How AI Is Helping Utilities and Credit Unions Prevent First-Time Delinquency
Preventing Delinquency
===
[00:00:00] Welcome to this deep Dive, a collaboration where human curiosity meets AI synthesis to explore the power of data in AI driven solutions, unpacking complex topics from the source material you bring us. I'm Tom, an AI host. And I'm Anna, also an AI host. Hello everyone, and we're ready to guide you through today's deep dive.
That's right. Sit back, relax, and discover how AI is helping utilities and credit unions prevent first time delinquency. Welcome to.
Yeah, it's a really timely topic. It's something that's, uh, increasingly on the radar for so many organizations right now. I'm definitely seeing that we're seeing this well, this significant shift where customers and members who have historically [00:01:00] been, you know, incredibly reliable payers are suddenly for the first time falling behind.
Right. And the traditional systems, quite frankly, they just aren't built to catch this kind of evolving challenge. Exactly. The old playbooks, they're often designed to react after the fact, aren't they? Not really to anticipate these shifts Precisely. So our mission in this deep dive is to extract the core insights from, well, a fantastic news source.
It's a blog post that's a preview of blast point's, latest white paper, a proactive strategy for financial stability, predicting and preventing delinquencies. Mm-hmm. Good stuff in there. We'll show you how leading organizations are truly getting ahead of this, this very real risk. They're doing it by combining AI powered predictive insights with, uh, modern digital engagement strategies.
So, okay, let's unpack this a bit for organizations already, you know, steeped in managing payments and collections, first time delinquency might seem like just another category. You'd think so. [00:02:00] Yeah. But it sounds like it presents a really distinct. Often overlooked challenge. What makes this particular type of delinquency so maybe elusive for traditional systems.
Yeah. Why is it proving such a tricky problem to get ahead of That really is the crux of it. Yeah. You see most traditional collections. Processes. They're fundamentally built on a reactive model. Okay? They're designed to kick in after a payment has been missed. You know, after an account is actually overdue, they basically wait for that red flag to pop up in the system, right?
Waiting for the alert. Exactly. They're just not engineered to look forward to, uh, analyze the subtle signals that might indicate a previously stable customer is maybe about to run into some financial difficulty. Uh, it's kind of like having a smoke detector that only goes off once the house is already, you know, properly on fire rather than detecting that first.
Whiff of smoke. Okay. So it's really about shifting from reacting to a problem that's already happened to proactively identifying a potential problem before it fully [00:03:00] materializes. That makes complete sense. Mm-hmm. And what's driving this trend then? These previously stable customers suddenly becoming a risk.
Is it purely economic or are there other factors kind of feeding into it? Well, it's often a mix of factors, really, but these economic disruptions, they certainly play a massive role. Sure. You've seen a lot of volatility recently. You know, persistent inflation, kind of eating away at purchasing power, unexpected job losses, or even just the, the cumulative burden of rising costs for essentials, housing, food, energy.
Yeah. All the basics. All the basics. These external pressures can just swiftly push a customer or member, someone who has always paid on time. Into a really precarious position. Right. And the critical insight here, I think is that these aren't your typical like long-term delinquent accounts. Mm-hmm. These are usually good intention payers who are just experiencing a sudden, often temporary financial shock.
And without the right tools to spot those early indicators, these emerging risks simply go unnoticed until it's too late or much harder to fix [00:04:00] exactly until the balances have piled up, making it a much larger and often more costly. Problem to resolve for everyone involved, the organization and the customer.
Okay. So given that traditional systems are essentially, like you said, operating with blinders on, always looking backward. How does AI powered predictive intelligence really turn that on its head? What's the fundamental shift that it enables for organizations to actually look forward and anticipate these issues?
Right. And this is where the innovation really becomes a game changer. The research we looked at highlights, BlastPoint customer intelligence platform as well, A prime example of this next gen approach, mm-hmm. Its core function is to leverage AI and machine learning to detect those subtle behavior shifts before a payment is even missed.
Okay. Yeah. Think of it not just as an early warning system, but a really intelligent one. One that continuously learns. So instead of simply setting static rules, like, you know, if a payment is 30 days late, do X the old way. The old way, yeah. Yeah. [00:05:00] AI allows the platform to continuously learn from these huge data sets, identifying really complex, often non-obvious patterns across many hundreds of data points that reliably predict future behavior.
Hmm. It's less like a simple checklist and much more like a, maybe a constantly evolving neural network that's predicting the next move a customer might make. That is a huge conceptual leap for the traditional methods. So it's not just flagging an overdue bill, but seeing the preliminary signs that a bill might become overdue down the line.
