Delinquency Behavior: What Today’s Signals Reveal About Risk, Reachability and Recovery

Delinquency is rising and confidence in traditional credit data is waning.

In this episode, Carrie Coker, Senior Director of Market Planning for Servicing and Recovery, Denise Cross, Director of Consultative Solutions for Servicing and Recovery and Joe Jarpe, Senior Manager of Data Science, explore delinquency behavior, behavioral risk signals, alternative data and transiency to explain why many consumers enter collections with no recent credit delinquency. 

Learn how reachability, public records, credit invisibility and early warning signals are reshaping collections strategy, prioritization, compliance and recovery performance.

To view the data we discuss in the episode, click here.

DISCLAIMER: The information provided in this podcast is for informational purposes only and is not intended to and shall not be used as legal advice.  The views and opinions expressed in this podcast are solely those of the speakers and do not necessarily reflect the views or positions of LexisNexis Risk Solutions. LexisNexis Risk Solutions does not warrant that the information provided in this podcast is accurate or error-free.

LexisNexis and the Knowledge Burst logo are registered trademarks of RELX Inc. Other products and services may be trademarks or registered trademarks of their respective companies. Copyright© 2026 LexisNexis Risk Solutions.

Transcript

Welcome And Host Introductions

SPEAKER_02 0:01

Welcome to Credit in Focus, a podcast series by LexusNexus Risk Illusions that unpacks the global complexities of Credit Risk across the customer lifecycle, from marketing and origination to account management, collections, and recovery. Let's get into it.

SPEAKER_00 0:20

Welcome back to Credit in Focus. My name is Carrie Coker, and today I'm joined by my colleagues, Denise Cross and Joe Jarpey. Denise, why don't you introduce yourself and share with everyone what it is you do at Lexus Nexus Risk Solutions?

SPEAKER_01 0:35

Sure, thanks, Carrie. My name is Denise Cross, and I'm the director of our solutions consultants in the servicing and recovery vertical at Lexus Nexus Risk Solutions. My team's responsible for working with all of our customers across the organization and looking at their their collections operations or servicing operations all the way through litigation. Are they utilizing our products? Are they utilizing them in the best practices possible? Been with Lexus for just celebrating my 20th anniversary. Thoroughly enjoy what I do. Joe, let me pass it to you if you'd like to do an introduction of yourself as well. Thanks, Samise.

SPEAKER_03 1:06

Uh yeah, my name is Joe Jarpey. I'm a senior manager of data science here at Lexus Nexus for Solutions. Been here going on 19 years developing solutions across the entire credit lifecycle. And currently I manage a team of data scientists tasked with creating thought leadership from a uniquely Lexus Nexus point of view that we've called the Information Hub. The analyses that we'll be discussing today have been produced by my team.

The 2026 Debt And Delinquency Picture

What Traditional Credit Data Misses

SPEAKER_00 1:26

Great. Thank you both for being here. You're two of my very favorite people. Before we dive into the study and data from our information hub team, let's set the stage around our current economic conditions. And for a reference point, today is April 29th, 2026. According to the New York Federal Reserve and their quarter four 2025 report on household debt and credit, overall household debt is sitting right around 18.8 trillion, of which 4.8% is delinquent. Student loan debt is about 1.6, 1.7 trillion. And a quick note here, the quarter one 2026 report is due sometime in mid-May 2026, so just a few weeks from now. I think there are some expectations around high levels of delinquency flattening out a bit, but still remaining elevated, well above the pandemic era lows. But we'll see in a couple of weeks when that new report comes out. Now, everyone's watching delinquency rates, not just for that outstanding student loan portfolio and the expectant resumption of the involuntary collection efforts, but also across other assets like auto, card, personal loans, and mortgages. Mortgages have had that uptick recently in a regional fashion around delinquencies and rising rates there. But the bigger story is how consumers behave before they ever even land in collections. Today we're going to connect those macro pressures like student loan repayments and the micro behavior signals like transiency that directly impact right party contact and prioritization. Denise, let's start with you. When you look at the current environment, what's the biggest disconnect between what traditional credit data shows and what collection teams actually are experiencing operationally?

