AI Reshapes Loan Decisioning for Credit Unions Ready to Lead
AI Reshapes Loan Decisioning for Credit Unions Ready to Lead
===
Tom: [00:00:00] Welcome to the Blast Point Deep Dive podcast.
Anna: Hello everyone. This
Tom: is where we explore the power of data and, um, AI driven solutions. It's a collaboration between human insight and AI analysis. I'm Tom.
Anna: And I'm Anna. Today we're diving into how AI is really starting to reshape credit union lending based on the information we've gathered.
Tom: That's right. So, uh, sit back, relax, and discover how AI is changing the landscape of credit union lending. Welcome to.
Anna: The traditional ways of lending, you know, they work to a point, but some material shows they can overlook opportunities.
Tom: Yeah. And miss out on people who are actually quite credit worthy. Right? Yeah. Like if you only look at A-F-I-C-O score or a strict [00:01:00] income threshold.
Anna: Exactly. That doesn't always paint the full picture of someone's financial health or a reliability,
Tom: so who gets missed.
Mm-hmm. Sources mentioned a few groups.
Anna: People like gig workers, you know, with fluctuating income, recent grads just starting out, maybe immigrants who have a good history, just not here yet, they often get overlooked by those older methods.
Tom: And meanwhile, credit unions are facing more competition, aren't they?
From those slick digital lenders.
Anna: They are. The pressure's on to adapt, become more efficient, more targeted,
Tom: and this is where AI steps in according to the sources we looked at offering better data. Smarter segmentation
Anna: precisely, and predictive insights. It's not just about automating things, it's about.
Um, a deeper understanding, better targeting, and importantly, more inclusive growth.
Tom: Okay, so let's dig into that first point. Understanding the whole member. Mm-hmm. How does AI go beyond just that credit score number?
Anna: Well, the sources indicate AI can analyze a much, much wider range of data points, things traditional methods might not even consider.
Tom: Like [00:02:00] what sort of things?
Anna: Uh, payment behavior over time. Paying bills consistently. For example, income stability, even if it comes from multiple sources, how members are already engaging with the credit union.
Tom: Ah, so their existing relationship matters,
Anna: right? And even broader factors, sort of social determinants that provide context to their financial situation.
Tom: So this could really help people with say, thin credit files. Students, maybe gig workers, like you mentioned, immigrants.
Anna: Exactly. Or even retirees who haven't used credit recently. AI as described in the material, can potentially spot those signals of reliability that a simple score might miss.
Tom: That seems like a big deal.
Is there data on how many people might be affected?
Anna: There is one source pointed out something quite stark around 17% of adults worldwide. That's like 1.4 billion people were unbanked as of 2024.
Tom: Wow. 1.4 billion.
Anna: Yeah. So by using these alternative data sources, which AI makes feasible, credit unions have this huge opportunity to expand access to credit, [00:03:00] potentially serving many more people responsibly.
Tom: That's a massive potential impact. Okay. So AI helps understand individuals better. Yeah. What about spotting, uh, wider opportunities within the community? How does that work?
Anna: AI is really good at finding patterns in large data sets. The sources explain that credit unions can use it to compare their current membership against data for the whole community
Tom: to see who they're not reaching
Anna: precisely.
It can reveal demographic groups or even geographic areas that are underserved where people might need credit but aren't currently getting it from the credit union.
Tom: So it's about finding unmet needs.
Anna: Exactly, and the material suggests this isn't just good for growth reaching new markets, it's also about more equitable lending, making sure financial services are accessible more broadly.
Tom: That makes sense. People respond better when things feel relevant to them.
Anna: Absolutely. And there's data backing that up too. A 2023 TransUnion survey we saw found that 76%. A huge majority of consumers would consider switching financial institutions just to get more personalized products or services.
Tom: 76%.
That's [00:04:00] a, yeah. Yeah. That's a powerful motivator for credit unions to personalize things.
Anna: It really is.
Tom: So linking that to lending. How does AI help make the actual outreach, the marketing campaigns more effective beyond just knowing who to target?
Anna: Right? The material talks about predictive segmentation. So instead of just sending out, you know, a blanket email about auto loans,
Tom: which probably annoys more people than it attracts,
Anna: probably AI helps predict which members are most likely to need a specific loan.
Right now, it allows for really tailored campaigns.
Tom: Can you give an example from the sources?
Anna: Sure. Like, uh, AI could identify a member whose car lease is ending soon and automatically trigger a relevant, timely auto loan offer.
Tom: Okay, that's smart.
Anna: Or maybe identify members likely to have back to school expenses coming up and offer a line of credit.
It's about anticipating needs based on data patterns
Tom: much better than a generic flyer. And does it actually work? Do the sources show results?
Anna: They do. The [00:05:00] indication is that this kind of personalized outreach can lead to a pretty significant jump in loan application response rates somewhere in the range of 20 to 30% higher,
Tom: 20 to 30%.
