This blog is based on an expert discussion at our Summer 2022 Brainstorm Event. Register to attend our next one now!
Credit unions have been historically slow to react to technological innovations, but that could change with data. Data analytics can help to monitor, operate, and enhance the member experience.
That is, if they can access all the right data, enough of it, and then use it to create a roadmap that leads to implementing real change across the organizations. Outdated technology stacks, decentralized data, and a lack of resources could be difficult hurdles to overcome as financial institutions start down the road to analytics.
Experts from TerraStrat, OM Financial Group, Trellance, and Datava join facilitator Chris Otey, CRO of CU 2.0, on the panel at the CU 2.0 Brainstorm Event to consider solutions and issues they foresee as credit unions begin looking ahead to a world where data analytics are an integral part of the business.
Watch the video below to catch the whole thing, or read on for a short summary.
Changing Culture Around Data within the Credit Union Industry
Karan Bhalla, CGO at Trellance opens up the discussion with a concern: how do credit unions determine what tools and services are most applicable when looking at analytics provided by fintechs? Credit unions are often lacking in resources and need to know how to make the biggest impact with the smallest investment.
In the long run, everyone is going to need a data warehouse regardless, but in the meantime, are you able to utilize just enough of what you are investing in to see a return?
Kirk Kordeleski, Partner at OM Financial Group, says the dynamics of the credit union are dramatically different than for-profit institutions. Without stock, there hasn’t been a culture of using data and analytics driving strategy and goal setting within the industry. Now that the culture is changing, it’s difficult to catch up and use the data effectively, and credit unions need to get an urgent roadmap planned for the next several years. Institutions need to be utilizing data as decision making criteria and talking about it as often as possible, while implementing new technology at the same time, to ensure that the system is centered on decision making rather than simply reporting.
CEO of Datava, Gordon Flammer, outlines a difference between research and execution. Credit unions require both sufficient data and analytics to be effective in impacting member experience, as using bad data can worsen the member experience. Regardless, if you aren’t implementing positive changes based on good data, the whole system is rendered useless, especially where training high-turnover staff is concerned.
Ray Wachauf, CEO at TerraStrat agrees with Flammer, stating that companies should be creating routines around data, assessing for viability, and understanding how to treat the data. Stable models where data can speak for itself take a while to achieve, but careful application of solid data science and getting people with experience is the biggest hurdle.
The Age of AI and New Analytical Expectations
It’s necessary to understand how members are interacting with interfaces throughout the member experience, as Wauchauf points out. Finding out how they are using the credit union, what is working for them, and where to improve the experience to make the biggest impact starts by asking questions that lead to data points:
- How are they using online banking and mobile apps?
- Where are you losing members in the experience?
- What can you gain from that information?
He points out that not many institutions are capturing the data and using it in a meaningful way.
For Flammer, it brings to mind the difference between the “Terminator” generation and the new AI-friendly generation: now, consumers want predictive analytics to suggest relevant products and services, whereas older generations were far more wary of data being tracked and recorded. We can now use it to identify opportunities to develop new or better products and services for members to improve their experiences.
The most improvement comes from using data scientists and engineers to look at the data and determine if you have enough to make decisions and the predictions they are making, according to Kordeleski. Institutions aren’t currently using the data effectively as a tool to improve services.
Further supporting the point that Flammer made about the differences between the generations, Bhalla highlights a shift in expectations. Younger people are not only more comfortable with their data being used, they now want you to know their needs and desires ahead of time, shifting interactions from real-time to being as proactive as possible. To get there, credit unions have to use data faster, whereas they’re currently behind the curve. The challenge, it seems, is to help credit unions get ahead to actually use the data in a timely manner.
To Kordeleski a cultural transformation and strategic prioritization is critical. Embracing data in strategy and decision making can benefit credit unions, but it needs to be implemented efficiently and urgently.
Centralizing Data to Get an Accurate Picture
How to help consult to develop that roadmap in a way that will happen quickly that will turn over short term gains and long term gain competitive advantage
Skillset and budget has to be significant to make it happen, well, with outside expertise. Area needs to be centralized, not dispensed to a lot of people part time, cultural and needs to be organized correctly in order to do it well.
Flammer urges credit unions and their tech partners to start building models now. Any model can be improved, so perfection doesn’t matter when it comes to getting something in production. There are three key areas to focus on in production:
- Collecting and curating the data
- Analytics
- Creating good models and actually utilizing them
Collecting and curating the data seems to be an industry-wide challenge that needs to be addressed before implementing strategies in order to get an accurate picture of the member experience. As Wauchauf points out, most CUs don’t have integrated cores and the data isn’t interacting across all products and channels.
Both Kordeleski and Wauchauf back this up, offering their own experiences working with credit unions as further proof that something needs to be done to integrate data. Most credit unions use upwards of 30 different databases to store sets of data, and people within the same department store data in different places.
Flammer further solidifies the idea that data consolidation is important, stating that credit unions need as much useful curated data as possible, because one data point could mean the difference between predicting whether you will retain or lose members.
Further Discussion
The panel opens up the floor for audience members to contribute to the conversation. It’s mentioned that credit unions have a lot to benefit from data being housed collectively and then going to AI applications. Partnering may be a more viable option to institutions with limited resources, leveraging the power of credit unions to share data may be where the future is headed.
Joe Keller from Visions FCU reinforces this point from the audience, expressing that he also believes that the collaborative use of data is a largely untapped resource, as credit unions typically have more data than most financial institutions.
The consensus is that the challenge that may be the most worth overcoming is creating a collaborative environment within the industry, allowing credit unions to partner together to solve common problems and garner quick wins for their members rather than waiting around for startups and fintechs to do it for them.
