When Todd Lindemann joined Redwood Credit Union about a decade ago, the institution did not have a good handle on its data. Like many banks and credit unions, data was trapped in silos and data governance was iffy.
For the California-based institution, nirvana was establishing one-on-one relationships with its 400,000 members. Sure, artificial intelligence (AI) is key to understanding customers, but Redwood ignored the hype surrounding AI and chose to first focus on a more boring, less sexy project: data stewardship.
“We knew we were terrible with data, but we knew what we wanted to accomplish,” Lindemann — Senior Vice President of Payments — explained during a panel discussion hosted by Neuton.AI. “We knew that the only way we could anticipate member needs was with data coupled with member feedback.”
“We needed to get our data house in order,” he said, adding, “Redwood is extremely good at executing strategy in the right order.”
So, the credit union, with total assets of $7.2 billion, took its first small step: building a data dictionary. Lots of small, measured steps later, Redwood has an enviable ability to engage one-on-one with its members as well as predict what they need — maybe even before they realize it.
Read More: Six AI and Big Data Trends in Banking
Explore the three keys to improving your digital experience and accelerating customer and business adoption: tokenization, digital onboarding, and a unified customer experience.
This webinar will show how to develop marketing strategies that will generate new checking account volume.
First Step Along the AI Road
More than 80% of financial institutions believe that AI is the key driver to competitive success, according to a survey by NTT Data. At the same time, the executives identified using AI to provide customized advice that attracts and retains customers as their biggest challenge.
Creating a roadmap and then executing on that roadmap is an area in which many financial institutions struggle, says Naveen Jain, Founder, CULytics, another panelist. He explains that the best approach is to clearly define your data strategy, including how you will tie your data initiatives back to KPIs to measure progress and how you will help the executive team visualize how these initiatives will further organizational objectives.
Don’t let your institution’s size stop you. Redwood is a large credit union, but Blair Newman, CTO, Neuton.AI. points out that “You don’t have to have a robust IT department with data scientists to take advantage of AI and predictive analytics.” You do need to adopt new ways of thinking, however.
Getting data right is a critical first step in AI initiatives, agrees panelist Jay Lauer, Senior Innovation Strategist, PSCU. “Eliminate data silos, shine a light on dark data, and adopt data stewardship principles,” he advises.
A slow-and-steady approach to AI not only builds confidence in the data, but addresses one of the biggest challenges that banks and credit unions face: Creating the business case to secure funding.
“Once you know what data you have and what your goals are, you can assemble specific use cases to support your overall business case for data-driven initiatives,” Lauer states.
- Building an Intelligent Bank is No Longer Optional
- 4 Data Analytics Tips Help Financial Institutions Match Consumer Needs
What Redwood Is Using AI for Now and What’s Up Next
Because of its initial groundwork, Redwood Credit Union is well ahead of its peers in using AI-powered data to turn personalization into action. For instance, the institution’s data models can predict when members might need a one-time increase in their ATM withdrawal limit, perhaps due to an upcoming vacation or life event. The institution offers customers an ATM limit increase of up to $10,000, an amount unheard of in a risk-averse industry.
Members confirm the bump up on the mobile app, providing two-factor authentication, explains Lindemann. The new limit remains in effect for three days, he says.
Reducing the number of times members hit their withdrawal limit has increased member satisfaction without increasing risk, notes Lindemann.
Redwood’s upcoming AI-based moves include expanding the use of predictive risk models to implement dynamic overdraft limits, creating its own credit scoring model.
Banks and credit unions traditionally lean on FICO scores to approve or decline consumer loans. Redwood plans to use AI to analyze member behavior and include its own credit scores in its lending decisions.
Redwood also plans to incorporate predictive data analytics into collections to identify those members who are at risk of becoming delinquent so the credit union can offer proactive assistance. The model will also identify how likely members will be to repay past due amounts so the credit union can prioritize collections outreach efforts.
Finally, the institution will use AI to better estimate customer lifetime value.
Data and Mobile-First Go Hand in Hand
At the same time it was evaluating its data deficiencies, Redwood committed to a mobile-first strategy that includes everything from lending to account opening to payments. More than half (58%) of its customer are mobile only, according to Lindemann. And that’s a good thing: the credit union has a low branch-to-member ratio so it needs to be digitally efficient, he says.
For instance, when a member loses a credit or debit card, Redwood can issue a digital card in seconds. The institution’s mobile app includes a card manager platform that members can use not only to report lost or stolen cards and receive a digital replacement, but can also freeze their card and dispute charges.
- Banks’ Poor Use of Data Drives Business Customers to Fintechs
- Banking Must Use Real-Time Insight to Improve Customer Experiences
- Machine-Driven Marketing: The Future of Ethical AI and Digital Banking
Start Small and Build from There
Redwood’s approach to starting small is one that other financial institutions should adopt, advises CULytics’ Naveen Jain. “It’s much better to focus on a small number of projects and completely follow-through on those,” says Jain. Jay Lauer agrees, saying, “To overcome inertia, start small and build confidence.”
Too often financial institutions have dozens of data initiatives going on at the same time and the teams end up becoming overwhelmed.
Removing areas of customer friction is a good place to begin, observes panelist Ann Legg, Founder of Thrive and author of “Big Data/Big Climb.” Then identify the data you need and build a roadmap based on getting that data. But this process isn’t just about eliminating friction. “A data roadmap will let you be predictive and proactive in addition to solving for friction,” she states.
It also allows you to give customers “a hug.” “You can truly change customer and member lives in ways that you can’t even see until you connect the data with your mission,” says Legg.
Instead of ‘wait and see,’ change your mindset to ‘watch and be ready,’ Lauer advises. “Innovation doesn’t just come to you. You have to be ready for innovation opportunities by having the technology infrastructure and talents and skills already in place.”
Data In 5 Words or Less
Panelists shared their insights about data, in five words or less — give or take.
“Data is powerful. Use it.” Todd Lindemann, Senior Vice President of Payments, Redwood Credit Union.
“Identify the friction.” Ann Legg, Founder of Thrive.
“Start small, think big, move fast.” Naveen Jain, Founder, CULytics.
“Being informed is data driven.” Jay Lauer, Senior Innovation Strategist, PSCU.