SPEAKER_01 3:18

Yeah, great, great question, Carrie. And I think, you know, we we're all familiar with what's missing on credit reports, scenes and judgments. That's the biggest one that comes to mind, right? And that can make a huge impact when you're looking at the ability of someone to repay. Now we know that there's been the student loans, you know, reporting, not reporting, are the balances correct? How many do I have, et cetera? So again, the total picture of a consumer's delinquency is just not there. It's just not necessarily reflected on what you're seeing in traditional credit reports today. And that can have a big impact on how you decide to collect an account, settle an account, not settle an account, work an account, sell an account. So all of those things are heavily reliant on good information about your consumer's ability, stability, and willingness to pay. So I think those are some of the biggest things that are missing. And I think when you look at consumers that have credit today, some of those got credit when that large swath of student loans were removed, what, a year or so ago. So it it took someone's credit score that maybe was a 550 up to a 700, right? And they went out and, as most people do, applied for credit. And so they probably still have the same income, same inability to repay that they had when they had that big student loan on their the student loan didn't go away. It just wasn't reported on the credit report. So you've seen consumers adding debt and large volumes of debt with you know still the inability to pay. So I think that's probably from an operations perspective. Just really identifying those consumers that have the ability, ability and willingness to pay is harder and harder. Uh you're gonna have to look at other other data sources, right? Other things about a consumer to decide liquidity and and the you know how you're gonna work the accounts because it's expensive to work an account. We know that traditionally uh fees were higher than they are today. You know, I think uh really finding that happy balance between how much is it gonna cost me to collect an account and is that account gonna be liquid or not is is becoming increasingly more difficult. So, Carrie, you know, how do you think collectors should think about accounts when a consumer's budget may be impacted by wage garnishment or offset?

Prioritization When Bankruptcies Rise

SPEAKER_00 5:18

That's a really great question, Denise. And and I know that's one that we are talking with our clients about regularly. What we hear is that servicing and collection teams are focusing on implementing prioritization strategies like filtering for collectability earlier on. But first, and before any resources are deployed, and I know this is one of your favorite points to talk about, Denise, teams really need to make sure that that debt is legally and practically collectible. For example, let's talk bankruptcy. In tandem with those elevated levels of delinquency and default, bankruptcies are rising for both consumers and businesses. Consumer bankruptcies are showing an upward trend moving towards pre-pandemic levels, and business bankruptcies show that same directional trend. These bankruptcy trends don't matter. Um they they really matter because it changes the recovery playbook, especially around compliance channel strategy and timing. If bankruptcies are rising, the penalty for poor prioritization grows. And they're gonna, you're gonna waste those touches on accounts or those resources, right? You're gonna waste them. Um, that and and that's really it's costly. They're resources and you're wasting that ability to expend those same resources in the uh actual collection activity. So, Denise, what's the most important operational shift that teams should make when bankruptcies rise? Segmentation, monitoring, or how they treat contact strategies.

SPEAKER_01 6:39

Oh, yeah, for sure. I mean, I think once you've filtered out, and you know, bankruptcy is bankruptcy. So if I have 10,000 accounts today and a thousand of them go bankrupt, I still have to focus on those 9,000. So in theory, even though the bankruptcies are rising, it's not necessarily as much of an impact to my operational strategy, right? So I get those out and then I just have to focus on those, you know, 9,000 accounts I have left. And the biggest thing for me today is really watching those consumers, and I think you need to watch them before they even go delinquent. So implementing, you know, triggers, right, or early warning signs of financial instability. Is someone moving? Are they changing their phone numbers frequently, right? Are they changing from ATT to cricket? To your point, Carrie, you know, I think watching those consumers, some we tend to want to wait until someone goes delinquent, right? And then we say, we really should go out and get a phone number or, you know, find an address for Denise, an email, et cetera. And I think you can see early triggers prior to me going delinquent. And it's really important to get a get a hold of me before I get into a 90-day delinquency. Once I'm there, I'm there, right? I've already gotten to myself into a situation where I'm 90 days past due and there's nothing I can do about it. So it's much more efficient, in my opinion, to catch that person before they go delinquent. Even if I get them in that 31, 35 day delinquency bucket, I can find out what's changed in their life and help them resolve. Because again, once I get into a 90, 120 day delinquency, I'm I'm beyond in my mind the ability to figure out how to resolve. So I think we have to catch them with triggers, monitoring, and just watching your consumers long before they ever go delinquent.

