and needs to show how this decision was made. Part of this is the initial, and Adam, you and I have talked it a thousand times is going to the vendor and saying, Okay, how is this model trained? Who doing how did it learn? How does it prevent bias? How is all this stuff in there, right? Like, so that's step one before you even deploy anything, is make sure you understand that. Is it all yours? Is it fourth party? Are you reselling something else? Like, you have to understand that. And then part of what I I'm doing, I'm going to CBA Live and talking about what questions they ask, and one of them is how do I audit this, right? Like, where's how do I see how you came to this settlement, right? Or this payment plan. And if you can't show that, and I don't know what you're gonna do. Because it's highly regulated. It's got to be built. And I think we saw some like companies come over two years ago at RMAI, want to see the numbers of accounts that are in this business and say, we want a piece of that. But they're marketing companies. They had none of this built in, they had no reporting, no audit trail, and they left. Because there's I had one say, Man, how did you guys pass the data security these banks are out of their mind requiring? I'm like, because we built it for them. Like, this is a collection tool. What are you talking about? How did you not know this?

SPEAKER_02 22:36

So, but it's entry from third-party countries, too. Organizations coming in from other countries not understanding the level of complexity. So, like the first two questions that I always ask of an organization that says they are bringing in an AI voice bot to the marketplace is who's your attorney and who's running compliance? It's the two most important questions that I can ask. And but I want to go back for a second. We were talking about kind of how you're going through that auditing and how you're documenting that decision, but we're going to have to measure this at some point. And I think the this crossover between the idea of the KPIs and how we're going to put some numbers to being able to measure this output and what these scores are going to look like over time. Because the documentation of how we change it, I think is important, but we're going to also have to show a positive trending results line that says, look, we are improving this over time because if we can't or if it plateaus, I think the regulators are going to have something to say about that.

SPEAKER_00 23:39

I agree with you. I think they are very much aligned. And I think that the metrics and the change management decisioning and all of those things, I think they all play really nicely together if we can take that information and then put it in understandable terms for the regulators. That's the other key to it, right? So if we've got a bunch of data scientists saying, well, I don't know why you don't understand all this beautiful stuff here. Um, like Mike and I have to understand this, right? Like, like dummy it down for us. Yeah, because we've got to be able to explain that to a regulator. And because they're not gonna be, they're not, they're they're going to need it in the most simplest form. But um we're we're gonna have to show our work, right? That's that's ultimately what it is.

SPEAKER_02 24:49

And I think you're gonna have to show technical capacity in a traditional format because the regulators aren't gonna understand the technical capacity, they're not hiring data scientists, right? They're used to auditing a live collection agency, and we're gonna have to be able to communicate PhD level complicated things to someone who has a middle school understanding of a concept. And I think that gap is going to be a significant challenge, which is why we focused this episode on that measurement and understanding of what the compliance guardrails are ultimately going to look like as this evolves over the coming years. I can see you have an opinion. I want to hear it.

SPEAKER_01 25:34

Yeah, I would say that, right. I I think you made a great point. It's gotta give the output of traditional collections, right? Like that that has to be there. The the the conversation, the notes, the you know, Spanish translation or French Canadian translation, like that all has to be there. They have to go through the audit. Same thing with like payment processing. That's that's not hard. But uh I do think there's gonna be more information that if they want to dive into, um, and I think that's one of the questions you ask your vendor is you know, what what responsible AI guidelines have you utilized to make this process? Like what you know, Europe's been ahead of us on this. We've talked about that. I think a lot of the state laws are based on the European Union's uh AI laws, which I think make a lot of sense, right? Like EXL follows them, we follow them globally, not just over there, we follow them everywhere, every market because they're logical, right? Like, so um I think it's it you're right, you're whoever you're using has to send you everything you need for traditional audits plus more, and that's where like I always say be careful what your AI vendor gives you. They ask for. They're asking for age, get a good attorney, right? Like, call Adam and find a good attorney, or call Sarah and find a good attorney. Because right now, you are dead, like that is biased, and that is ridiculously dangerous. Like, so if you hear something in that initial talk and they're asking for tons of things, 30,000 hours of tapes for a relationship they have nothing to do with, regulator might say, How is this train? And if it's trained on your information, you know, they're not creditor ABC. Why did they listen to these private phone calls? Explain that. I don't ask like I think you have to think of like as if I'm handing these to a human being and they're gonna listen to them. Is this just gonna get me in trouble? So there's a lot you're right, it's you gotta do the traditional, and then you gotta be ready for what they're gonna ask for bias and things like that, where these models you gotta responsible AI, I would look read look into that, and then model development and training. How's that happen?

