Adam Parks (00:08)
Hello everybody, Adam Parks here with another episode of Receivables Podcast. Today I'm here with an exciting guest that many of you have met at conferences over the last couple of years. I've got Anna Burke with Equabli joining me today to talk about artificial intelligence and the use cases of artificial intelligence specific to the debt collection industry. So Anna, thank you so much for joining me today. I appreciate you coming on and sharing your insights.
Anna Burke (00:34)
Happy to be here. Thanks for having me.
Adam Parks (00:37)
Absolutely. Well, I'm actually really excited to hear your story because I don't know it yet. So for anybody like me who is still getting to know the real Anna Burke, could you tell everyone a little about yourself and how you got to the seat that you're in today?
Anna Burke (00:50)
Sure. So I have been in and around the banking and lending industry for about 15 years now. Spent more or less the first 10 years of my career in commercial banking at Bank of America. So kind of sat on the other side, watched a really big bank try to adopt new technology, get in the corporate space, and then spent some time at a very large payments company ended up leaving big corporate world and going to my first startup, I think about five years ago in the predictive analytics space, again, serving the lending industry and kind of fell in love with that. The excitement of a startup and also like that level of intelligence and insight that you can bring to a business and then found equably just about two years ago. And so came here to help transition from kind of founder led sales to a company as Cody and I like to joke, but into a real sales engine and kind of built out our go-to-market strategy and team here.
Adam Parks (01:56)
Fantastic, so could you tell everyone a little about Equabli and what it is that you do there?
Anna Burke (02:02)
Yes. So Equabli is kind of a tech first sort of modern platform or suite of solutions serving late stage credit as a whole. So we work with lenders, debt buyers and agencies and try to bring next generation solutions in like a very digestible, manageable way to them. So I think what we're most known for and kind of what inspired this discussion is Our work in predictive analytics and machine learning, custom modeling space is really our core competency. It's like the thread that ties all of our solutions together. And then we also, again, we were born from a former debt buyer. And so we have a lot of perspective on the industry and what it needed. So we've also built some really kind of innovative technology solutions to address the fragmentation that I think everyone deals with when trying to like piece together a recovery value chain. So connecting lenders to debt buyers, to agencies, to, you know, other vendor partners in like a very holistic, streamlined way. And then also we've entered into the digital collection space as well with a platform that we sort of call our digital agency in a box. So kind of empowering lenders and debt buyers to go off and capture that self-service opportunity on their own.
Adam Parks (03:24)
So you're giving them agency in a box from a digital perspective, which I think is something the industry desperately needs. And I've been watching closely through the years, because Cody and I have been quite close, going back 15 plus years. And so it's been really interesting to see how the organization has evolved and how you've approached the marketplace. Because it looks like to me, over the last year or two, as I've really started digging into artificial intelligence, in trying to really understand the different use cases that are applicable to the debt collection space. Every time that we identified one, I'd talk to Cody and he'd tell me about the tool that is already in development. we're six months into developing that and then seeing some of those things hit the market and the approach that it has taken, but also the impact that it has had for the end users has been really interesting to watch as an outsider.
So when you and I were at the ACA conference recently, we were talking about artificial intelligence use cases, and I really enjoyed the discussion. I felt like I learned a lot from you in that. So what I wanted to do here today was kind of recap those six use cases. Let's call the six main use cases for artificial intelligence for debt collection organizations and see how we can start to break down some of this in a more finite way. So. When I look at the debt collection industry, I think about scoring and treatment as one of those major artificial intelligence use cases. And that's not to say that accounting and HR and other parts of the business are not going to benefit from artificial intelligence, but I wanted to concentrate today on what is specifically within the debt collection industry. So we talk about scoring and treatment.
We talk about data sourcing and appending. We talk about chat and written communication, voice communication, quality and compliance, and then negotiation and self-service options. So we've got a lot of different things that we can ultimately pull together here. So when we think about scoring and treatment, for example, how do you think artificial intelligence can benefit a debt collection organization from a scoring and treatment standpoint?