SPEAKER_00 8:03

Yeah, really strong points, Denise. And I think, you know, when I reflect back on my experience in the consumer data ecosystem during the last Great Recession, that's exactly where many of our customers pivoted to was proactive monitoring with trigger programs across the board. And I think that really highlights the tightrope. It's a tightrope act, if you will, uh for modern collections. They're really balancing those high granular segmentation strategies with the absolute non-negotiable compliance requirements of bankruptcy. And it's legally necessary, right? It's it's a it's a high risk for bankruptcy noncompliance and it's a foundation of operational integrity. But as any seasoned collection leader knows, compliance is the floor, it's not the ceiling. So to find out where the real lift is happening, we need to look beyond the spreadsheets and into the story the data is telling us when we zoom out. That brings me to Joe. If there's anyone who can turn a mountain of raw variables into a roadmap for operational excellence, that's you, Jo. I know you've spent quite a bit of time digging into our unique data assets here at LexusNexis Risk Solutions and finding patterns in the data that can help provide deeper insights into a consumer's financial standing and ability to pay. I really love hearing your insights. So, do you mind sharing some of those insights and how they might be able to apply to our customers directly?

Who Enters Collections And Why

SPEAKER_03 9:24

Of course. Thanks, Carrie. That's a great, great lead-in for me. So one data set the Information Hub samples on a regular basis are inquiries on consumers who are actively being collected upon in FCRA third-party collections. Now, these inquiries come from a wide variety of collectors and debt types. We take those consumers and run all our different attribute sets on them, including alternative and trade line-based credit information. So these data sets show us who's in collections now. On the slide that we're sharing here, we can see in this study sample, age is trending older. Really, all age cohorts are growing as a share, as a higher share of collections entrants since 2022. In particular, that 65 plus group is growing pretty pretty dramatically from where it had been historically. Our next slide is looking at this from a credit score perspective. And when I say credit score, I mean trade line only based scores, akin to a FICO or Vantage cross-industries score. This particularly one, this particular one was shown shown was developed internally for RD purposes at LexusNexis Risk Solutions. And we can see that most consumers in collections are either deep subprime or credit invisible. Really not at all surprising here. So the data suggests the mix is aging, meaning the reasons for distress and the best engagement approach may look different. And credit invisibility matters operationally. If a consumer doesn't have scorable trade lines, traditional score approaches is context. How does an older collections population change how you think about empathy, channel selection, and payment plan design? I'll start with that one with Carrie, please.

SPEAKER_00 10:50

Yeah, it's it's so interesting. Managing an older demographic of delinquent or defaulted accounts really requires shifting from that sense of urgency to a sense of resolution. Empathy really needs to evolve from simple reminders to acknowledge what we might call debt fatigue, replacing high pressure type demands with a more collaborative tone that reduces the consumer's defensive barriers. Strategically, I think this means moving away from some of those high-frequency outbound calls in favor of low friction channels, like texting or a mailing, maybe that directs them to a self-service portal that allows the consumer to engage in their own terms when they finally reach a moment of liquidity. Payment plans designed for sustainability over speed or utilizing micropayments and flexible grace periods. That can help ensure that agreement can withstand the long tail of recovery without collapsing at that first sign of financial turbulence or event-related distress. Denise, what are your thoughts on that?

