United Federal Credit Union

Michael Benassi serves as Vice President of Enterprise Analytics at United Federal Credit Union, where he leads enterprise data strategy, analytics, and AI initiatives. His experience spans data architecture, business intelligence, and analytics transformation, with a consistent focus on building scalable data platforms, strengthening reporting frameworks and advancing predictive analytics capabilities across financial services.

Building Analytics Maturity in Financial Services

For many financial institutions, the foundation of analytics maturity still begins with tried and true predictive analytics. There remains significant opportunity to develop models that anticipate member attrition, recommend next best products or services, detect fraud, and support other forward looking use cases. This is especially true for smaller institutions with lean teams or limited resources, where even modest predictive capabilities can deliver meaningful value. While it is tempting to jump straight to the latest AI concepts highlighted in webinars and industry events, many organizations benefit from starting with solutions that are safe, internally built, and well governed. Early success does not require boiling the ocean. Small, focused use cases can generate quick wins, build internal confidence, and create the muscle memory needed to responsibly scale more advanced capabilities over time.

A practical middle ground is introducing an approved AI partner—such as ChatGPT or Microsoft Copilot—into a controlled enterprise environment. This enables the future ready workforce many institutions already have to begin ideating and experimenting, while staying within governance and compliance guardrails. These tools often deliver immediate value through simple but impactful use cases: refining the tone of an email, organizing an inbox, or drafting clearer communications. One of the most impactful early wins comes from meeting intelligence—automatically capturing notes, summarizing key discussions, and clearly outlining takeaways and next steps. These capabilities reduce friction, improve accountability, and ensure alignment without adding administrative burden.

Once you have the basics built and you have trust in the foundation established, organizations can move toward large language model (LLM)–powered solutions that directly support member facing teams. Financial institutions sit on a wealth of unstructured data across policies, procedures, and internal knowledge bases. Unlocking that information through a streamlined, intuitive interface can dramatically improve efficiency and consistency in how member questions and requests are handled by providing answers with supporting links to required documents and steps by step instructions. This can provide a large ROI and efficiency gain for member facing teams and member experience.

Ultimately, this use case is about enabling member facing teams to respond faster and more accurately—while giving members confidence that when life events or concerns arise, the person assisting them can resolve the issue quickly and correctly the first time.

Making Enterprise Data Both Accessible and Accountable

The goal is simplicity: taking large, complex datasets and presenting them in ways that are intuitive and easy for end users to explore. The challenge for most organizations is not a lack of data, it is making that data both accessible and easy to use.

"It’s about empowering memberfacing teams to respond quickly and accurately, giving members confidence that during important life moments, their concerns will be handled with clarity, care, and precision."

Dashboards play a critical role in this effort. They can distill massive volumes of information into clear charts and visuals that provide high‑level perspectives on performance and trends. What is often overlooked, however, is the importance of pairing those visuals with access to line‑level detail. When users can move seamlessly from summary views into the underlying data, they are empowered to ask the next question—and the one after that—without friction or guesswork.

Dashboards provide trust, and AI provides speed. The real power emerges when these capabilities are combined. AI further accelerates this experience by helping users quickly interpret data and surface insights or recommendations from these trusted datasets. When users trust the data behind their dashboards and can rely on AI to deliver speed and clarity, organizations unlock a powerful balance. Trust remains foundational—but speed is essential. Together, they create analytics that are both impactful and sustainable.

Governing Data While Enabling Innovation

Effective data governance starts with trust—and trust is what enables innovation at scale. Governance provides the structure needed to align stakeholders, vet ideas, and establish approved analytics and metrics that the organization can confidently rely on.

When teams share common definitions and focus on the same outcomes, efficiency increases dramatically. Time spent debating metrics or reconciling conflicting numbers is reduced because foundational decisions have already been made. While it is unlikely that every definition will achieve unanimous agreement, transparency matters. When teams understand the why behind a definition, they may not fully agree—but they will follow it.

As analytics and AI capabilities evolve, governance must extend beyond data definitions to include thoughtful oversight of innovative technologies such as AI. A cross‑functional team—bringing together legal, compliance, analytics, security, and IT—plays a critical role in monitoring regulatory expectations and evaluating AI use cases. This ensures outputs remain trustworthy, are regularly reviewed, monitored for bias, and still allows for rapid experimentation and prototyping. Done well, governance does not slow innovation—it gives it room to move safely.

The Evolving Skillset of Analytics Leadership

Today’s analytics leaders are defined by far more than technical expertise alone. Innovation, collaboration, and strong technical foundations remain essential—but equally important are interpersonal skills, business acumen, and effective project management. Analytics is evolving at a pace unlike anything seen before. As a result, leaders must be lifelong learners—quick to adopt new capabilities when appropriate yet disciplined enough to apply caution when risk demands it. The ability to move quickly, while knowing when to slow down, has become a defining leadership trait. This balance is rarely learned in theory. It is built through experience—by navigating both the successes and setbacks that come with complex data initiatives. Over time, strong analytics leaders develop the judgment to know when rapid prototyping is safe, when governance is required, and how to guide teams through uncertainty without losing momentum.