Anna Burke (05:34)
Well, that that's certainly our specialty, but I love your list of applications because I think, you know, people have lots of feelings about AI right now. And I think we're all sort of bombarded with it, but it is undeniably good for our industry. It's moving us forward. And so it's just a matter of kind of prioritizing across those six use cases. Like what's really the most impactful, what's in line with like the future of the industry and, and borrower preferences and all of those things. And so.
The scoring and treatment piece of it is certainly what's in our wheelhouse. And I think I mentioned our core competency is there. And I think the kind of existing usage or underlying tech there is old school AI, it's machine learning, which has been around for a long time. And so we rely heavily on that to build, we're in the custom scoring business and so taking all of those historical insights and synthesizing them into predictive models and then building strategy around that. I think this next generation of AI helps us and helps everyone just, again, capture so much more data and embed so much more data into those models. It makes them smarter. It makes them kind of you know, more adaptive, move more quickly, adjust to like the changing kind of ecosystem or preferences more quickly. But it also kind of creates this layer over top where it makes, I think, operationalization a lot easier, where we're adding in, layering in like tools and engines to help like really provide very specific recommendations where existing modeling, it's like, How do you build a segment and align treatment strategies on a segment level? I think this lets us go down to, you know, we sometimes refer to it internally as campaign of one specificity. So next best action on the individual account level versus having to treat things more broadly.
I would think that based on pattern identification within a large enough data set, the predictability of those things and being able to say that this account should see this treatment and from a scoring perspective, being able to prioritize which accounts are most important to work through which channels because there are always capacity limitations on any given channel communication process, whatever. And so I think that there's some interesting approaches to that scoring methodology. But as you mentioned, AI ML or the machine learning portion of it has been around for a long period of time. But I would think the larger the data set that's available to you, the easier it will ultimately be for you to select the correct next best action or the highest probability at least.
Anna Burke (08:29)
I think this is like such a, and again, I'm certainly biased and have the most sort of perspective on this piece of it, but like this is such an important piece where like the marriage of technology and kind of that domain expertise comes together. And, you know, I think this applies across the industry. We have a lot of great operators who really know their business, but they don't have the brain of a computer. Like, you know, the human mind just can only.
Adam Parks (08:34)
Thank you.
Anna Burke (08:53)
Calculate so much and so much of strategy today is derived from some combination of this is how we've always done things or these are the trends, you know, we're they're doing analytics but more empirically I guess and so this is sort of like making it much more accessible to pull together like all the minutia of the data but then using that expertise and experience to say, okay, this there's a lot of noise here. What's actually important? Where does the signal lie?
Anna Burke (09:20)
in this now ever-growing pile of data that we have access to. And I also think that there's a part of this that helps tie together all of these disparate tech stacks and levels of sophistication. Like it helps to level that playing field and make it again easier for operators, I'll just say lenders, defiers, agencies, whoever to kind of turn these into actionable solutions. So everyone's working with some like different level of sophistication around technology. And so how do you take this information and actually do something with it?
Well, historically, we've reported the past and it's been difficult for us to predict the future. And I think the pattern identification of machine learning puts us in a new and interesting position to start predicting what will have what's the next action or the next behavior. And that's where I behavioral psychology really starts to play a direct role in the debt collection industry over the next couple of years. And You'll see plenty of that content coming from me in 2026 as I go down that rabbit hole personally to try to better understand even the psychology behind discounts. mean, there's a whole, there's trade associations around the psychology of subscription services. And I'm realizing as I've started to dig deeper and deeper down some of the rabbit holes that AI has sent me down that I've realized how much I just don't know.
Anna Burke (10:35)
Yes.
It's fascinating. And like, that's such a great example of the type of data that, you know, we can use AI to capture and, or I should say information and then like data-fy it and turn it into something that can then be fed into a machine learning model. And, you know, just like capturing all these new insights, you know, looking at like just advancement of sentiment and call reporting and like all of that borrower interaction and like vibe data. You know, I think it's like super powerful stuff and it's the key to collections, right? Like how you apply it.