SPEAKER_01 11:51

Totally agree. And I think everything that you've you've said is spot on. And I think, you know, in just looking at that population, look at when phone calls come over on your on your cell phone. If I don't recognize you, I'm not answering. And and we know that that it's times have changed. Maybe I don't like email. I like letters. I want to go to my mailbox and get a letter. I want to see, I want to pay with a check. I don't want to go online and pay. So I think modifying your strategy, right, is is definitely necessary in dealing with this population because it's not it's not all the same straight across the board. Right. I can tell you that my mom, for example, she doesn't answer her phone. If it's not me, it's not a getting answered. She doesn't check her email. She thinks everything is spam. Everything is someone's trying to fish her or you know, take her data or give her information. So I think you really have to adjust your collection strategy to accommodate accordingly.

SPEAKER_03 12:38

That's the really interesting answers there and makes me think about ma my parents. I wish they were as savvy as yours, Denise. Um what do teams typically get wrong about that uh credit invisible consumer group?

SPEAKER_01 12:49

I think it's making assumptions, right? And assumptions are never good. So just I may be somebody who is a cash-only kind of girl. It doesn't mean I can't pay my bills or don't want to pay my bills. Maybe I'm somebody who just likes to have one credit card or you know, one mortgage or what have you, and I I'm a I'm a cash only. So I don't think you can make assumptions going into a credit invisible person and assuming that they're trying to, you know, fraud the system or they're they're a fraudster kind of person. I think traditionally just you have to just treat your operational strategy should be straight across the board, right? You're gonna score the accounts, you're gonna look at the accounts, and just because I fall out credit credit invisible or or maybe hard to score doesn't mean I have an inability to pay. It really is just about getting a hold of that consumer and talking to the consumer and figuring out, you know, what what is their ability, what is their willingness to repay.

When Delinquency Disappears From Files

SPEAKER_00 13:32

Denise, I would echo that. I think one of the biggest mistakes collection teams make with the credit invisible type consumers is treating that lack of data as a high-risk intent not to pay. Because these individuals, oftentimes they're younger or maybe recent immigrants or low income, they lack that traditional credit score. Some automated systems may tend to default to a more aggressive collection tactic that unintendedly then alienates the consumer before a conversation can even begin. Instead of relying on that stagnant or incomplete traditional credit bureau data alone, collection teams should really leverage alternative data points like professional licenses or, like Denise referenced earlier, that mobile phone consistency to build more nuanced profile of a financial stability. But failing to adapt those outreach strategies or offering just straight across the board rigid payment structures that don't account for that non-traditional income cycle, teams might miss the opportunity to build brand loyalty with a population that is often highly motivated to establish that financial footprint. So looking back at the data, we had a surprising finding in our study. I know we saw some consumers with no recent delinquency before we saw them in uh coming into collections. Why don't you explain that data and talk that through with us?

SPEAKER_03 14:50

Yeah. Thanks. In our sample of consumers and collections, we saw roughly half of them entering collections showing no recent delinquency, defined here as showing no delinquencies more than 30 days past due in the past three months, which is very surprising for a group of uh of individuals who we know are being actively collected upon. I think this is the stat that should make every prioritization model pause about half of those first-time collections entrants not showing recent delinquency on a traditional credit file. So if you're relying on that traditional credit file and looking for trade line delinquency alone, you're pet you're potentially late to the story and you may misrank accounts. Harry, what do you think explains this? Are we talking reporting lag, non-traditional debt types, or behavior that credit bureaus don't capture quickly?

