Modern Data Analytics: a Must-Have for Credit Unions

April 24, 2023  

Credit unions are facing increasing pressure to meet the rising expectations of their members. More people are joining credit unions as members, and they are bringing their deposits with them. To remain competitive, credit unions must invest in technology, and data analytics is no exception.

Organizations that have implemented modern data analytics are reaping the benefits. They are able to provide a more personalized member experience, identify potential risks and opportunities, and make more informed business decisions. We will explore some of these case studies later in this article.

Data analytics allows credit unions to better understand their members' needs and preferences. With this information, you can tailor services to meet the specific needs of members.

Yet many credit unions struggle to modernize their data analytics capabilities. This can be due to a lack of resources or expertise, as well as outdated technology and infrastructure.

To remain relevant and competitive in today's market, credit unions must prioritize modernization. This means investing in the necessary technology and expertise to gather, analyze, and act on data insights. By doing so, credit unions can better serve their members and stay ahead of the competition.

In this article, we will look at why you must modernize your data analytics, examples of success from other credit unions, and potential obstacles.

What are some trends that require modern data analytics?

Membership and deposits are increasing–and accelerating

Membership and member deposits are on the rise. Not only is membership increasing, the rate of increase itself is accelerating. 

According to data from CUNA, even before the COVID-19 pandemic membership rates were accelerating. During the pandemic, membership increased by 4.7 million in 2020–the largest annual increase on record.

In 2023, the collapse of SVB and other entities led to general fear of small banks. The largest amount of withdrawals from small banks have occurred, with deposits to large banks and credit unions increasing.

Competitors are investing

Credit unions are investing in data and analytics. No matter what business cases you pursue, they will all require a solid foundation. Many credit unions are dealing with legacy core systems, siloed data, and older technology and processes. Modernizing these is the only way to establish a solid foundation for data analytics.

Expectations of members are increasing

Members are customers of many other businesses. Generally, people’s expectations of customer service and customer experience are rising rapidly. 

One strong use of data is a simple Member 360 profile. Gathering data about members and all their interactions creates a single data set that anyone can reference. 

Customers of any business expect a consistent omnichannel experience. They expect you to have the right info–however they interact, whenever they interact, and with whomever inside your organization.

What are examples of applying data analytics?

What becomes possible–or at least much, much easier–with a solid data analytics foundation in place? 

Improving loan portfolio management

First Tech Federal Credit Union leveraged data analytics to improve its loan portfolio management by implementing a machine learning model. This model analyzed borrower characteristics, credit score, and payment history to determine the probability of default for each borrower. Based on this analysis, First Tech was able to identify potential risks in its portfolio and adjust lending policies accordingly.

For example, First Tech noticed that borrowers with lower credit scores were more likely to default on their loans. To address this issue, the credit union increased its minimum credit score requirement for certain types of loans, resulting in a decrease in delinquency rates.

By using data analytics to monitor and adjust its loan portfolio management, First Tech saw a 10% decrease in delinquency rates, enabling the credit union to offer more loans and improve customer satisfaction.

Member service and satisfaction

Data analytics has become increasingly important for credit unions to improve member service. One great example is how DCU (Digital Federal Credit Union) leverages data to provide personalized service to its members. They analyze member data to gain insights into their financial habits and preferences. This allows them to provide tailored recommendations and solutions to help members achieve their financial goals.

Additionally, DCU uses data to monitor member feedback and complaints. They can quickly identify areas where they need to improve and take action to resolve member issues. As a result of their data-driven approach to member service, DCU has consistently been rated highly in customer satisfaction surveys.

Data analytics can also directly impact a credit union’s ability to identify fraudulent activity, see a complete Member 360 profile, understand the member’s “customer journey” with a credit union, improve investment portfolio management, and improve risk management.

What does modern data analytics look like? 

I will spend some more time on this in an upcoming post. 

The general outline is:

  • A Data warehouse as a centralized place for metrics, with organization-wide definitions and calculations
  • Often, a data lake to simplify ingestion and aid exploratory efforts
  • Ingestion tooling and processes to gather siloed and scattered data from throughout the organization
  • Orchestration tooling and processes to schedule and automate
  • Data governance tooling and process to ensure regulatory compliance and data privacy
  • Automated data quality measurement to ensure the integrity and accuracy of data
  • Business intelligence to enable exploration and self-service
  • Data activation tooling to make metrics and Member 360 data available in other systems like CRM and online banking systems
  • Data operations (aka “DataOps”) for when things go wrong (and they will).
  • Infrastructure management to ensure high availability, reliability, cost control.

What are some obstacles to modern analytics?

Learn how to overcome these obstacles and more with our Guide to Managed Analytics.

Stretched too thin

This can come in many flavors. It often manifests as a lack of specialized expertise in the organization or budget constraints. 

Credit unions often look to improved tooling like Matillion and specialized services such as Datateer’s Managed Analytics.

Unknown risk levels

Financial institutions are among the most highly regulated organizations in the world. Security requirements are high. Compliance with regulations of data privacy and usage restrictions are non-negotiables.

For these reasons, executives and boards have historically been hesitant to even consider cloud-based solutions. However, most modern innovation is in cloud-based solutions. 

Recent years have seen major improvements to unlock approval for credit unions to adopt modern, cloud-based data analytics solutions. Examples are security frameworks from organizations such as Center for Internet Security, and specialized solutions like ALTR. These are established, proven practices for solid data governance in the cloud.

Slow time to value

Traditionally, showing value from large, technical efforts like data analytics takes a long time. But taking months–or even years–before making business impact is not acceptable in today’s business environment. 

Fortunately, newer frameworks and tools are rising, such as the Simpler Analytics framework, Sigma Computing for BI, and dbt for metric calculations. These tools turn everything on its head. Instead of long, technical efforts, they enable shorter iterations and faster time to value.

In a future article, I’ll demonstrate how to apply Simpler Analytics to credit union data analytics.

Data quality issues

Unfortunately, this one won’t go away. Data quality is not a technical concept. Beyond technical concerns like accessibility and reliability of data, data quality is a business concept that treats data as an asset. 

It is a “never finished” type of issue–data is never perfect. 

Also unfortunately, data quality remains ambiguous–no universal standard exists. Within your organization, establishing a working definition of data quality and how to measure and report on it will reduce these issues. 


  1. Credit Union National Association (CUNA) Data & Statistics
  2. Center for Internet Security
  3. How BECU made ATM big data manageable
  4. Digital Federal Credit Union On Innovating To Meet Members’ Digital Demands
  5. How DCU is using data analytics to improve member service
  6. Risk Strategies to Level Up in a Competitive Auto Finance Game
  7. BECU Puts Data to Work with Help from Cloudera
  8. Big bank deposits rise as small banks see outflows
  9. Datateer 
  10. Matillion
  11. Sigma Computing
  12. Simpler Analytics
  13. ALTR
Do you like this series?

Receive an email when we create new content about Credit Union Data Analytics

Adam Roderick

Follow me here

About the Author

Adam's tech career spans startups to global firms, touching finance, tourism, e-commerce, and consulting. Spotting a way to give small- and medium-sized companies an advantage with data, he founded Datateer to democratize analytics. He values relentless progress, simplifying complexity, and putting people first.