Only 9% of millennials say credit unions are their primary financial institution, compared with 74% of millennials who are customers of major banks. Perception explains much of this trend. In particular, young people may believe they can only access mobile banking and payment experiences at the big banks, thanks in large part to targeted marketing efforts surrounding these services. Building a reputation for innovative financial experiences and exemplary customer service will be essential to credit unions as we move forward.
Paradoxically, this offers credit unions exciting opportunities. Providing members with products and guidance as they move through their financial lives - from buying homes to saving and investing - lines up perfectly with credit unions' mission.
There is a sense of urgency around the adoption of the best (latest) software available and credit unions are leveraging the aggregation power of the internet and decisioning power of AI for the first time to grow their member base beyond their existing footprint.
We see the following specific opportunities as credit unions adopt AI:
Smarter loan decisions, more ‘yes’ to members:
AI-powered loan decisions unlock the ability for credit unions to offer new products and services to their members. That's because AI can quickly analyze all the credit bureau data and marry it with alternative data from sources like LexisNexis and MicroBilt. This will allow credit unions to approve borrowers more frequently and faster. Typically, 20-40% more members can be approved.
Product bundling:
The use of AI-based decisioning platforms can provide nuanced insights into members and can provide signals on which members will be best suited for an additional product. For instance, credit unions can bundle a card offer with an auto loan, creating a stickier, more compelling offer.
Nuanced reviews and engagement:
This allows credit unions to better interact with and grow their share of wallet among existing members. The AI decisioning platform can automate 100% of loan decisions. That leaves the credit unions with the time to focus on the underserved members or those who would have otherwise been declined after using the traditional underwriting methods. For instance, a member applying for an expensive car can be approved for a less expensive model without straightaway being rejected.
Upselling:
Many credit unions would like to offer additional products to their members but lack the technology to do so. By using AI, credit unions can design products that will have high adoption rates and improve member financial well-being.
In the short term, these opportunities will help credit unions build stronger relationships with their members and capture a greater share of wallet for their financial products. In the longer run, though, the potential could be even more impressive: imagine a scenario where credit unions became the new neo banks of affinity groups.
With the right AI partners to find and serve their membership, it’s entirely possible.
Scienaptic is one such partner to look at. Scienaptic is on a mission to increase credit availability by transforming technology used in credit decisioning. With over 70 clients, Scienaptic’s AI is powering 7 million annual credit decisions. Credit unions using Scienaptic’s AI are seeing 40% more approvals and massive lifts in instant decisioning rates. While Scienaptic AI’s system can help boost the number of loan approvals without increasing risk, it can also catch riskier loans that may have otherwise been approved.
Measuring Success and looking forward:
Pankaj Jain, Cofounder, and President of Scienaptic explained in a recent interview on how they meticulously measure success of their credit union clients. “After the contract is signed, there is an onboarding process, which takes six to eight weeks,” said Jain. “We bring in a lot of quick-start models for each product, but we also ask the institution to provide us with two or three years of loan application data, as well as performance data on approved loans.” Scienaptic AI then runs that data through its system, which allows the institution to compare actual results to projected Scienaptic AI results. In other words, the credit union has a good idea of what to expect before it ever goes live on the system.
“Assuming there haven’t been major changes in the product mix or member profile, they’re going to know what to expect in terms of business performance before they ever go live,” said Jain. “At one recent credit union, we saw approvals jump from 61% to 81% overnight.”
According to Jain, loan decisioning is changing for the better, and credit unions that do not adopt that change put themselves at risk. “If credit unions hope to compete with big banks and fintechs, they need to get better at what they do. They can either push forward with new technology or be left behind,” he concluded.
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