Transiency And The Reachability Problem

SPEAKER_00 15:31

So we the answer here is complex and it's constantly evolving, but there's some key factors. Um, and we talked about this in the past on this podcast in particular. It's been a series of unrelated regulatory and broad industry changes that have really resulted in this overall contraction of visibility to delinquency in traditional credit files. One of the big ones that's that's pretty obvious, but people tend to forget about is that back in 2017, so it's been a while now, under NCAP or the National Consumer Assistance Plan, I believe it was six state AGs got together and sued the credit bureaus around accuracy of public records. What was the result? Well, the credit bureaus had to remove most civil judgments and tax liens from their credit reports. Broadly, we say, oh, public records aren't on credit reports anymore. It's not totally true, but the vast majority of civil judgments and tax liens were removed. That's really a significant visibility to outstanding debt obligations that's simply not present on credit reports any longer. I mean, there were FICO adverse action codes that specifically specifically called out the presence of public records. And once the public records were removed from credit reports, those adverse action conditions just simply didn't generate anymore. That's a huge gap. Also, around the same time period, but separately, right? Um, original creditors began deleting charged off portfolios from the credit bureau furnishing processes after they sold them to debt buyers. I mean, think about that. That removes significant visibility to severe delinquency and default. When they deleted those portfolios from credit bureaus, they're deleting the full history of that trade line. Then, again, kind of separately, third-party collection agencies began adopting more consumer-friendly policies to do the same. They delete collection trade line if the consumer settled or paid in full. Now, most of those collection agencies have their deletion policies posted publicly on their FAQs, on their websites. So it's not that traditional credit data isn't good per se, it's that it no longer should be relied upon really as a complete view of the consumer's financial standing. Alternative data then allows you to gain a clearer picture of the consumer by supplementing and or filling in additional insights for your consumer population and then subsequently factor that information into your recovery efforts. Joe, our next section of data is on transiency and why what we call reachability is really getting harder. Can you walk us through some of that insight?

SPEAKER_03 18:03

Yeah. The data shown here are new addresses for individuals in the six months leading into collections. And we can also build out two more lines, phones and emails, essentially all the different ways to contact a consumer. So this shows us that consumers who are moving into collections show lots of new addresses, phones and emails on their way into there. So this could be indicative of transiency, right? Not a surprise when you're talking about uh consumers who are in collections, but it could also be indicative of folks who have been in collections in the past and they know to change their contact info to make that reachability much more difficult for the collectors. So on the next slide here, we have a slightly different view of this, maybe kind of additive to the one that we had previously. And we can see that the majority of consumers in collections are associated with multiple addresses, phones, and emails in the year leading up to collections, right? So we're not just talking about new phones, we're talking about pre-existing phones, emails, and addresses for these folks, right? So this is the operational reality. It's not just their willingness to pay, it's that ability to reach, right? That transiency drives inefficiency. More incorrect phone numbers and emails, more wrong addresses. That adds up to more compliance risk if the identity isn't resolved correctly. Denise, what do you think is the most effective way you've seen teams reduce waste from contact decay? Better data, better segmentation, or a different cadence?

Public Records Assets And Bankruptcy Signals

SPEAKER_01 19:22

Yeah, great point, Show. And I think all the above, right? I mean, everybody would love to go into an account that gets placed with you with good phone numbers, a good email, and a good address. And we know that unfortunately that's generally not the case, right? So you really have to strategically rank accounts, really based on some robust real-time data. Either you're monitoring monitoring alerts, right? Looking at non-traditional alternative data, right? Because again, people are pretty good at knowing how to kind of manipulate the system, right? And especially if they this is not their first rodeo, as you call it. So enhancement with alternative data sources kind of gives you a clearer view of a consumer's financial standing. I might have a property that's paid for. I have three professional licenses. You know, there's a lot of things about a consumer notwithstanding just credit data. So really utilizing better intelligence with alternative data. It really enables smarter collection decisions and strengthens compliance. And that's the name of the game, right? So next up, Joe, let's talk about public records and asset signals, risk and opportunity. Joe, what does this data tell us?