SPEAKER_02 28:07

And what's it being developed and trained on? What are we gonna measure? What are these numbers gonna look like? Because you know, hallucination percentage, as Sarah mentioned, she hasn't come across one yet. And I think hallucination became a major topic with ChatGPT 4.0, and I think it became a major topic because you had attorneys that were submitting documentation that had zero research to it, and it was citing cases that didn't exist and those kinds of things. So it was like irresponsible use of the tool set, I think is what prompted a lot of that fear set. But I don't think that that's the the big challenge that we have now. Because Mike, as you've talked about stacking those LLMs and looking at it from that perspective and everything, you know, it's it uh it reminds me of the movie Casino, right? Like everybody's watching everybody, and the eye in the sky is watching us all. And it feels like that's the way that we're gonna go, but we are gonna have to slim some of these things down in a measurable way to be able to communicate it to someone who has zero technical expertise. Because they are not sending coders out to conduct these audits that where you're gonna be able to say talk uh above or around them. You're going to have to bring it to their level for their understanding and their comfort level, or we're gonna pay the consequences as an industry and as individual organizations.

SPEAKER_00 29:25

I mean 100.

SPEAKER_01 29:27

I think it's easier than you think, right? Like, because you know me, Adam, I'm the most technical guy on earth, right? Like working at an AI company. Um, like I'm the worst. But I look at our reporting and I'm on client calls and we go through the measurables that is on, like, it's uh it's the it's uh AWS quick site, usually, but it could be a Power BI tool or tableau, right? Like you have everything you could possibly dream of measuring right there by segment, by date. You can go on account level, you can go on a portfolio level. The great thing about AI is the best thing about it is it's great at measuring insane amounts of data, even unstructured data. We structure and say, why did people ask for assistance? Right, like the reason they put, they said, right? Like that's all measured now. Like you can have a category of how many people lost their job, right? I think I think that's gonna help regulators, not like I think it's gonna be more convenient to audit. Like, uh that's how I think of it. Where you where if you think of pulling some of that data off an old collection system, it was a pain in the neck, right? Like you had to prep for an audit. And now it's like, here's the tool, what do you want to see? Right?

SPEAKER_02 30:48

Like, as you provide more information, are you creating more risk?

SPEAKER_00 30:51

I was just you knew were you reading my mind?

SPEAKER_02 30:54

All right, so I think we just think the same way here.

SPEAKER_00 30:58

Please, I want to hear your what you wish for, because we know regulators love to say things like known or should have known, right? So that phrase gets thrown around in consent orders regularly. It's also in many laws, known or should have known. And so when we what um we need to, as an industry, when people like Mike are giving us these great tools and feeding us all this information, we must do something with that information. To get that information and do nothing with it creates risk, right? Um, and that is where you're going to end up getting your hand slapped. So sometimes it's best to not have the information in certain instances unless you have the resources to make informed decisions based on that. So if you're learning from based on the metrics you're getting from your AI tool, that one of your policies really upsets people, right, and creates complaints, and you do nothing about it, you don't evaluate that and maybe adjust it or do something different, then you were going to create risk within your organization. There's no doubt about it.

SPEAKER_01 32:22

You're absolutely right.

SPEAKER_02 32:23

I just think back to the original CFPB. At the very beginning of the CFPB, they were coming out and they were fining for nothing. And I think the more information that they have, the more likely they are to do that. Because let's be perfectly frank, the the political pendulum swings. And right now it may be pushed really far in one direction, but it's gonna come back again. CFPB's not dead, they're not gone, and they're gonna hire again. And when they hire again, they're gonna hire the same sycophants that they had at the beginning, and we're gonna come into these same challenges. So if we start providing this mountain of new data, they're gonna find and invent new ways to create challenges and problems where they maybe don't necessarily exist. But that's not gonna stop the risk level, it's not gonna stop the fines, and like usual, it starts with the creditors and works its way backwards.