Adam Parks (11:29)
Well, and as we talk about these data sets and being able to understand and review large data sets, it makes me start thinking about our second use case here, which is the data sourcing and appending. So how can we or how are we starting to use artificial intelligence to bring more data into the mix? And one of the examples you gave was talking about disparate systems. And how do we start to connect these disparate systems and structure the data in a way in which a model can even understand it?
And I think one of the interesting things that I've seen in a few projects has been the use of artificial intelligence for the purpose of structuring data for other models. And now being able to look at these collector notes, I think is the best example that I've seen, because it's a different language in every organization that I've ever visited. It's like every company has its own secret coded language as it goes to collector notes. And it's not just companies, could be collector to collector or team to team. So what kind of models and things can we be doing to structure that data in a way in which it's more usable?
Anna Burke (12:33)
I can't get overly technical here, but I think I've more seen the benefit of that. And we're seeing a lot of progress. That's been one of the primary kind of roadblocks to development, I think, of the scoring models where there's... And again, this industry has lagged from an innovation standpoint. I think a lot of people are still working their way up from really you know, old limited legacy systems and like they're playing catch up and you know, there's a spotlight on collections now sort of across the credit industry for better or worse. And so, you know, but there, there, there are challenges there. And so just making it a lot easier to address that initial roadblock of like data hygiene and aggregation opens the doors to pretty much everything else for
Anna Burke (13:22)
an agency or whoever, like you can't do anything with junk data that's housed in 12 different places. so bringing it all together in a manageable way is just an incredible asset.
Adam Parks (13:35)
It's weird to be talking about artificial intelligence and machine learning when some of these systems are still using a green screen. And that's, that's where I think it starts getting interesting. Now I realize that a lot of that technology is being deprecated in the next year or even, you know, shorter time period. And I think that's why we're seeing more organizations than I've ever seen before that are shopping and looking at migrating or already in the process of migrating their system of record to new technology. I think a lot of that
Adam Parks (14:04)
is being driven by organizations need to deploy artificial intelligence and to have the tool set and the open data set to enable that to happen because you've got some of these data systems that won't even allow you to go in and see the tables and to manipulate that data and structure things the way that it needs to be structured in order to use artificial intelligence because they want to sell you their model to sit on top of it. Personally, I believe that your workflows and your data management tools and your data storage need to be two different worlds. The way that we store and treat something versus the way that we're going to manage a workflow of something and to drive intelligence into it ultimately needs to be two different things, but your data storage needs to be ubiquitous on some point. Like you need to be able to connect across these things. Like we talk about these disparate systems.
Anna Burke (14:53)
Yes.
Yeah, it's so true. It just reminded me of a funny story. So we always pride ourselves at, you know, we can connect to anyone. Like we're tech stack agnostic, especially from an analytics perspective, we can plug in anywhere. You don't have to buy SaaS from us to use our models. We had one client and this is like a year, year and a half ago. And so it's funny just having this conversation now where we probably could solve for it in this, you know, modern world from a year and a half ago. But
Anna Burke (15:22)
We had one client we were not able to work with because they had, I'll just say a bad system, but a hundred percent of their effort and treatment history, call notes, everything, every dial, every email was captured in a call note format. I mean, I don't know how they survived an audit frankly, like they had no, it was completely unstructured. There was not an Excel file to be found. There was nothing. And so we did.
Adam Parks (15:41)
completely unstructured.
No reporting. ⁓ god. Well, there's no reporting
then. How do you run an organization with no reporting?
Anna Burke (15:52)
God forbid they listen to this podcast and know who I'm talking about, but they, we couldn't do it. Like we just didn't have a means of parsing that data at the time. And like today we probably would. And that was just a year and a half ago. And it's like a different world from that standpoint. And so it's
Adam Parks (15:58)
Yeah.
Look, we were at ACA a couple of weeks ago and the world is already different because between the time in which we sat down and had that discussion at a dinner and the time of today, ChatGPT 5 has rolled out and things have started to change. And now, you know, I've been to two conferences where voice AI becomes the big conversation, but we're going to get to that in a minute. But before we go to voice, I really want to talk about chat and written communication and how that has started to play an impact because we've started looking at text messaging.
Anna Burke (16:13)
Yeah.
True.
