The Decision Maker

A Clean Room, a Pool, and Actionable Information from Credit Union Data

Posted by Mark Portz on Jan 10, 2017 11:01:00 AM

A Clean Room, A (Data) Pool, and Actionable Information from Credit Union Data

In the second episode of the BIGcast Data Analytics Series, John Best speaks with Blesson Abraham of SavvyIntel about credit union analytics insights, data pooling and optimization. The financial services industry is facing immense pressures, and credit unions cannot rely on the intuition or “how it has always been done” any longer. As John and Blesson discuss, utilizing big data and advanced analytics is no longer an option, but a necessity for credit unions.

You Have to Clean Up your Room

Analytics is a major point of discussion these days for credit unions. However, less frequently discussed, is the organization of the data required to perform analytics. Companies such as SavvyIntel have created incredible models which can be utilized at all credit unions. However, no model is ever going to work properly and provide truly actionable insights if the data isn’t clean and validated.

Credit unions have to be able to easily answer questions such as, “Which payment date is correct?”, and “How many members do we have?” Unfortunately, it is very common that credit unions attempt to maintain a number of different reporting systems for different departments, which creates silos and varying results. Credit unions need to have clean data and a “single source of truth” to provide truly actionable analytics enterprise-wide.

As Blesson expands on this idea, another way to look at a single source of truth is the foundation of a house. The foundation, or the single source of truth, should be an enterprise data warehouse with consistent reporting and data definitions for all departments across the organization. Without this foundation, the house sinks and tips.

What can Credit Unions do with this Clean Data?

Credit unions today should really be using data to drive all business decisions. One example Blesson brought up in the podcast is related to predicting the likeliness that specific members will leave the credit union.

To do so, SavvyIntel has built a model that takes all kinds of member interactions into account, such as which products they have, how they are using them, trends in how their interactions are changing, and more. By cross-referencing this data with demographic data, SavvyIntel is able to uncover very valuable insights, which indicate the risk of each member leaving the credit union.

However, this type of analytics requires very in-depth data. It is necessary to utilize data broken down to the transaction level, and must be updated daily. This is the only way to analyze behavioral trends, and react to the trend before the member has already closed his/her accounts. Traditional marketing tools don’t offer this type of insight. If you wait for a report from an MCIF system for month-old data on a product-level, it is already outdated, can’t be utilized for behavioral trends, and the member has likely already left.

If profitable members are being flagged in this model as high risks of leaving, the credit union now knows who to target with valuable and desirable opportunities to encourage to them stay. Now is the time to foster, maintain and grow these relationships. Utilizing member data, the credit union should also be able to figure out, for example, which stage of life the member is in, how to best serve that member, and encourage him/her to maintain the membership.

Data Pooling to Push the Credit Union Movement Forward

A great thing about the credit union movement is the opportunity to work together. However, collaboration in this industry is no longer an opportunity, but a necessity. Credit unions need to “look outside of their four walls”. Even larger credit unions with $2 billion+ in assets lack data to perform accurate analytics. Data pooling is the solution to allow all credit unions to grow and compete with the changing financial services industry. Data pooling will help the entire credit union movement to make better models and, ultimately, business decisions.

To listen to the entire podcast, follow this link: http://bigfintechmedia.com/Podcast/data-rich-and-insight-poor

Check out the entire podcast now!

To better understand how this topic affects member experience, check out John Best’s recent podcast with Anne Legg of Thrive Strategic Services: http://bigfintechmedia.com/Podcast/curbside-service-with-anne-legg

 

Topics: Data Pool, Podcast, BIGcast

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