SPEAKER_00 33:17

Correct. And logic doesn't necessarily rule the day in these, right? So um and I say that maybe No, no, no, no. But for those of you who are new to the United States of America and and how this works in a regulatory concept, like that's that's one of the things when I'm talking to people who have launched these tools overseas and now are coming into the US market, is that they have this um sort of notion that they can logically explain this and it'll all make sense. And it's like, no, that's not how our regulators respond. Um that's not how a court case gets decided in the US. Like, I wish it was that simple, folks, because logic does not rule these often, right? And um what they deem as consumer harm might not be harmful at all. It's just their sort of opinion, right? Um, in some of these cases. Now, um, and yes, the more information they have, then they can the more subjective they can be with what they extract from that to sort of create that narrative. And so we need to always be thinking about how could this be twisted the other way, which is what I do all day, is like, all right, how do we make sure that um the ex this is improving the experience for the consumer and that we can show it's a it's improving the experience for the consumer, that we're being more compliant, um, and that this isn't creating undue harm. And I think, I really do believe, I don't just think, I actually believe that AI will help us accomplish those things. Um, it's just going to be about how you evidence it and making sure that you are the information you you are getting back from the AI tools, that you are disseminating that information so that into your compliance management system and doing what a compliance management system is intended to do, which is identify risks and fix and remediate them, right? Um, change policies as needed, change your practices, and then document that so that you can show Mr. Regulator, Mrs. Regulator, look at what we're we've done as a result of this, right? So it's a positive experience.

SPEAKER_02 35:49

You just made me think about what it really is. No, I uh you made me think about this a little bit. Can you explain this to a jury of your peers? And I know that sounds like a kind of a simple concept, but I don't know when the last time either of you have actually gone and done jury duty, but it is a shakingly scary thing when you realize what a jury of your peers actually means and what level at which this needs to be explained in order to meet that standard.

SPEAKER_00 36:18

Yeah. No, I think that's important to remember. I love that, Adam.

SPEAKER_01 36:23

And I think compliance has to be part of the decision making for these tools, right? Like when you're buying a tool or building a tool, you have to like, I think there's a couple things people, especially people who build have run into that come back to us and say, updating this thing with all the laws is a nightmare, right? There's 21 AI laws on the books right now. Sarah, you're on the committee with my colleague. And I was like, what? And uh 21 different ones. And you know, you gotta be able to go in and change it and change it by location of the consumer. Um then just I think the that some creditors like I always think of the fair fair lending acts, right? Like that is a big part of I think the European AI law is kind of based on some of those and risk modeling. Like if if if you've been doing this a long time, like we started 2018 with this product, so we've kind of seen a little swing in different directions. And I was at the CBA when uh the CFPB declared war. Hey, we're gonna sue most of you, and or some of you are suing us and we're gonna defend ourselves. Like, I was like, whoa, this is crazy. But yeah, it so you have to build a product that's gonna withstand regulatory compliance, and you have to part of the like I think part of the problem that I've heard from other people is they run into a tool that they just can't get what they need to show. Like being able, I think that's a great way to say it, Adam, is to go into court and show why it did it and how it protected this consumer. The beauty of AI.

SPEAKER_02 38:08

What happened, what happened when I mean and I remember the Chopra incident that you're referring to at the Consumers Bankers or the Consumer Bankers Association and what that actually sounded like. I mean, look, it was a scary day, I think, for all of financial services and for the consumers themselves because of the impact that some of those statements have. But on the tail end of that regulator, if you can't if the regulator refuses to understand, not can't understand, but refuses to understand, in the end, it ends up with a jury of your peers. And can you explain this to a jury of your peers? This is where you have to start thinking about the average leading, the average reading level of an American, the average understanding level of an American, and make sure that you can explain these decisions and this tool set to that population.

SPEAKER_01 38:56

It's unfortunately the reality of it. And those rules will change, right? Like it has to be flexible enough to change with the times. It has to be.

SPEAKER_02 39:04

And can you unwind it once it learns? So if we're training something and the rules start to change, what capabilities do we have to be able to work backwards and to get these tool sets to be able to comply with it? Now, sir, I got an interesting question for you because I know you work with both tools, you work with the depth buyers that are deploying tools, and you work with AI-first agencies or AI-first organizations. Do you see a difference in their outlook of compliance between those three segments of the market? The those that are building the tools, those that are deploying the tools, and then those groups that are actively building themselves as an AI-first organization. Is there any difference in their compliance posture?