Adam Parks (16:36)
We've started looking at emails where the industry has become more comfortable with those channels after Reg F. But when we start talking about the chat and the written communications, I think we really need to focus, when we talk about digital channels, not just on our ability to send a text message or an email, and we really need to start thinking about the content of the messages that we're outbounding and how can we improve, how can we A-B test that content? How can we improve the content aspects of those written communications?
Anna Burke (17:14)
totally agree. And I love all digital first self-service kind of driven applications of AI. think this is such, to me, mean, obviously custom scoring is the number one in this case, but number two. But really, I think the biggest win for the industry is driving at borrower self-service, like getting more aligned with, we do see a strong shift in borrower preferences, even across demographics for.
Anna Burke (17:40)
you know, a no touch like non-human interaction or interactive type solution. And so this is like such a great application of this stuff is like building better messaging, doing a better job at connecting with borrowers using again, all of the learnings and the intelligence of like what has worked historically and not just building a score, also like figuring out how do you communicate? What's the right offer? When, you know, what's the channel? All of those different aspects of that digital first communication are just like such a perfect application of this in my mind.
Adam Parks (18:20)
And taking that step beyond the channel. The channel is not what we're going to find differentiation between collection agencies in the future. The collection agency of the future will be measured based on the quality of their content and their strategic input of how am I going to take the raw data? How am going to actually turn that into communications? And what results do we see on the tail end of that? But I think that's where we start to move.
Anna Burke (18:28)
Right. And it's that hyper yeah, it's that hyper specific, like, again, like borrower level customization that this can do that, you know, an agency cannot write 40 million different emails, but AI can.
And how do you keep the compliance aspect of that, which we're going to talk about in a few minutes here about quality and compliance. So if we're going to have the AI that's actually generating these, what's that look like? What do our content libraries start to look like over time?
Anna Burke (19:11)
Yep, That's not a question for me. Bye. Yeah.
Adam Parks (19:16)
Yeah, I mean, it's kind of a rhetorical question in terms of the know, what actually happens with our content libraries. But, you know, when we think about the content library, I want to take that one step further, because I feel like we can all wrap our brains around the written in the chat communications and what that content library looks like. But how does that all start to change when we move into voice AI?
The new challenges that we're gonna start to face from a voice AI standpoint because the canned messages, even if you prepare 40,000 messages, from a voice perspective, doesn't resonate the same way in a voice channel as it does in a chat channel. But voice is like the hot button topic right now. I I was approached by no less than 15 different.
Anna Burke (19:43)
Yep.
Yep.
Adam Parks (20:06)
voice AI companies in the last couple of weeks, you know, to talk about their product. What are you seeing from a voice perspective?
Anna Burke (20:14)
We're getting the same requests, kind of we're constantly bombarded with vendors there. I mean, you can see my wheels turning, but there's just like so much to think through in that transition. like, you know, I think the big thing, and there are opportunities within our ecosystem. know we're a vendor, but there are opportunities in our ecosystem that we're actively exploring and, you know, doing some proof of concepts around.
How do we incorporate maybe AI voice components into some of our solutions? And it's just so hyper sensitive. It feels to us like such a risky area. Like you have to get it right. Otherwise you're creating more problems for yourself. And just like you said, like that.
Anna Burke (21:03)
that text to voice or canned responses end up creating more friction, negative experiences, they end up having a negative impact on your performance. But yeah, we're exploring and it's interesting because it's changing so rapidly and so it's evolving so quickly, but it also doesn't feel like it's all the way there yet.
You know, and so that's like us as a company and then just more anecdotally as like a sales team, we're hearing a lot of it in the industry too. Like we've had, you know, candidly, like a number of projects sort of, you know, deprioritized in favor of, we have to implement this, you know, this, this voice agent immediately. This is our top priority. This is going to save us. I don't know however much they believe it's going to, going to save off the bat.
Anna Burke (21:55)
And it's like, it's bit of a shiny object, maybe. Like, I'm very eager to see, especially as these things mature and they test out against some of the digital strategies, like, what's the winner? Is it the voice? Like, it's a big line item. Collectors are a huge line item. But what ultimately drives the performance improvement or the top or bottom line revenue in the organization. Is it that investment or some of these other use cases?