SPEAKER_03 20:21

Yeah, so what we see here is maybe a little bit surprising, but you know, when when you think back about it, it's talks about it really how collections isn't a monolith. Uh there's a variety of different individuals in there. So this graphic is showing more than a quarter of consumers in third-party collections are actually property owners. Property ownership challenges the assumption that these collections populations are always assets in. The nuance is in that liquidity versus assets. So similar to the overall bankruptcy trend shown earlier. We also see that consumers in collections show a slow but consistent increase in bankruptcies since 2022. That's that red line here. We saw bankruptcies up 30% in the past for a few years there. We're seeing this same trend show up for individuals who are also in collections. Chapter 7 showing about double the rate of chapter 13's making up those folks who are in collections here. Jump into the last set of public record derogatory events here. We've got judgments, liens, and landlord tenant disputes. And they appear consistently across corridors. Public records can indicate that severe distress, which we'll talk about in our study later, but they can also help refine skip segmentation and compliance strategies. Erie, how do you think this data helps our customers make more informed collection strategy decisions?

SPEAKER_00 21:31

Well, Joe, I I love this data. So the realization that over 25% of consumers in third-party collections are actually property owners, that fundamentally flips the asset thin stereotype right on its head. For our customers, this kind of data can bridge the gap between a perceived risk and actual recovery potential by highlighting that critical distinction between liquidity and assets. A good consumer could be called cash. Core in the moment, but they aren't what we might consider value core. By integrating public records like judgments and liens into their segmentation models, our collection customers can move beyond surface level credit scores to really identify that severe distress versus temporary setbacks. This allows then for a much more surgical strategy, refining those skip tracing efforts to find high value candidates, ensuring compliance with bankruptcy state to avoid litigation, and ultimately prioritizing accounts where there's real collateral or long-term stability to back up a settlement.

Four Distress Signals Across America

SPEAKER_03 22:36

Cool. Great points there, Terry. Now we're going to move on to a new study here. We're going to move away from just consumers and collections to the entire U.S. adult population, but we're still going to focus on those public record derogatory events, what we're going to be calling distress here, right? So we're defining distress as anyone who has a public record derogatory event such as a lien, judgment, landlord tenant dispute, or a bankruptcy in the previous year. So similar to some of the data we saw earlier, we can see that public record derogatory events are up 40% compared to 2023 at the end of 2025 here. Our question here was what is driving this increase in uh in public record derogatories during this time period? And what are the signals comparing 2023 and 2025? My team that did this analysis came up with four pretty consistent signals across this time period. What changes is really that rate, as we see the 40% up here. The relationships are mainly all the same, but these four signals really call out that uh distress during these time periods, and we'll step through all four of them separately. First signal is renters. Renters consistently show double the derogatory risks compared to homeowners in both 2023 and 2025. We know the overall rate has gone up 40%, like I just said, but the increase in risk for a renter versus a homeowner has stayed pretty consistent over that time. It's about double the rate of public record derogs in the past uh year. The next signal is movers. So on the graphic on the left side, we see there are fewer movers overall since 2023. And really, I've looked at this graphic going back decades, and the moving rate has gone down significantly over the past few decades and really elevated and increased during the COVID time period. What we see here is really the one place where there's some differences on the right hand side between between 2023 and 2025. In 2023, the people who were moving were slightly less risky than the non-movers because there were way, way more homeowners buying houses, and generally there was much more stimulus and accommodation still working their way through the pandemic economy to stave off these derogatory events, particularly for renters. But we're seeing much higher risk in 2025 because homeowners are moving much less, and all that pandemic money has essentially dried up to prop up those uh those folks that that need that assistance. The third signal we're looking at is living alone. Living alone correlates with elevated distress no matter how old you are. We can see that on the right hand side here. Um but that's particularly acute among uh younger adults. Let's move back over to the left graphic. We can see that there are more people living alone due to that aging population. Roughly 3.4 million more people living alone in 2025 versus 2023. But younger people are living alone less. We can see that uh that rate dropping by a percentage point, that 18 to 44-year-old grouping, going from 29.3 to 28.3%. It's pointing to an increase in financial distress, causing more cohabitation for young folks. And then when you move back to the right-hand side, you can see that young population who are still living alone at the end of 2025 are showing much more public record distress than their counterparts who are uh not living alone. The last signal is a little bit more nuanced than the other signals here. The way to understand the high amount of relatives and associates is to think about how an individual is actually tied to that many adults here. Generally speaking, it is through shared addresses and not an overwhelmingly large family. So that when we think about this particular variable, it is a proxy for transiency, and transiency correlates with those higher derogatory rates. So generally, the more you're moving, the more relatives and associates that you're gonna pick up, and the more public record distress that's happening here. We see that tipping point at about 20 or more relatives and associates for this particular grouping. And that that makes up, you know, roughly 16% of the U.S. adult population, but over a quarter of those with that public record derog in the past year. Now, bringing it all together, we've walked through all the four primary risk signals and how similar they are in 2023 versus 2025. We know individually that renting, moving in the past year, living alone, and having a large amount of relatives and associates as a proxy for transiency here leads to higher levels of public record distress. But when you move to the right hand side here, the more these signals stack up on each other, those derogatory rates of public record distress rise sharply. When a consumer has all four signals, no matter whether it's 2023 or 2025, that risk jumps dramatically compared to all the other groups of signals. Four signals is, you know, nine times, ten times higher than no signals, but it's also more than double three signals. So all these risk factors compound upon each other and really lead to some outcomes that are less than ideal here. Kiri, what do you think is important about this data that I just stepped through?