SPEAKER_00 39:53

Yeah, I do, only because, you know, if you're a large debt buyer, uh consent is going to be something that you really have to evaluate from a risk standpoint, right? Because you've got um there's some murkiness in our laws. So a deregulatory environment creates a whole bunch of gray areas sometimes. And that has happened with the TCPA, and um whether or not consent is passed through, and then and then you've got the question of how reliable is that consent. So, you know, for outbound, this is a tricky, this is tricky business for the debt buyer space. Um, so until we get some clarity around that, that's going to be, you know, they've got different reservations. Um whereas a direct creditor relationship, there's not those kind of relations, you know, that those kind of reservations because they know what their their source of their data is coming from. And um, so when it comes to sort of how they view sort of at least the TCPA is the first big consideration. The other thing I would say is um between those groups is the understanding of the FDCPA and the interpretation in the least sophisticated consumer, you know, for people who are not from our industry traditionally, they don't fully appreciate oftentimes how that gets twisted in a lawsuit, right? Um, and understanding what the least sophisticated consumer really means in in front of a judge. And, you know, if you're deploying the technology as some as a company like a debt buyer or a third-party agency and all the traditional debt collector folks are still there, they're going to build in and have oversight over those things and be looking for those things. If you don't have those types of folks integrated into your business, you're probably going to get tripped up. And that's going to be, that's going to be a problem, right? And so, you know, all the all these different groups. Um, I I love when I see the tech people and the industry people really coming together and solving these problems together and being really collaborative. I think that's the way this has to be deployed because we have really um we have PTSD for really good reasons related to taking some of these risks, right? We have experienced the class actions, the consent orders, we've seen, we've seen our peers um, you know, make the headlines, and we don't want to, we don't want to relive those days, and we don't want to take those kind of risks um necessarily, right? We might be we might be okay with taking certain risks, but not jumping both feet in and just kind of throwing caution to the wind. Um I I think most industry people don't have at we don't have interest in doing that. Um we've had a lot of starving lawyers um when it comes to TCPA suits uh since the Facebook case, and they're they're getting new life in them because of artificial voice. So just keep that in mind.

SPEAKER_02 43:17

There's always a quote unquote consumer attorney looking for an ambulance to chase.

SPEAKER_00 43:23

Maybe need to do shoes. I mean, come on. Like they they they are hungry.

SPEAKER_02 43:30

No, look, and I I evaluate their websites and we can already see the traps that they're setting up in search engine optimization and even within the LLMs to try and draw consumers in. I was having a conversation recently with um with John Bedard and some of the folks on the defense panel for ACA talking about some of the challenges that they're starting to see in terms of how the pro se litigants even leveraging these LLMs and how is this gonna start to right? How how are we gonna handle this from the other side? But I I Kind of one last question for the group here, and I I'm curious to get your take on this. You know, one of the things that's kind of come up over the past couple of months is the idea that we're going to have the uh the bot wars. And what I mean by that is, you know, we've got a bot on the collection side, the consumers got a bot, the debt settlement companies got a bot calling in. You know, what happens as these bots start arguing with bots? And you know, what are we gonna start to see as the consumer attorneys roll out bots that are built for the purpose of trying to trip up the collection bot? I think it's already happened.

SPEAKER_01 44:38

To be honest, right? It is already happening. That's where the conversation's coming from. And you know, you you this is where I think to what Sarah's point was, industry experience on the building of the tool and the maintenance of the tool really matters, right? Like, um, again, for my talk, the first question is ask them some collection questions, like use some collection jargon. If they don't know what you're talking about, ask why don't they know what you're talking about and who at their company does, right? Like, that is the easiest you want to see if that if it's a collection AI tool, if they don't know the jargon, end the meeting, move on, right? Like, like, because if you didn't build this with industry expertise, there's so many ways to get caught. But I don't I'm not worried about it, to be honest. Like, the great thing about AI, like, we it's a great topic, but most of the compliance people that are my customers are very happy, right? Like, it's the same process. Like, there was something today that came across like, hey, you texted this person at this time, and it's like, yeah, but they texted us one second before, you know, like it's the trail is so together. There are predictability of the behavior, like it doesn't have a bad day, like you know, their team loss at the NCAA tournament, and they're mad, and they're you know, they're just gonna start screaming at it. Like, it is pretty when you get it up and running, and it it is you know, adjusted to your business and how you do business, it's pretty like we have we need not run into this. AI calls us, talks to our AI, what do we care? Right? Like, uh, you know, if it's trying to trick it, it's gonna trick up like it's a great question, and I think probably the point of the question, Adam, is what does it do if it it it knows it's trying to get trapped into conversation not to do with its debt, right? Like that's what they're going for. So there has to your vendor has to explain that you know, for us, it's can you repeat that? I didn't understand. It goes again, and like I'm having a tough time, let me transfer you my supervisor, even through text, same thing. Okay, I'm not understanding you, let me get you part of the protection is locking it down, not adding more, right?