So I can tell you from the TransUnion Debt Collection Industry Survey in 2024 that voice and phone was still the number one channel for collecting payments, especially for collection agencies. So it was still a very strong channel. I question where are we in the life cycle of those voice AI products? And as those that I've talked with that have been successful in deploying this type of technology have not.
Adam Parks (22:53)
reduced their staffing levels, they have reallocated staffing levels. I think about it from a success standpoint, similar to the next use case, which is the quality and compliance. And you look at organizations that have or were, let's say, five years ago, listening to 10 % of the calls that were outbound from their organization, right? Now they're able to review 100 % from a from an on from an AI standpoint, like their bot or whatever their model can review 100 % of the calls and bring the most difficult 10 % to the surface. And I look at voice AI in a similar way. You may find some value there, but it's about getting the live person on the phone with those consumers that need a live person. There's some that are gonna react and respond, but even looking at
Anna Burke (23:43)
without making them angry. Yeah.
Adam Parks (23:50)
location, looking at age demographics, like my wife and I don't communicate the same way. We both have iPhones. We do not communicate in the same way. Like she prefers WhatsApp. I'm in SMS. She's texting voicemails back and forth with her friends. I haven't listened to a voicemail since 1996. So I think there's different folks that are going to react and respond differently. And I would expect, or at least it's been my experience, that The way in which the loan was originated will directly impact how that consumer is comfortable with these different channels. Like your FinTech consumer versus your I went into the dealership and bought a car, you're gonna have two different styles of consumers that ultimately wanna be communicated with different.
Anna Burke (24:34)
Absolutely. And I think you're maybe where you're seeing more success. And again, this is purely my opinion and conjecture here, but like, I think that those are more realistic, maybe applications, at least for where voice AI is today is, you know, it's, it's an augmentation, it's, it's creating some additional bandwidth, but
Anna Burke (25:03)
I in the convert some of the conversations I'm having, it's like this, you know, golden ticket to I'm going to fire, I'm going to eliminate my workforce, I'm going to save millions of dollars in expense. And it's I'm just going to have this, this AI bot and, and it's, you know, at least today, I don't think it's there yet. And I, again, it's like,
Adam Parks (25:11)
Yeah, it's the silver bullet that's gonna solve all their problems.
Anna Burke (25:30)
And I think this leads into your compliance question as well. But, you know, it's, it's a fine line to walk to be very pro AI, but also risk averse without sounding like some old curmudgeon. But there's great opportunity. Exactly. It's not a hundred percent. And then it's again, within those six applications, like, what are you really trying to do? And like, where does it get you?
Adam Parks (25:41)
There's a balance to be struck. Well, the current state of the CFPB, I think, is emboldening people to try these new technologies in ways that maybe they would not have otherwise, but the political pendulum continues to swing. I am a little bit more, I guess I'm a little more hesitant to attack voice AI in general. I'm more comfortable personally with the scoring methodologies, managing my workflows.
Anna Burke (26:05)
little bit.
Adam Parks (26:19)
dealing with my online communications. It's the real timeness, I think, of the voice that still gives me some hesitation. Like I can, I realize that we can use judge models on voice. I can use judge models on text too, but I can run a judge model before the communication has gone out to the consumer on a written standpoint. And that's not to say that we won't get there from a voice perspective or, you know, maybe somebody does have the silver bullet out there, but I'm very.
That's not the way I live my life. I'm way more focused on the short realities of where we stand today. But even talking with some of the voice companies, you know, I've had people tell me that, you know, look, six months ago, we were not ready for this. So if we're just into that six month window of this technology starting to be in a usable format for the industry, I'm not usually a front mover like that. I don't want to be the first one to litigate my way through that in the courts and set that precedence.
So I want to kind of see some of these things unfold and as the quality control and compliance aspect or that use case of artificial intelligence starts to improve at the same rate that the voice is improving, maybe my confidence level will be higher next week or three weeks from now or a month from now or whatever. But I definitely share that hesitation on some level of diving straight into voice.
