It’s no secret that regional lenders need to increase their profitability, advance their competitive positions and improve their customer experience. Incorporating artificial intelligence into their decision-making enables them to better compete with larger financial institutions. Unfortunately, it’s a challenge more easily articulated than solved.
When discussing the subject with credit unions, I usually hear some variation of the following:
- “We don’t need machine learning — our key differentiator is the human element.”
- “There are too many gaps in our data — and what we have is often unreliable.”
- “We’re low on analytic and IT resources.”
- “Your solutions are too expensive or complicated for what we need!”
Let’s first acknowledge that the “status quo” is no longer an option, if indeed it ever was. Innovation in today’s financial services industry, whether it’s the advent of e-transfers, the transition from human advisors to app-based investment or the push to free online banking, is driven not by banks but
A survey from Accenture found that
Fortunately, credit unions have a weapon up their sleeves their larger counterparts don’t: the human element – or, in business terms, better customer service.
It will probably not surprise you to know that banks aren’t known for providing great customer service. That trend isn’t based on my personal experiences, which have been nothing but positive. The industry is near the bottom of
And if the best an industry’s leaders can say about their customer service is that they outperform another industry liked by only
To be fair to banks, their efforts to improve their customer experiences are paying off. A 2019 survey by
But even the most highly regarded banks can’t compare to credit unions, which received an average rating of 4.5 out of 5 in the same survey. This is as ideal an example as you’re likely to find for small organizations outshining and outperforming their larger, better-funded competitors. The question, then, is how credit unions can harness their superior service to their best advantage.
Contrary to popular belief, affordable, scalable, machine learning-powered analytic tools exist that credit unions can use to improve their origination rates and digital services — and they are available for a small up-front investment.
The credit union-facing tools are as capable of integrating with a smaller organization’s loan origination system as with larger one. This gives credit unions access to the same machine-learning powered analytics models enjoyed by their larger competitors and at a competitive price.
The benefits can include:
- Improved risk management, thanks to analytics that combine risk scoring models with a credit union’s individual policy rules;
- Improved decision-making, powered by machine learning, often leading to increased application volume capacity and a reduction in manual reviews;
- Increased profitability, thanks to a reduction in delinquencies and bad debt; and
- Faster time-to-value, with credit unions now capable of implementing new credit programs in days, rather than months.
Most importantly, integrating analytics into their operations allows credit unions to improve their customer experience by helping them deliver personalized financial services anytime and from any device, polishing the aspect of their business that already shines brightest.
By using analytics to enforce, automate and accommodate member services, credit unions can deliver a digital experience equal to the most cutting-edge banks and fintechs, freeing their staff to focus on what they already do best: making human connections with customers and members.