SPEAKER_02 47:08

Like if you restrict the threat identification and move it to a live person because the bots are gonna be used against live people too.

SPEAKER_01 47:14

If you if it's Eskin who won the Super Bowl and where can I get good, you know, a better banking rate, it's gonna it's gonna dump them and and move on. And that's get them to a person, or in some cases, like if it's a repeat offender, it's just not gonna take the call. So there's there's the tool has to have that protection in it, is basically what you're saying. But it's a great question to ask, right? Like, what what does it do if it's someone's trying to trick it up or something? Sarah, what do you think about the pending bot wars?

SPEAKER_00 47:50

So I think I think a lot about this actually. Um so I see a feature where um on inbound calls or even outbound calls um where there might be a question that is asked. Are you a live person or is this a bot um coming from us, asking the consumer ultimately? And then do you have a defense if you third party disclose to a bot that isn't technically authorized, right? Right, because then you've done your CYA, right? You've said, well, they said they were like I I see, I've seen this um, like this is in my in my groups, this is something we've talked about, right? Is um do you have consent? Does the TCPA apply for them using a month to a month, right? Um there might be an 04 um opinion that might open that up. Like, I mean, there is like all sorts of this could get really wild, folks. So um I think that for companies, it's important that you know you have a stance on either whether you're going to engage with a consumer bot or not, and because the risk could be a third-party disclosure risk. Um, how that plays out in court, I don't know.

SPEAKER_01 49:14

That's a good point, sir. Like, like that is a good point. And like what was your disclosure? Like, what was your verification question, right? Like, was it up to snuff? Is gonna be um, and to be honest, like most of our clients pick those, right? Like, whether it's year of birth, last four of Soch, like something where is this Mike Walsh? Yes, okay, just to verify, boom, and it's gonna identify on our end it's a virtual agent. And then how are they gonna get through to the next level of that account information, right? Like, there's gotta be a protection layer in there, and if it is, and someone I I I saw one, they're like, Oh, they just use you know your voice. I'm like, you can't do that. I could record my voice, like my voice is already in AI, like I don't have to read all my promos anymore, right? Like, which I am terrible at. So um that's not right there, Sarah. You're right. Like anybody could do a voice recording now. Like, so you have to start thinking, there has to be a question.

SPEAKER_00 50:15

There has to be a question that and basically a disclaimer that says, okay, verify this you, and here's the question, they answer the question, and then if it's me, Mike Walsh is a bot called if it is a bot and they say it's the live person, like, okay, well then what are you supposed to?

SPEAKER_01 50:35

I mean, yeah, what is your collector supposed to do, right? Yeah, you know, what is your what is anybody supposed to do? So I think that point is either there's a fraud going on, which we try to detect those frauds, right? Like, or which usually the questions identify it as possible fraud and we report that back, or the person did it on purpose and wants, you know, for convenience, the bot to handle. Like when I when I saw Heath talk about our friend Heath talk about those bot conversations, the average bot inbound to an agent is 10 minutes of phone time. And there's usually never a payment because they're just negotiating the payment terms, and then they call back and make the payment or go on your website and make the payment because they don't want to talk to you. So, you know, if it's bot to bot, 10 minutes doesn't cost you much, right? If it's bot to human, man, you just spent a lot of money collecting nothing, right? Like you ha gotta hope your your website's easy to use, right? Like so. Um, but it's it's very interesting too. Like the scam bots are gonna come. They're right here too. Like I just talked to one today, it was fun. But um it's something to think about for sure.

SPEAKER_02 51:46

This is where it's gonna start getting real interesting, and I feel like we're gonna have a whole follow-up conversation at some point in 2026 as these bots start to roll out. Because being able to create a bot of yourself and and use it for these purposes, I think is definitely what we're gonna see. And I think it's gonna be an attack vector of these quote unquote consumer attorneys to try and trip some, to try and trip up a collector or to trip up another bot in order for them to be able to file these frivolous suits. But that's probably another conversation for another day. I want to thank both of you for coming on today, having this conversation. This is quickly becoming my favorite series talking about artificial intelligence and the practical applications that we've talked about today of being able to roll these things out and getting past the theoretical discussions, I think is really going to have an impact on our industry. This is fun for coming. Yeah. Thank you everybody for listening to Applying AI, where we explore how to make artificial intelligence work in the real world of regulated industries. Subscribe to the show on your favorite podcast platform or YouTube and find more insights at receivablesinfo.com.

SPEAKER_03 52:57

We'll see you all next time.