Anna Burke (27:29)
Yep. Yeah. And, and, know, and I think this compliance use case is like, what a great way to step into it. And, you know, it's like, it's like anything, you know, it doesn't, again, nothing's a hundred percent out of the gate. You have, you, you've sort of maintained those more human based or, or, you know, the legacy compliance management systems and tools in place while you test into these different technologies. But.
Anna Burke (28:06)
Yeah, I mean, it definitely broadens, you know, obviously, again, you see agency example here, but it, you know, it broadens their ability to sort of manage a lot of different compliance risks without needing, you know, one to one legal coverage for every agent to check everything, you know, and so like, there's a huge efficiency opportunity there. I think, you know, again, it's just about like, really, effective training and management. then, you know, again, like kind of protecting those, those models, you know, again, if they're, if you're building something specific, it's like, you need to make it private and you need to manage it and constantly update it. And so that's, you know, a whole other thing. Right. I know we spend a lot of time talking about this, like these new AI
Adam Parks (28:48)
That's a whole other discussion that we can have another that's a whole other discussion.
Anna Burke (28:58)
only agencies. It's like, well, are you just replacing human capital costs with deaf costs? You know, like it's, it's not free.
Adam Parks (29:06)
Well, and I continue to ask the question of the use cases or those individual consumers that need that live person. So I think that some sort of a hybrid approach is going to be the wave of the future. And I had done a podcast with Larry Costa from Capital Management Services, and we were talking about how he had reallocated a lot of staff to first party solutions, right? But it was all about reallocation of those existing resources because
Anna Burke (29:08)
Yeah.
Yes.
Adam Parks (29:32)
88 % of companies in 2024 were having trouble hiring. 81 % were having trouble retaining the people that they did hire. So I don't think anybody is looking around being like, I'm gonna get these collectors out of here. Like I don't need them anymore. I don't think I don't see it as a truly realistic approach in 2025. Maybe in 2030, I don't know what's gonna happen. I mean, just even like I said, over the last couple of weeks, things have advanced pretty significantly. And now the context windows that we're able to see or that
Anna Burke (29:57)
Yeah.
Adam Parks (30:01)
the artificial intelligence models are able to leverage in order to understand the data being presented to it before it formulates its response has improved pretty dramatically between ChatGPT 4 and ChatGPT 5. But I'm hoping most of the tech that we're talking about today is not a fancy wrapper around ChatGPT. Like it's different purposes, different use cases. But the final use case that we were talking about from an artificial intelligence standpoint was negotiation and self-service solutions, which is that existence of the portal. think that also ties into not only from that perspective, but we had mentioned the use of the right content to go along with the right message. So When we're thinking about self-service, I think about the content that's feeding my digital channels, my text messages, my emails. I also think about my portal. I think about the opportunities for negotiation. And in my mind, when I think about negotiation, it's not just the Herb Cohen back and forth negotiation. I'm also thinking about which offer am I going to present to which person at which time and for which reasons.
Anna Burke (31:05)
Yeah. Yeah. I mean, this just adds again, that like real time component that that individual specificity there. So these are all things that like, like we do today again, from an ML standpoint, but it's much more segment based. And so this lets you present these things kind of in real time. And I just love like, this totally ties back to your point about, you know, consumer psychology and behavior and like starting to capture more of those elements in these in these models and their interpretation and application. And like, I mean, that, to me feels like that's that's the silver bullet. Like that's the golden ticket there is like, how do you really start to predict actual behavior and like, you know, just like, very rapidly synthesize it into into a response, you know, via a self service channel.Predict behavior.
Anna Burke (32:01)
much faster than the human mind can respond to someone.
Adam Parks (32:04)
Well, I think about evaluating a portfolio and saying, what's the net present value of this portfolio of accounts versus what's the net present value of this account? And I'm only aware of one debt buyer out there that goes down to, and I'm sure that there's others at this point that have been going down to that like specific level of depth and understanding the individual account net present value and then making their negotiation decisions based on that. But I don't think that most organizations have that level of data science available to them.
Yeah.
All right, well, we do that for a number of organizations. that is actually very specifically one of the things that we provide, but it's exactly that. We're making that level of data science sort of commercially available. But it's that, but I mean, really.
