Data Is the Key to AI Governance

For credit unions and financial institutions, effective AI governance starts with a solid data foundation, Velera SVP Jeremiah Lotz writes in a new PYMNTS eBook, “AI Runs Payments. Governance Decides What Happens Next.”

Artificial intelligence is now embedded in the core infrastructure of payments, powering fraud detection, identity verification, member support and operational decision-making. These systems often operate in milliseconds, influencing fraud outcomes, regulatory exposure and — most importantly for credit unions — member trust.

At this stage, the industry has largely moved past the question of whether to use AI. The more pressing question is how to govern it.

AI Governance Starts With Data Governance

For credit unions and financial institutions, effective AI governance starts with a solid data foundation. Before deploying any model, institutions must understand how data moves across their ecosystem of internal and third-party systems, including processors, FinTech partners, fraud vendors, cloud platforms and API‑based AI services.

Mapping these flows helps credit unions set guardrails around what information can be shared, how automated outputs interact with operational systems and where additional protections are required.

Ensuring Data Is Ready for AI

The next challenge is ensuring data is ready for AI. Many legacy systems were not designed for machine learning, leaving data incomplete or inconsistently structured. Generative AI can make these gaps more visible, especially when models connect to internal documents and knowledge bases. Many institutions wish they had invested in a well-managed data foundation earlier, as doing so enables decisions that are traceable, auditable and trustworthy.

Explainability Is Essential

Once strong data foundations are in place, explainability becomes the next critical layer. AI now influences fraud checks, identity verification, lending outcomes and everyday interactions — all areas where credit unions need to understand why a decision was made.

Explainability gives institutions the ability to see how a model arrived at an outcome and to communicate that logic to risk teams and regulators. It ensures decisions can be reviewed and improved over time, supporting the oversight needed to maintain member trust.

Cutting Through the AI Hype

AI enthusiasm has accelerated experimentation, but for credit unions, the goal isn’t to “win the AI race.” Institutions that try to keep up with every new technology or rush to be first frequently see their AI pilots falter — largely because they lack clear ownership, defined success metrics and the readiness to scale.

Instead of chasing every trend, the focus should be on initiatives that align to the organizational strategy to deliver measurable value for members. Those that scale artificial intelligence successfully put guardrails in place early: well‑scoped, strategically aligned use cases, clear model and data ownership, and ongoing performance monitoring. These structures allow teams to experiment responsibly while ensuring AI investments translate into lasting impact.

Key Questions for Financial Institutions

As AI becomes more prevalent, boards and leadership teams should ask three critical questions: Where is AI solving for organizational strategy, what data powers those systems and how is that influence being governed?

Leaders do not need to be technical experts, but they must understand where AI is shaping decisions and how outcomes are being monitored. The institutions that do will be best positioned to scale AI responsibly while preserving the trust that underpins financial services.