That's substantial.
Anna: It definitely is.
Tom: Now, these benefits sound great, but uh, it's important to stress. AI isn't taking over completely. Right. It's not replacing the human element.
Anna: That's a really crucial point. The sources emphasize. AI in London isn't meant to replace underwriters or relationship managers, or importantly ethical judgment.
So
Tom: it's more of a tool.
Anna: Exactly. The successful approach seems to be what's called human guided ai. The credit union teams stay in control, they make the final decisions, they maintain the member relationships. AI just provides them with, um. Enhanced insights, better information to work with.
Tom: So it augments human expertise rather than replacing it
Anna: precisely.
And the sources are clear. Making AI work well requires a solid strategy collaboration across different teams in the credit union, and a commitment to, you know, continuously learning and [00:06:00] refining how the AI is used.
Tom: Okay. So let's recap then, based on everything we've looked at, what are the main takeaways?
Mm-hmm. The key advantages for credit unions using AI and lending.
Anna: The material points to a few core benefits. First, potentially expanding lending without necessarily increasing risk because the Ians are better informed.
Right?
Anna: Second, reducing some of the inequities in loan access. Reaching those underserved groups.
We talked about uhhuh third, reaching new markets more confidently. And fourth, personalizing outreach to boost engagement and ultimately build stronger member relationships.
Tom: That sounds like a compelling package now for credit unions listening who think this sounds interesting, but maybe intimidating.
How does a company like BlastPoint fit in? How do they help make this practical?
Anna: Good question. BlastPoint, based on the sources, offers a platform designed to help credit unions leverage AI without having to, you know. Rip out and replace all their existing systems. It adds an insight layer,
Tom: an insight layer.
Tell me more about the features the source has [00:07:00] highlighted.
Anna: Okay, so there's member intelligence. This involves analyzing member data, financial behavior, demographics, engagement patterns to identify who the best potential borrowers are for specific products,
Tom: finding the right people within their existing base.
Anna: Exactly. Then there's community level opportunity mapping. This uses broader community data to help the credit union see where the growth opportunities are geographically or within certain demographic segments they might be missing.
Tom: Helps with strategic planning,
Anna: right? The platform also provides channel preference insights, basically figuring out the best way to actually communicate with different member segments.
Email, mail, phone, online ads.
Tom: So the message actually gets seen.
Anna: Uhhuh. And finally, campaign Performance Insights. This helps create the targeted messaging for campaigns and then tracks what's working so they can refine audiences and messages over time for better results. It optimizes the whole process,
Tom: so it integrates with what they already do.
But makes it smarter.
Anna: That's the idea. Presented in the material. Yes. An insight layer to [00:08:00] optimize existing tools.
Tom: Did the sources give a real world example, a case study perhaps?
Anna: Yes, there was. One mentioned a Midwest Credit Union that worked with BlastPoint. Their goal was pretty straightforward. Increase membership, but instead of just generic marketing,
Tom: they use data.
Anna: They use the platform to really understand the communities they served, identify specific neighborhoods with high potential, understand the financial behaviors there, and then tailor their outreach.
Tom: And what happened? What were the results?
Anna: The results cited were pretty impressive. They achieved a 4% increase in overall membership,
Tom: which is good compared to the average.
Anna: Yeah, the source mentioned the industry average growth was around 2% at the time, so double the average and beyond membership, they generated, uh, $16 million in new deposits and $10 million in new loans.
Tom: Wow. So $26 million in new business altogether.
Anna: That's what the source indicated. It really shows the tangible impact this kind of data-driven, AI powered approach can have.
Tom: That's a strong [00:09:00] example. Yeah. Is there more detail available on that?
Anna: Yes. For listeners interested the source material mentioned a case study. It's titled, driving $26 in New Business, a 4% membership surge powered by data worth looking up if you want the specifics.
Tom: Good to know. Okay, so as we start to wrap up this deep dive, the takeaway seems pretty clear.
AI holds significant potential to really transform credit union lending.
Anna: It really does. It's about unlocking the insights that are often hiding in the data they already have, looking beyond the standard metrics,
Tom: and it offers a path for credit unions to grow smartly, reach more people and do it all while staying true to their community focused mission.
A way to blend, you know, technology with integrity.
Anna: Well said. And if any credit unions listening are curious about exploring this further for themselves,
Tom: what should they do?
Anna: The suggestion is to reach out. BlastPoint encourages inquiries@infoatblastpoint.com to discuss specific needs and how AI driven strategies might help.
Tom: And of course, if you enjoyed this deep dive into data and ai, [00:10:00] make sure to subscribe to the Blast Point Deep Dive podcast for more amazing stories from the cutting edge of technology. We'll be back soon with another episode exploring how data and AI are shaping the world around us.
Anna: Until then, keep exploring, keep learning, and keep pushing the boundaries of what's possible.
We'll be here to guide you every step of the way.
Tom: Thanks for joining us.