Adam Parks (32:40)
Okay.
Anna Burke (32:48)
It's more than that. It's like you understanding the net present value and actually like capturing the maximum value from an individual borrower. There's a lot that happens in between there. And so that's, that's the thing.
Adam Parks (32:55)
Yeah, that I'm oversimplifying in terms of establishing value in order to drive next action. I that it sounds like you guys are actually managing next action and using that to feed what is going to happen down the
Anna Burke (33:02)
Yes.
Yes, but like this this AI piece of that is like, you know, taking that and actually interacting with the borrower to obtain that value, I guess, is what I'm mentioning. So there's definitely growth there, opportunity there.
Adam Parks (33:21)
Okay.
Well, that brings me back to the original use case of the, you know, the scoring and workflow management. Like, how are we going to drive that? So it sounds like you guys have really already kind of looked at all of these different use cases, selected the ones where you thought there was going to be the largest impact focused on those and, you know, have continued to add on to the ecosystem over time, which considering where we were from an AI ML perspective when Equalbley started, whatever it was three, four years ago, I mean, it's light years ahead in terms of what technology is currently available to the marketplace and having a platform by which to allow debt collection organizations to operationalize that type of technology in an easy way. Because you know what I've never seen in the debt collection industry? An IT team with additional capacity. Like with the capacity to go take on something else. No, because anytime that there's an extra guy in IT, there's an accountant there ready to cut that off.
Anna Burke (34:14)
true. Yes.
Adam Parks (34:21)
And so I think being able to work with a company like yours to help navigate some of those more complex data science questions, because data scientist is not a popular role around the debt collection industry as of today.
Anna Burke (34:36)
Correct, right. And appreciate the plug there for sure. But I think we're also, hopefully we have a good perspective going into it, just given our experience and knowledge of these other components of AI. But we are also rapidly trying to deploy different parts of this new technology as well, because it is, I think we said at the beginning, It is absolutely good for our industry. It's just like, how do you do it intelligently and, and ensuring like you're captured, like it should be a creative, like how are you getting value from, from these solutions?
Adam Parks (35:14)
What's your order of operations in order to deploy these things? Once you've identified the use cases that you think are the biggest value attribute for your organization, how do you then start to actively prioritize those and attack them one at a time? I'm a big fan of small steps first, Like take some baby steps into something, get comfortable with the idea, with the technology, with the capabilities, and then continue to build on. But that's just my risk averse personality.
Anna Burke (35:30)
Yeah. I feel the same way. I mean, it's like from a business leadership perspective, also, I think it's, it's easy to get distracted by shiny objects. And so I, I think I have a little bit of this fear just based on, again, some of the interactions I've had, but like, taking a step back and looking at like the strategic vision of the organization, does this line up with what you know, where we're trying to go our goals from, you know, revenue cost savings, like, whatever it is. And then also, where do we believe the industry is going as a whole? Like what's going to drive collections for the collections industry? And then how do we fit these solutions in versus, you know, letting a bunch of, you know, cool things like completely drive the direction of your company. So I think just like maintaining some focus on fundamentals without, you know, being left in the dust is still important, still applies here.
Adam Parks (36:40)
Agreed, Anna. I really do appreciate you coming on and sharing your insights with me today. This has been a great discussion, just like we had a dinner going into the depths of artificial intelligence and really how it can be applied and how it is being applied to the deck collection industry.
Anna Burke (36:55)
Well, thank you so much for having me on. Really enjoyed the discussion as well.
Adam Parks (37:00)
Awesome, for those of you that are watching, if you have additional questions you'd like to ask Anna or myself, you can leave those in the comments on LinkedIn and YouTube and we'll be responding to those. Or if you have additional topics you'd like to see us discuss, you can leave those in the comments below as well. And hopefully I can get Anna back at least one more time to help me continue to create great content for a great industry. But until next time, thank you so much. I really do appreciate all your insights.
Anna Burke (37:23)
Thank you so much.
Adam Parks (37:25)
And thank you everybody for watching today. We'll see you all again soon. Bye.