Precisely. You mentioned subtle behavior shifts. What makes 'em subtle? Exactly. And what specific kind of patterns? Is the platform actually looking for, because you know, that phrase can sound a bit abstract. That's a great question. Helps make it concrete. What makes these shifts subtle is that, well, individually, many of these behaviors might seem insignificant or just random, maybe to a human analyst, right?
Like one late payment isn't a crisis. Exactly a single, slightly delayed payment, or maybe one instance of dropping autopay enrollment that might not raise [00:06:00] a big alarm in isolation, but ai, it excels at correlating these seemingly minor disparate signals over time and across a customer's entire interaction history.
Okay. It finds a pattern that is statistically predictive of future delinquency. It's the combination and the consistency of these small shifts that the AI really illuminates. Got it. So what are some of those specific patterns? Well, the platform analyzes several key things. For instance, it looks for nuanced changes in payment behavior.
Okay. Like what? This could be a customer who say always paid their bill five days early, suddenly starting to pay right on the due date. Or maybe even consistently a day or two late, still paying, but the pattern changed. Ah, okay. That is subtle. Very subtle. It also tracks actions like dropped autopay enrollment.
I mean, if someone who's been reliably on autopay for years suddenly switches that off, that's a pretty clear signal. Something has changed in their situation or planning. Yeah, that makes sense. Another key indicator is digital [00:07:00] disengagement. So if a customer who regularly used your online portal or your mobile app to manage their account suddenly just stops interacting with those digital channels.
That can be another subtle red flag. Mm-hmm. Interesting. And crucially, it also folds in broader environmental factors like regional economic stress, uh, like community level data. Exactly. If a specific zip code or maybe a neighborhood is experiencing a sudden spike in unemployment claims or other signs of economic downturn, the customer is living there, might just face a higher collective risk.
Right. Wow. Those are incredibly specific and insightful signals. It's combining the individual behavior with these wider economic trends. It's not just one isolated factor, is it? It paints a much clearer, more forward-looking picture. That's the idea. So once the platform spots these complex patterns and sort of determines a customer might be at risk, what's the actionable output?
Like, what does it do with that information? How does it help the teams? Right. Because data without action is well. [00:08:00] Just data. It isn't valuable. Exactly. So based on these analyses, the platform generates what are called propensity scores. Now, these aren't just a binary like yes, no flag for delinquency.
Okay? Think of them more as a spectrum, maybe a score from zero to a hundred percent, indicating the likelihood of a customer or member becoming delinquent within a specific timeframe, say the next 30 or 60 days. So it's probability score. Exactly a probability. And these scores allow organizations to segment their customer base with incredible nuance.
You can identify who is at the highest risk, sure, but also crucially who still has a high propensity to pay if they receive the right intervention, even if they're showing some early warning signs. Ah, so you can prioritize better precisely. This precision enables teams to prioritize their outreach. So instead of that old blanket reactive approach, they can now target specific customers at the right time through the right channels with a message that's highly [00:09:00] relevant and hopefully empathetic to their specific situation.
Right? It's all about being proactive and precise rather than reactive and just general. Okay. So it's about intelligent targeting. That makes sense. Mm. But what does this all mean for actually helping people? Because you know, it's not just about predicting who might fall behind. Mm-hmm. It's about how you engage with those insights.
Mm. Right. How you actually reach out and make a tangible difference, both for their financial wellbeing and let's be honest, the organization's bottom line. Absolutely. Prediction is really only half the equation. Once you know who is potentially at risk, the next critical step is effective engagement.
Right. And our source material, the white paper preview, it strongly emphasizes that modern customers, particularly across utilities and credit unions, they now expect seamless digital experiences. Yeah. That's just table stakes now, isn't it? It really is. They want to interact with organizations in ways that are convenient and intuitive for them.
You know, just like they do with any other service provider in their daily lives. [00:10:00] They live on their phones, they expect instant access, personalized communication. That makes perfect sense. I mean, nobody wants a generic, impersonal letter in the mail three weeks late when they're used to managing their entire life through personalized apps and instant messaging.
Exactly. Not effective anymore. So how does combining these AI insights with these robust digital strategies actually improve the outcomes? What does that look like in practice for an organization trying to implement this? Well, it's really about delivering the right message at the right time through the right channel, and often doing it before the customer even fully realizes they might need some help or flexibility.
Proactive. Yeah. The white paper preview highlights several key digital engagement components. First, there's personalized messaging. So instead of that generic past due notice, you can maybe send a message that acknowledges their longstanding history of on-time payments, perhaps offers flexible payment options tailored to their [00:11:00] likely situation based on the AI insight.