Operational Takeaways And Closing

SPEAKER_00 27:40

So this is incredibly insightful. What's the powerful thing here that pops out really is consistency. These signals show up as predictors across time, not just in one year. This data has the ability to quantify transiency as a direct predictor of recovery risk. When we see signals like frequent moving, renting, or high density of associates clustering together, we're looking at a roadmap really of public record distract. For collection operations, the four signal group really represents that critical tipping point where derogatory rates don't just rise, they skyrocket compared to those, especially compared to those with zero or one signal. Recognizing that, that's going to allow teams to move away from that one size fits all approach and instead use these indicators as high definition lenses for segmentation purposes. By identifying these high transiency consumers early on, collection professionals can pivot their resources towards more sophisticated things, specialized outreach, ensuring they aren't wasting high cost efforts on accounts where the stability of the consumer just simply isn't there. This is this is great insight, Joe. Really interesting. Denise, um, if you could oper operationalize only one of these signals into a collection strategy tomorrow, which would it be and why?

SPEAKER_01 29:04

Yeah, great question, Carrie. And I like all of them. So if I could take all of them, I would. But the question being, if I had to select one, you know, I don't think the takeaway and that's there isn't more data. It is better prioritization, utilizing these signals to figure out, you know, collections is expensive. How do I get to the right person fast and reduce, you know, my dead ends and my costs? So transgency is rising, identity and contact resolution becomes foundational. I just have to get to the consumer. If people could cure themselves, they would. They wouldn't be in this circumstance if they just knew how to pay and had money to just, you know, to pay the bills. They really need that talk-off conversation, so you have to reach them. How you do that is important, right? And I think, again, contact resolution is just the foundation. So I think the strongest lift comes when you layer signals. And I think Joe's done a phenomenal job of showing us what those signals are, right? And I think prioritization is key and utilizing the utilization of the alternative data, I think we've we've shown is key. Anything to add, Carrie?

SPEAKER_00 30:03

Yeah, I I would just compliment you, Denise. I think that's excellent. I know you've spent over 30 years in this particular industry. You just celebrated your 20-year anniversary here at Lexus Nexus Resolutions. Excellent points. And I I think just to kind of to close the loop, if you will, on this really, really interesting stuff, Joe, Joe, from InfoHub, amazing. I think the main thing is that collection teams really need to focus on the fact that delinquency is rising. Um, and and the bigger challenge and the bigger change there is identifying that consumer behavior and those signals. So teams that adapt to behavior will win efficiency back, right? Better insight leads to better outreach, and better outreach leads to better recovery. So, with that, thank you, Jo and Denise, for joining our discussion today. For more information on what we discussed, please look in the show notes for links around the data.