McKinsey’s $170B Forecast Overlooks a Key AI Risk for Credit Unions

Guest Editorial by Edward Vincent, CEO, Lumio Solutions

McKinsey’s latest Global Banking Review warns that banks could lose $170 billion as agentic AI tools make deposit movement faster and easier. The report focuses on banks, although the implications for credit unions deserve separate attention. Credit unions operate with leaner liquidity positions, more concentrated member bases, and service models rooted in long-term relationships. These factors change the way AI-driven behavior affects them. The central challenge involves enterprise data discipline and the ability to interpret member activity accurately as conditions shift.

AI Opportunity…and Risk

Boards often ask how AI can support personalization, decisioning, and operational efficiency. These are important questions. Successful adoption; however, depends on the reliability of the credit unions’ underlying member data. In environments where data lives in silos and relies on manual maintenance processes, AI has limited value or worse, can yield ill-advised actions, because the credit union does not have a complete or timely view of the relationships it is trying to support.

Throughout the years working with financial institutions, the pattern has remained consistent: clarity about member behavior enables clarity in strategic decision-making. Credit unions achieve stronger results with AI when their data is complete, accurate and organized, even while sourced from across business units. When those conditions are not in place, AI risks steering an institution away from opportunity, rather than towards it.

Complete, Accurate, Organized Data Mitigates Liquidity Pressure Requires Accurate, Shared Information

Deposit mobility increases customer unpredictability and speed with which money moves.  When data resides in unconnected core systems, lending platforms, fraud tools, and compliance modules, reconciling trends become slower and less certain. Leadership teams may miss early indicators of member churn, shifts in deposit sensitivity, or changes in competitive positioning.

Introducing advanced analytics into this environment can obscure these blind spots. Robust data management, including strong governance, improves situational awareness because it ensures finance, risk, and operational teams are all working from the same information. When that alignment breaks down, the credit union loses the ability to respond quickly, thoughtfully and with confidence.

A Practical View of Strong Governance

Before moving deeper into AI initiatives, credit unions would benefit from crafting a data strategy, inclusive of architecture, governance, storage, quality, reconciliation, maintenance, and ultimately delivery. This includes consistent data definitions, clear ownership of critical fields, transparent oversight, and reporting frameworks that extend across all lines of defense. These practices promote clarity and support faster, more accurate decision-making, powered by complete, accurate and organized data.

When governance is strong, AI amplifies and even supplements institutional judgment. Leadership teams can evaluate model results, validate underlying assumptions, and adjust strategies in real time because they trust the integrity of the data. With this foundation, credit unions are better positioned to anticipate liquidity pressure, understand emerging member needs, and address regulatory expectations with clarity.

Consumer behavior is changing quickly in an AI-enabled financial ecosystem. Credit unions experience this more directly because of their concentrated membership and relationship-driven mission. They cannot control the pace of change, but they can build structures that allow them to adapt effectively.

Credit unions that prioritize crafting a data strategy, and establishing a strong data foundation,  early gain a clearer view of their risk profile and can incorporate advanced analytics in a way that improves operational precision. This approach supports innovation without creating unnecessary volatility. Governance provides stability, alignment, and a shared understanding of the environment. AI builds on that stability when the foundation is sound.

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Edward Vincent, CFA, is the CEO of Lumio Solutions. With more than 25 years in fintech, analytics, and financial services, he has built and scaled technology and data businesses across capital markets, compliance, and risk management. A dual-MBA graduate of Columbia Business School and London Business School, Edward brings an operator’s mindset to modernizing how financial institutions turn data into decisions.