Right. Something more understanding Exactly. Or even just a friendly reminder through their. Preferred communication channel. Maybe it's a text message with a direct link to make a payment or see options. Makes sense. Second, self-service payment options are absolutely crucial. Empowering customers to manage their accounts, make payments, or set up payment plans on their own terms through an app or a user-friendly online portal, that dramatically reduces friction.
It gives control, it gives 'em a sense. Control. Yeah. And finally, mobile first communication is pretty much non-negotiable today for sure. So many people simply do not check email regularly or not quickly anyway, but they're constantly on their phones. Reaching them via text messages, push notifications, or through a well-designed mobile app, significantly increases the likelihood of engagement and ultimately action.
All these strategies when they're directly fueled by those granular AI insights, they lead to significantly improved on-time payments and a [00:12:00] tangible reduction in actual delinquency rates. This sounds incredibly powerful. It paints a picture of a more well empathetic approach. Mm-hmm. But also a far more efficient one for tackling this longstanding problem.
Mm-hmm. But the big question. Does it actually work in practice? Are there real world examples? Are organizations seeing tangible results from this combination of AI and smart digital engagement? Yeah. That's always the ultimate litmus test, isn't it? Does it actually move the needle? Right? And the answer seems to be a resounding yes.
The blog post previews a very specific and frankly compelling example of the kind of impact these strategies can have. Okay, let's hear it. It highlights a utility company. Yeah. That strategically targeted first time late payers very early in their process. They use these predictive insights to identify exactly who to reach out to and how.
Yeah. And by engaging proactively and appropriately, they were able to recover an impressive $6.7 million in delinquent balances. Wow. $6.7 [00:13:00] million. Hmm. That's, that's not just a small win. That's a massive financial impact for an organization. It really is significant. And crucially, you said it was from a segment of customers that, as the source notes had a high propensity to pay.
So this wasn't about just chasing after, you know, lost causes. Oh, exactly. It was about identifying those reliable customers who just needed a timely, maybe personalized nudge or a flexible option to get back on track. That's a fantastic point, and it truly speaks to the efficiency gained here. Often traditional methods might only recover a fraction of that amount, right?
Yeah. And typically that recovery comes from customers already deep into delinquency, where the cost of collection is much higher, and maybe the customer relationship is already pretty strained. True. What makes this $6.7 million particularly impactful is where it came from. Those first time late payers, customers who, like we discussed often just need that timely understanding intervention.
It really speaks to the efficiency of intervening early and precisely. [00:14:00] Something often missed by those. Older blanket reactive approaches. It's not just the sheer amount, but the efficiency of the recovery, and importantly, the preservation of the customer relationship. That makes it such a powerful insight That makes sense.
And the source makes it clear that this is just one preview, you know? Yeah. Just the taste of the kind of detailed results and success stories that are explored more deeply in the full white paper. It really underlines the potential here for both significant financial recovery and, uh, strengthening customer loyalty at the same time.
This deep dive has truly illuminated the power of proactively addressing first time delinquency. It seems combining AI powered predictive insights with smart, personalized digital engagement as the way forward. Mm-hmm. It really feels like a true win-win. Organizations protect their bottom line. They enhance efficiency while customers get to avoid the stress, the fees, and maybe the credit impact that comes with falling seriously behind.
Absolutely. And this whole deep dive, it really raises an important question for [00:15:00] you, the listener to consider. What could preventing even a small percentage of first time delinquencies mean for your organization's financial stability, for its operational efficiency, and maybe most importantly for its long-term relationship with its customers and members.
That's a great question to ponder. Yeah, it's a strategic shift that moves beyond just collecting overdue payments to actually fostering financial wellbeing and loyalty. So if your organization is seeing rising delinquency, or maybe if you simply wanna stay ahead of the next wave of economic shifts and enhance those customer relationships, this white paper really sounds like a must read for you.
Definitely worth checking out. It's clearly more than just theory. It provides a practical, actionable guide. The preview suggests you'll learn exactly how to predict and prevent first time delinquency, how AI and machine learning can dramatically enhance your existing collection strategy. Mm-hmm. Real tools and discover proven best practices for digital engagement that truly resonate with [00:16:00] today's customers.
Ultimately, it sounds like it provides a repeatable framework to improve your bottom line and strengthen those vital customer relationships. Yeah, solid roadmap. We definitely encourage you to download the white paper today. And of course, subscribe to this deep dive. For more insights into critical topics like this one, you can find the link to the white paper right in the episode description.
Thanks for tuning in. Thanks everyone.
