Analytics, Data Science, Financial Services, Growth Opportunities, Predictive solutions

How Credit Unions Get Ahead of Competition with Predictive Analytics

Donna Sanchez

Predictive analytics can help provide hidden information by identifying patterns in data that would otherwise go undetected by human beings.

According to CUNA Strategic Services, the credit union industry has lagged in adopting predictive technology. Predictive analytics is only being used by 12 percent of credit unions vs. 80 percent of banks, according to the study. The report also discovered that credit unions are not using member data to their greatest advantage.

Yet, most credit unions are aware of the benefits of using predictive analytics, but haven’t taken action. The ability to predict future behavior is invaluable in helping credit unions make better decisions about their lending, marketing, and member service strategies. However, most credit unions are struggling to implement predictive analytics in their daily operations.

Why? They don’t yet know how without incurring execution risk.

A report from CU Direct and the Credit Union National Association (CUNA) finds that only a small percentage of credit unions have implemented predictive analytics.

While most credit unions have invested in their core systems, they are still struggling to fully leverage their data assets. Especially when it comes to using data science tools.

Most credit unions report they don’t have the resources or manpower to build predictive analytics models, which means they might be missing out on ways to serve members better and grow their business.

This is because they are considering enterprise-class tools that require an IT support team and data scientist team to implement. This is beyond the operational scope of most credit unions.

Now, there are new options that don’t require these additional human resources.

 

New Benefits and New Challenges 

New management skills come into play when bringing on predictive analytics.

Data quality: Predictive analytics requires a high-quality data set with complete information on all members and loans. If any part of this information is missing or inaccurate, it could result in poor predictions or incorrect decisions being made based on the model.

Complexity: There is a learning curve associated with building models and integrating data silos into one cohesive system. It takes time for everyone involved to understand how these systems work together, how they should be used and what kinds of results they can expect from them. The good news: this is a skill that lasts one’s career.

Business knowledge: Technical knowledge is more powerful if paired with business acumen. Data scientists rarely understand how those numbers affect real-world situations.

Timely: Prediction models take time to produce results. It must be built fast enough so that they’re relevant at the moment they’re needed most.

 

New Tools Fit Credit Union Requirements 

The widespread adoption of predictive analytics has taken longer than expected among credit unions, but many experts expect that trend to change soon because there’s an increasing need for better decision-making and more accurate predictions about what will happen next with customers’ spending patterns and interest rates. 

New predictive analytics tools now meet credit union requirements for relevant reports without requiring a staff of IT and data scientists. Your current analysts can do it with some additional training.

Introducing NEMESIS.

 

NEMESIS, an A.I. predictive analytics platform features:

  • Automated ingestion and cleansing of data into the system gears analysts up for stellar data modeling.
  • Drag-and-drop options allow users to build out a wide range of tailored models, without coding, equipped with side-by-side model comparisons.
  • Reduces the gap between the technical team and business units. Non-technical team members can create and run reports, and build models without relying on a data scientist, model builder, or analyst.
  • Data visualization tool translating the model results into a crystal-clear business language.
  • End-to-end control of the analytic process so that you can get decision support at the speed of thought.

 

“There is a great opportunity for credit unions to use the data they have collected over the years,” said Michael Collins, vice president of marketing at CUNA Strategic Services. “If they don’t do it now, they will be left behind.”

Learn more about NEMESIS and schedule a conversation to see how it can help you predict your future.

Donna Sanchez

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