In a highly regulated industry like financial services, AI can be a game-changer — with the right guardrails in place.
Chief Credit and Data Officer Tudor Enoiu’s team at Alliant Credit Union knows this well. His team continuously monitors advances in AI, data platforms and analytics through a risk and value lens and creates space to pilot emerging capabilities quickly, while embracing strong but nimble governance and clear business outcomes.
“That allows us to balance speed to action with risk discipline, which I view as a critical trade-off, and scale innovation in a way that’s sustainable, trusted and aligned with the core economics of the business,” he said.
With these measures in place, Enoiu’s team successfully created a couple of large AI use cases, and so far, both the business and its customers have benefited from it.
“The impact has been tangible: better member experience, meaningful efficiency gains and a growing level of trust from business and risk partners that AI can be scaled responsibly,” he said.
Read on to learn more about how Alliant Credit Union’s tech teams are staying ahead of emerging technology, the recent work they’ve been doing with AI and how they foster innovation through a focus on experimentation.
About Alliant Credit Union
Headquartered in Chicago, Alliant Credit Union is a digital credit union guided by its mission of “boldly disrupting banking norms to do good for our members, employees and communities.” The company offers its members financial services related to checking and savings accounts, credit cards, loans, retirement and investments.
How does your team stay ahead of emerging technology trends while scaling fast?
In my world, which covers credit risk, data and AI, we strive to stay ahead of emerging technology by being very intentional about where we experiment and where we scale. My team continuously monitors advances in AI, data platforms and analytics through a risk and value lens. We ask not just what’s new, but what’s durable, explainable and fit for a regulated environment. We put a lot of effort into creating space to pilot emerging capabilities quickly, while anchoring everything in strong but nimble governance as well as clear business outcomes. That allows us to balance speed to action with risk discipline, which I view as a critical trade-off, and scale innovation in a way that’s sustainable, trusted and aligned with the core economics of the business.
“We ask not just what’s new, but what’s durable, explainable and fit for a regulated environment.”
What recent product or feature are you most proud of — and what impact has it had?
I’m most proud of how we’ve stood up our first couple of large AI use cases in a way that’s both practical and credible in a risk‑managed environment. As we did so, we focused on building a foundation — clear governance, strong data pipelines and production‑ready AI use cases — that allows multiple teams to move faster with confidence. The impact has been tangible: better member experience, meaningful efficiency gains and a growing level of trust from business and risk partners that AI can be scaled responsibly. That foundation is now enabling us to partner closely to identify additional opportunities to deliver member value through AI.
How do you create a culture where innovation and experimentation are encouraged daily?
We try to build a daily culture of experimentation by making low‑risk innovation easy and high‑risk innovation disciplined — without turning everything into approval‑by‑committee. We give people practical access to the right tools based on need, such as broad access to Microsoft Copilot, more advanced capability for smaller groups, etcetera, and then pair that access with clear guardrails and a tiered framework so employees know what they can do immediately versus what requires review. We reinforce it with real on-the-ground enablement — not slogans — through playbooks/prompt libraries, a subject matter expert support group that helps teams refine prototypes into something real, and a community model that encourages peer‑to‑peer sharing of use cases and lessons learned. And because building trust both internally and with regulators is so critical, we’re explicit that humans remain accountable, data protection is non‑negotiable and the level of governance scales with impact, especially for agentic workflows, so teams can move fast and stay safe.
