7 Ways Credit Unions Can Make the Most of AI

By Slaven Bilac, CEO and Co-Founder, Agent IQ

The capability of AI continues to evolve at a rapid pace, with organizations finding creative and low-cost ways to accomplish tasks that seemed far-fetched for any computer system even a year ago. AI’s ability to help automate specific tasks and help answer questions -- even complicated ones – is leading many credit unions to consider how AI can work for them, how they can best utilize its capabilities, and how best to implement artificial intelligence within their organization.

There are seven questions that credit unions should ask before jumping into AI technology:

1. What problems do you want to solve?

Many organizations start with saying “let’s find a way to deploy AI.” However, they should really start by identifying a problem that is suitable for an AI-based solution. Look for repetitive, mundane, and expensive problems and then rate them based on what fraction of the problem you can solve for the relevant domain and how much value an automated solution would bring to your organization. 

If a problem has lots of data associated with it, or is highly repetitive, and would deliver lots of value, it should move to the top of the list for AI to solve. For credit unions, member support requests are a good example. Ideally, credit unions store these requests in a database, and measure how much time the team spends addressing them each month – important metrics as this provides a baseline measurement to monitor performance and set realistic expectations. 

2. Who would benefit from the solution?

Solving a problem for a member looks very different from solving problems for an organization. Credit unions that have already implemented AI tools mostly use them behind the scenes for things like detecting fraud and defending against cyber threats. Most members greatly benefit from these deployments but will never see those systems in action. 

Beware of solving one problem only to create another. From the credit union’s perspective, less call center traffic is a win, but for members who experience frustration due to the inability to find a solution for their problem, it’s actually a loss. Regardless of who benefits from the solution, consider the user experience for everyone it touches.

3. Is your data ready for processing by AI?

Credit unions are custodians of vast troves of data, but it isn’t always tidy and stored in an accessible format, and this could be a challenge for integrating AI tools into workflows. The costs of data access (from core providers and other third-party vendors), as well as data discrepancies, should be considered with the understanding that data hygiene is an ongoing practice, not a one-time task. IT teams can then audit data ecosystems and estimate AI coverage of the underlying data.

4. How will credit unions assure the member-facing aspects feel beneficial?

A great way to answer this question is to establish a small-scope test for AI. This allows credit unions to integrate with the vendor, get staff up to speed, and monitor the tool's progress. The user experience can be tested, and problems can be remedied while they’re small. This also enables credit unions to better assist members with the new process, which is especially important if using AI to assist with member communications and engagement.

5. How to monitor AI and check for any issues?

It’s nice to think that computers and machines don’t make mistakes, but they do and will. Credit unions need a process to monitor the AI and log activity. They should periodically review and validate that the tool is operating according to the parameters that have been set. 

In the same way new employees are paired with more experienced employees, the AI must be trained and reinforced to follow the correct protocols. AI is a dynamic, changing system and should be treated as such in advance of and during deployment. This is critical from a liability standpoint as well, as the FTC expects financial institutions using AI to operate with the same level of transparency and disclosure as always.

6. How to ensure that regulators will approve of theimplementation?

This question should stimulate credit unions to examine and document the decision process. If organizations follow the same level of due diligence and care that goes into decisions around other technologies and processes, then the chances are far better. The framework that is already in place for compliance is a good starting point for working with AI. Just keep in mind that the regulations around AI will be a moving target for some time. It is best to follow the principles and methods that both the credit union and the regulators are already familiar with.

Activity logging and leaving an audit trail are two must-haves to be able to prove to regulators that AI toolsets areoperating in a compliant way. It also gives teams an avenue to flag and fix errors before any issues arise.

7. How will success be measured?

Keep in mind that marketing hype sometimes leads to unrealistic expectations of what is possible. Hence it is important to calibrate expectations and communicate them to intended users. Ultimately, the focus should be on the utility AI provides and not on the exact approach or implementation.

This confusion is made worse by marketing hype. The technical particulars of how an algorithm differs from machine learning and how machine learning differs from AI aren’t something most people can be expected to care deeply about. However, lots of organizations are feeling pressure to include AI as part of their products or servicesin order to appear relevant.

Credit unions that are realizing success with AI are those that adhere to the mantra of “start small and iterate from there.” Credit unions should work with their AI vendor to establish clear, realistic metrics for success and then stay accountable to them. Ask for case studies from similar institutions and use those examples to calibrate goals. Ensure metrics that have been tracked for a while are chosen because without that historical data, it will be challenging to quantify any improvement.

It is tempting to get sucked into the hype around AI in banking. While technology is advancing rapidly, perfection is still elusive. AI is not designed to replace humans, but to enable people to use AI as a tool to augment themselves, allowing them to continue the incredible things they do well, but also make their tasks easier and more efficient.