Enabling credit unions with AI

On February 1, a panel discussion was conducted addressing the topic of how credit unions can empower themselves with AI and be successful in their journey, hosted by Neuton.AI. It’s extremely vital for credit unions to steer corporate governance in the right direction to initiate innovations so seasoned industry experts gathered to raise the topic of successful innovation-based management and discuss the exact steps that credit unions should take on the way to building a truly data-driven culture.

The panel discussion brought together Miranda Flury, President and Consultant at Hawkeye Strategies, Anne Legg, Founder of Thrive and Author of Big Data/Big Climb, Fraser Uitdenbosch, Prior Director of Enterprise Intelligence at First West Credit Union, Michael Lawson, Creator at CUbroadcast, Blair Newman, CTO at Neuton.AI, and Andrew Gludt, Partnership Director at Neuton.AI.

What are the main obstacles on the way to innovations?

After a brief introduction of all speakers by Andrew Gludt, Michael and Blair proceeded with a brief fireside chat discussing the latest takeaways from a survey conducted by CULytics. The results showed that nearly 50% of credit unions had difficulty finding talent to implement their AI initiatives. Plus, nearly 50% indicated that they would do the initiative internally while the remaining 50% would have it completed by third parties.

Blair noted that finding resources has always been a common challenge for credit unions as they mistakenly think they should be looking for data scientists or other additional talent to implement AI. “These poll results indicate that every credit union is at a different point of its journey as it relates to data. That’s why some of them would like to have the advantage of someone’s guidance in forward-leaning technologies, such as machine learning.” — recapped Blair.

In practice, AI-based automation can be of great help in preventing mundane tasks and allowing credit unions to focus on their core competencies. Instead of spending extensive time on building machine learning solutions and facing typical headwinds, such as infrastructure development or coding, credit unions can use automation tools and focus on understanding and putting their data in action. And the best part, many of such tools don’t require any deep technical knowledge which eliminates the search for additional talent as well.

How to foster not only a strategic yet data-driven culture?

Speaking of the culture types within credit unions, Miranda Flury started by defining a strategic type, “Strategic culture requires that all individuals are good strategic thinkers and think critically on behalf of the organization. Strategic culture is a combination of individual competency, board dynamics, and systematic processes that are done routinely and consistently.” — highlighted by Miranda.

She also mentioned some steps that should be taken on each level to move towards a data-driven culture:

Individual Competency: At this point, individuals need to understand at what stage of the data journey their credit union stands and to really educate themselves on the importance of data, study use cases and possible pitfalls.

Board Dynamics: The leadership on the Board has to reinforce and support the need for data-driven decisions. It’s important to be receptive and well-balanced, but avoid the occurrence of analysis paralysis.

Systematic Processes: To make sound decisions, it’s vital to make sure that you have the right data and the right amount of data as well as to regularly bring outside expertise.

Additionally, Miranda emphasized that AI is not a strategy, but an enabler. As such, the ability to anticipate members’ needs is a huge competitive advantage for credit unions as it helps to build relationships based on trust.

How to bring the credit union’s data into action?

According to Anne Legg, credit unions can implement AI and get positive outcomes only if it starts as a strategic initiative. “If you don’t know what your business problems are, how do you possibly create a framework to land on what you are trying to achieve? You have to think about what’s going to be your focus: what you want and what business problem you want to solve. In a credit union, it’s usually a member problem to deal with. If you solve a member problem leveraging data and turning it to positive ROI, you’ll solve your organization’s problem.”

Fraser also elaborated on the importance of detecting urgent issues within your credit union, and shared a good example of the proactive approach to solving a problem within his credit union:

“Throughout the pandemic, a lot of credit unions were concerned about whether their members would be able to pay loans or not. We started to think what was the type of information that would help us identify those members who would likely face trouble paying their loans. We used AI to build a model for retail members and for business members, trying to predict which ones needed a deferral. That helped our organization to understand the risk for our balance sheet. Our research also showed that our members were uncomfortable about asking the credit union for help. Since the model was so accurate at identifying members who needed help, we proactively reached out to those members and informed them that our credit union was there to help them get through that challenging period.”

In regards to building digital and AI capabilities, Anne fairly claimed that it’s not only a strategy but a solid action plan that matters. Plus, credit unions should have the “test and learn” environment to help cultivate innovations. “How to handle the new information and insights? Be curious and be able to communicate the value of your data.”

On the final note

The panel discussion once again highlighted that data can become an essential source of power if used appropriately. In this regard, instead of making decisions by intuition, credit unions should have a clear understanding of the value of their data and leverage it proactively to address members’ problems and let them feel that a credit union really cares about their wellbeing.

The full recording of the panel discussion: https://youtu.be/JdMpA2yBFv0

Alex Miller

Alex Miller

Alex Miller is the Director of Data Science and Engineering at Neuton.ai. Since his university years, Alex has been passionate about AI technologies and their enormous potential to make ... Web: https://neuton.ai Details