Leaders and business intelligence at credit unions are putting a tremendous focus on ways to use advanced data analytics to identify trends, detect patterns and glean other valuable findings from the sea of information available to them. Without question, member data is valuable. But the greatest value lies in the ability to empower each line of business to achieve strategic initiatives and performance goals. When this empowerment is coupled with improving member service, a proven, repeatable best practice results.
OnApproach CEO, Paul Ablack, and Mike Lawson of CUbroadcast caught up at the NACUSO Network Conference in Orlando to discuss credit union industry trends and the upcoming 2017 AXFI Conference.
The interview covers credit union collaboration, fintech disuption, enterprise data integration, predictive analytics, data pooling, member experience, Identity and other exciting topics relevant to today's financial services industry.
Data continues to prove itself as a necessity for decision-making in financial institutions. For years, major banks and innovative companies such as Google and Amazon have taken advantage of “Big Data” to gain better insights into their customer base and make business decisions to position themselves for the future. The credit union industry is finally beginning to take advantage of their data and utilize new technologies. However, credit unions are much smaller than major banks and simply don’t have the same quantity of data that banks are able to collect from their customers. Fortunately, data pooling serves as a great solution to this problem. Here are 5 reasons your credit union should participate in data pooling:
Just as healthcare is developing robust analytics for patients, credit unions have a great opportunity to empower members to track their financial health and take actions to improve it.
Being raised in a small town, I never thought about the healthcare I received. I had the same doctor from birth until I moved to college. As long as nothing seemed wrong to him, I felt confident that I was healthy. However, when I moved to a bigger city, everything changed. I was no longer able to rely on my hometown doctor, and I needed a way to monitor and maintain my health. At the same time, the healthcare industry was going through a data revolution. The traditional relationships between doctors and patients were changed forever. In shopping for my new healthcare provider, I felt the most comfortable with the one that had the best analytics and enabled me to make data-driven decisions to improve my health.
In the fifth Data Analytics Series BIGcast, Sorting Socks: A Data Automation Conversation with Graham Goble, John Best speaks with Graham Goble of BankBI about financial performance management, reporting, and business intelligence.
The Excel Curse
One primary point of discussion during the podcast is about the use of spreadsheets in comparison to data automation. “The Excel Curse” is certainly not unique to credit unions, but it is absolutely a problem across the industry that requires action. Excel is a powerful tool and serves a number of purposes very well, but advanced analytics for financial institutions is not one of them. Even for a spreadsheet guru, there a several fatal flaws in using such a software as a primary reporting tool in credit unions:
In my previous blog, Big Data vs. Little Data: Part 1 - Structured and Unstructured Data, I discussed the two main types of data that should be top of mind for any organization thinking of becoming truly “Data-Driven.” In the world of data and data analytics, credit unions must leverage ALL the data accessible to them but the journey of mastering data analytics can be very tricky.
Determining the right tools will be critical. When it comes to data and data analytics, the order in which you introduce new tools is extremely important. In order to make each step up the analytics curve effective as the last, credit unions must consider the following steps:
As credit unions begin their journey into the future, they must rely on an industry standard analytics platform to guide them to their destinations.
Google Maps has revolutionized how we navigate our lives. It saves us from headaches caused by unnecessary traffic and other challenges in traveling. My journey from work to home has many different routes depending on traffic patterns. During days with slower traffic (i.e. - winter snowstorms), the Google Maps recommended route will change every 5 – 10 minutes. Using an analytics engine that informs me of the best route allows me to spend extra time on more important things in life. Credit unions have a similar opportunity when navigating their institutions into the uncertain future of financial services. Establishing an industry standard analytics platform will enable credit unions to cooperate on analytics and guide them to their desired destinations.
In the third Data Analytics Series BIGcast, The CECL Effect, John Best speaks with Dr. Joseph Breeden of Deep Future Analytics about CECL, data pooling, and predictive analytics.
The Impact of CECL
As Joe Breeden explains in the podcast, CECL stands for Current Expected Credit Loss, and is the new accounting standard for how financial institutions will set loss reserves. Typically, organizations under $10 Billion assets have utilized moving averages to calculate loss reserves, but this model is backward-looking and will not be acceptable for the new regulations. A moving average model will always set your loss reserves too low moving into a recession and too high moving out of the recession.
When discussing how to meet CECL requirements and create a forward-looking model, Breeden states, “There is a lot of flexibility on how you implement it, but there are two things that are pretty much unavoidable”:
Credit unions interested in advancing their data analytics efforts will find a wealth of information in a recent article in the McKinsey Quarterly. Simply entitled, “Making Data Analytics Work for You – Instead of the Other Way Around” (Mayhew, Salah, and Williams), the article provides an easy to follow list of steps for any organization to get the most out of their investment in data analytics.
The authors emphasize that improving corporate performance is the only meaningful reason for organizations to pursue data analytics. As a result, they state two important principles:
As 2016 draws to a close, it is time to look back on all that we’ve learned, and apply it to better our organizations for 2017. The credit union industry is rapidly changing as financial institutions are gaining better understandings of the necessity to optimize analytics and become truly data-driven organizations. As 2016 comes to a close, I have taken the liberty of compiling some of the industry’s favorite Big Data & Analytics related articles (and some you may have missed) from OnApproach’s blog, The Decision Maker. This is part two of two and features the top 5 articles you may have missed in 2016. Click here to see Part One highlighting the most popular blogs of the year. Enjoy!