To Get Business Value From AI, Your Credit Union First Needs a Data Strategy

By Matt Sabo, Wipfli Director, and Luke Ryan, Manager Wipfli

While the financial services industry is integrating more AI into its operations, large national institutions are generally moving faster than smaller peers. This risks leaving many credit unions at a disadvantage, especially given that newer AI tools offer deeper and more valuable business uses than yesterday’s chatbots.

But AI is useless without good data, so if you want to catch up on AI, you first need a data strategy. Keep reading to learn more about why, what an effective credit union data strategy looks like, and how to get started putting yours in place.

How Does a Lack of a Data Strategy Hurt AI and Technology Implementation for Credit Unions?

Credit unions have typically moved slowly on data strategy, largely due to caution around regulatory compliance. However, a lack of a cohesive data strategy makes it impossible for your credit union to properly implement AI or other modern systems:

• No data foundation for AI: AI is remarkably effective at drawing business insights from gigantic amounts of data. But it needs a strong data foundation to function. Without that, you won’t be able to use AI to create value inside your credit union.

• Disconnected systems: You need a data foundation like a data lake house (a central repository for all your structured and unstructured data) to be able to fully integrate your core systems like operations, lending, and HR so they can share information to give you deeper insights into your business.

• Limited visibility inside your business: Without a modern data strategy, you won’t be able to use AI to analyze what’s happening inside your credit union, like identifying trends or emerging risks.

• Stalled automation: Disconnected systems also mean you can’t automate routine tasks like data entry and cleanup, which leaves you reliant on slower systems like spreadsheets.

• Slow reporting: A lack of automation also slows your reporting, because your team has to manually organize data from multiple disconnected systems rather than having it instantly available at the tap of a button.

• Weaker cybersecurity: Credit unions without a modern data strategy tend to use on-premise systems rather than cloud-based ones. However, this represents a distinct cybersecurity vulnerability, as servers you operate yourself are almost always less secure than those run by a dedicated cloud services company. If you’re running non-cloud-based systems, there’s also a higher risk that those systems are outdated and unsupported, making them easy targets for cyber attackers.

How Does an Effective Data Strategy Benefit Credit Unions?

An effective data strategy gives you the foundation for integrating AI more deeply into your credit union’s operations, as well as connecting your core operational systems. You’ll also be able to develop a deep well of institutional knowledge within your organization, as your AI can essentially make the insights and experience of seasoned individual team members available to anyone in your organization.

Key benefits of implementing an effective data strategy include:

Deeper member and prospect insights

AI can glean your data, along with publicly available sources, for insights on your members or prospects. This means your loan officers and member service professionals can spend less time digging through data themselves and more time offering your members or prospects products they’ll actually be interested in.

Institutional knowledge reserve

AI can help you get institutional knowledge out of the heads of individual members of your team and into your systems, where it is accessible by your entire workforce. This means you’re no longer dependent on the experience of one person or a small handful of people, making your team both more effective and less vulnerable to retirements or turnover.

Time-saving automation

A strong data foundation allows you to take full advantage of modern AI automation capabilities. These can help your team move information around much more quickly, speeding up your reporting and allowing your team members to spend less time gathering data and more on higher-level work like financial modeling or strategy.

Cost savings

Because a modern data strategy involves moving onto cloud-based servers and systems, you actually have the opportunity to save on costs. Transitioning to cloud-based means you’ll no longer have to pay for operating your own servers and the associated IT expenses.

Stronger regulatory compliance

Financial regulators have thus far said very little about AI. However, that silence won’t last. Implementing a modern data strategy now will allow you to preemptively demonstrate your commitment to making thoughtful use of AI within your credit union and justify your choices to regulators.

You can also use AI tools to assess your overall compliance efforts by reviewing your policies against existing regulatory guidance and to watch for patterns of fraud.

Business insights from unstructured data

Finally, once you have a data foundation like a data lake house, you can make much better use of the mountain of unstructured data (like documents, emails, videos, or audio recordings) that you generate during the normal course of operations. AI can dig through this data to give your team fast answers to complex organizational questions.

For example, let’s say you ask AI to analyze your lending history for trends and uncover that you’re issuing fewer loans to members from a particular ZIP code. You can then follow up by asking AI whether your lending policy is making it harder to lend to that area, so you can assess how to reduce any roadblocks.

How Should Your Credit Union Take Action to Implement a Strong Data Strategy?

Credit union leaders should make implementing a cohesive data strategy an ongoing organizational priority. But to get started, consider taking three steps:

1. Develop a data-driven organizational mindset

Your whole organization needs to understand the value of operating from a data-informed perspective. Many credit unions still don’t work this way, but it’s essential if you want to compete in today’s business environment. You can start slow, but do start.

2. Do a maturity assessment with a technology advisor

Sit down with a technology advisor to understand where your credit union is right now from a data and technology maturity perspective. During this assessment, you should identify specific technology pain points or problems inside your business that need to be addressed.

3. Create a strategic data and technology roadmap

Work with your advisor to create a strategic data and technology roadmap to solve your pain points. This might include steps like building a data lake house, implementing AI tools, or transitioning onto a cloud-based CRM. Your roadmap will typically include a detailed timeline covering a period of months or years and will serve to guide your team as you move forward.

Connect with Wipfli to learn more.