U

Unlocking AI’s True ROI: Beyond the Obvious Obstacles in Credit Unions

Introduction: The AI Promise and Its Elusive Return

Artificial Intelligence (AI) has captured the imagination of the credit union sector, heralded as a transformative force for efficiency, personalization, and competitive advantage. Yet despite the buzz and significant investment, many credit unions still struggle to achieve tangible returns on their AI initiatives.

American Banker recently highlighted four key obstacles to AI ROI in financial institutions: the cost of modernizing core technology, the high price of AI talent, data governance challenges, and rising vendor costs. While valid, these are often symptoms of a deeper issue. True AI ROI in credit unions requires rethinking how member data is collected, managed, and interpreted to preserve and enhance the relationships that define the industry.

Beyond the Surface: Why the “Obstacles” Miss the Mark

Cost of Core Modernization: Necessary but not sufficient. A modern engine won’t reach its destination without a clear map.

• High Cost of AI Talent: A challenge, but often worsened by the absence of a coherent AI strategy.
• Rising Vendor Prices: A market reality, yet value remains elusive if data strategy is weak or duplicative.
• Data Governance (Closest to the Truth): The issue isn’t just the cost of governance—it’s understanding what data is being governed and how it’s prepared for AI use.

These obstacles are real but secondary. Modernization alone won’t guarantee AI success; a new “core” without strategic direction is just infrastructure. However, a new “modern core” doesn’t guarantee a desired destination without a clear map and a skilled navigator. Similarly, costly talent and tools fail to deliver if built on flawed data foundations. Data governance comes closest to the root issue—data truly is central—but the real challenge lies in the credit union’s overarching data strategy, not just the administrative overhead of compliance that’s becoming more of a focus in regulatory exams using the new FFIEC IT Handbook AIO Booklet.

III. The CRM-Embedded AI Approach: A Step, But Not the Destination

American Banker notes that EY’s Brian Gibbons sees value in partnering with core providers embedding AI or migrating to CRMs with built-in AI. While appealing, this approach often falls short, especially for credit unions.

The Core Issue: Data Fragmentation. Member data is scattered across loan origination, online banking, call centers, and wealth systems, each offering a “slice” of truth but remaining siloed.

Incomplete 360-Degree View. Even the best CRM cannot provide a full, evolving member profile. They store limited data and often miss external financial activity.

Preconceived Limitations. Integrating AI within these systems risks reinforcing current assumptions, locking in “today’s ground truth” and stifling adaptability. As a result, institutions optimize for what they already know instead of what they could learn.

IV. Reclaiming Relationship Banking with a Holistic Data Strategy

Despite the digital-first shift, members still value personal relationships, trust, and tailored service. AI should enhance this, not replace it.

Fragmented AI implementations, piecemeal or CRM-based, fracture the member experience and understanding. To preserve the essence of relationship banking, credit unions must empower AI with a holistic data strategy that fosters deep, contextual insight rather than narrow, system-defined views.

V. The Data Lakehouse: Foundation for Continuous Understanding and True ROI

A data lakehouse offers the modern foundation credit unions need. This architecture unifies all structured and unstructured data from transactional systems, digital channels, and external sources—creating a truly comprehensive member profile.

Key Advantages

• Holistic Data Capture: Centralizes data from all transactional systems, digital interactions, and external sources, creating a truly comprehensive customer profile.

• Continuous Data Shaping: Unlike rigid data warehouses or fragmented system integrations, a data lakehouse allows credit unions to continuously refine, transform, and shape data for use in new systems and AI models as understanding evolves. It’s a living, adaptable data fabric.

• Simplified “Before and After” Tracking: Provides a clear, historical record of customer interactions and model outcomes, enabling precise measurement of AI’s impact and iterative improvement.

• Scientific Signal Identification: By integrating all data, credit unions can move beyond preconceived notions and apply scientific rigor to identify the real signal in the noise of customer behavior and market trends. This fosters genuine insight, not just optimized assumptions.

• Protection Against Impulsivity: Distinguishes fleeting actions from meaningful needs.
  • Example: Consider a member who impulsively buys a candy bar at checkout. Traditional systems record a transaction; a lakehouse-based AI that integrates rewards data from a receipt capture tool understands context—distinguishing a one-off purchase from a habit and enables proactive, relevant engagement such as financial coaching or wellness savings suggestions.

VI. Conclusion: Empowering the Future of Credit Union Banking

The real barrier to AI ROI isn’t cost, talent, or tooling, it’s a fragmented approach to data.

A data lakehouse strategy empowers credit unions to truly understand members, continuously adapt to their needs, and unlock AI’s full potential to deepen relationships and drive sustainable growth.
To build a future where AI strengthens the value of relationship banking, contact Arkatechture to explore how a unified data platform like Arkalytics can help you see the whole member and turn insight into action. Ask about ArkaIQ: your new AI data analyst, ready to answer your everyday business questions in seconds. What is the direct loan growth versus indirect loan growth for 2024 and 2025? How many members do I have currently? ArkaIQ can answer these questions and more. Learn more about this tool here.

To learn more or connect with Arkatechture, visit their website.

About the Author: 

Pat Lapomarda
Director of Data Science
Arkatechture

Pat Lapomarda is the Director of Data Science at Arkatechture. He has a passion for harnessing data to drive informed action. Over the past 25 years, he has advised, developed, and implemented data science solutions at scale, including risk and marketing scoring systems. These solutions have primarily been at financial services companies ranging in size from top-ten banks, like TD Bank and KeyBank, to community finance institutions and boutique finance companies. Pat is a graduate of the College of the Holy Cross and completed graduate studies in Mathematics at Wesleyan University.