AI in Credit Unions: Opportunity Meets Responsibility

Insights from Melissa Pomeroy, CCUA's EVP and Chief Operating Officer

Balancing innovation with risk, trust, and member protection

Artificial intelligence is quickly becoming one of the most talked-about technologies in financial services. For credit unions, the opportunity is real: AI has the potential to improve member service, reduce manual workloads, strengthen underwriting support, detect unusual activity faster, and help teams make better use of the data they already have. But for credit unions, the conversation cannot stop at efficiency.

Credit unions operate in a business built on trust. Members share their financial lives with institutions they believe will protect them, treat them fairly, and make decisions in their best interest. That means any use of AI must be evaluated not only for what it can do, but for what could go wrong if it is implemented without the right controls.

Fraud and risk implications should be central to every AI conversation. As AI tools become more powerful, so do the threats surrounding them. Fraudsters are already using AI to create more convincing phishing attempts, synthetic identities, voice impersonations, and social engineering attacks. At the same time, AI systems inside financial institutions can introduce new risks if they are given access to sensitive data, allowed to influence decisions without proper oversight, or deployed without clear audit trails.

For credit unions, the stakes are especially high. A poorly designed AI tool could provide inaccurate information to a member, expose confidential data, create inconsistent outcomes, or make it difficult to explain how a decision was reached. In areas such as lending, collections, fraud monitoring, and member communications, those risks are not theoretical. They can affect members directly and create regulatory, reputational, and operational consequences for the institution.

That does not mean credit unions should avoid AI. It means they should approach it with the same discipline they bring to any other major risk-management decision.  The most effective AI strategies begin with clear questions. What problem are we trying to solve? What data will the system access? Could the output affect a member’s money, credit, privacy, or rights? Who reviews the results? How will we document what happened? How will we know if the system is drifting, producing inaccurate responses, or creating unintended bias?

AI is already an important part of the future of financial services. Credit unions have an opportunity to use it in ways that support their mission: serving members better, faster, and more responsibly. But success will not come from adopting the flashiest tool or moving the fastest. It will come from thoughtful planning, strong governance, and partnerships with people who understand both the promise of AI and the risks that come with it.