Why Banks and Credit Unions Need a Scalable Foundation Before Expanding AI

Artificial intelligence adoption in financial services is accelerating, but progress is rarely smooth or predictable. New capabilities often arrive in waves, and every major advancement requires customers, employees, regulators, and risk teams to reassess their confidence in the technology.

This challenge is especially significant for small and midsized banks and credit unions. These institutions operate within strict regulatory requirements and narrow margins while facing pressure to compete with national banks, global financial firms, and rapidly evolving fintech companies. At the same time, many customers and internal stakeholders remain cautious about using AI for sensitive financial decisions and services.

AI adoption is growing. A 2026 survey from Cornerstone Advisors found that 59% of credit unions and 49% of banks had already placed generative AI applications into production. That represents a substantial increase from the single-digit adoption levels reported in 2024. Agentic AI remains less common, although momentum is building. Approximately 17% of credit unions and 7% of banks have invested in or deployed agentic AI, while roughly one-quarter expect to invest in the technology during 2026.

Trust remains a major obstacle. A January 2026 consumer study from MX found that only 32% of consumers trust AI to help manage their finances, while half said they distrust it. Customer experiences with existing tools may be contributing to that hesitation. Among the 34% of consumers who have used an AI chatbot in financial services, more than half said it was not very helpful or not helpful at all.

Research from J.D. Power points to a similar issue. Only 28% of national bank and credit card customers use their institution’s virtual assistant. Satisfaction declines considerably when customers rely on these tools for sensitive needs, including resolving problems, disputing payments, or reporting fraud.

For smaller financial institutions, the answer is not necessarily to spend heavily on every new AI application or rush into deployment without sufficient controls. A more sustainable strategy is to build the underlying infrastructure first.

In an interview with The Financial Brand, Elias Hiller, product leader for AI at MX, explained that banks and credit unions can improve both AI performance and customer confidence by establishing a strong foundation for data, connectivity, governance, and oversight.

Data Readiness Should Come First

Technology teams may be tempted to spend most of their time comparing AI models, evaluating vendors, or analyzing token efficiency. However, Hiller said financial institutions should place greater emphasis on preparing their data.

Data at banks and credit unions is often distributed across multiple systems that use inconsistent names, formats, and classification methods. Before AI can produce reliable insights, institutions typically need to complete three important tasks:

• Data cleansing: Removing inaccuracies, outdated records, and duplicate information.
• Data normalization: Converting information from different sources into consistent formats.
• Data categorization: Organizing transactions and other records into useful groups that support specific business outcomes.

Once this work is complete, the information can be organized into structured context graphs. These may resemble the knowledge graphs used by major technology platforms. A financial institution’s graph could include product details, frequently asked questions, brand standards, and service policies. A customer or member graph could contain account data, contact center history, transaction information, and signals related to potential needs or purchasing interest.

Maintaining these data sets independently can be useful, but combining them into a shared and governed context is essential for more effective AI. When data is well organized, AI applications are less likely to generate inaccurate responses or unsupported conclusions. Clean data also allows institutions to coordinate deterministic rules with probabilistic AI functions more effectively.

A well-designed data foundation strengthens governance as well. Financial institutions can identify sensitive categories of customer information, define who may access them, and connect those permissions to relevant policies and regulatory requirements.

Legacy Systems Require a Secure Connectivity Layer

Data readiness alone is not enough. AI systems must also be able to access approved information through secure, controlled connections.

Organizations that build AI-native applications generally use API-first architectures. These designs allow authorized AI agents to access approved data sources through governed interfaces while receiving current information in a secure way.

Most banks and credit unions, however, were not built with this architecture in mind. Many continue to rely on core systems that were installed decades ago, before real-time data access and API connectivity became standard design priorities. Information may be spread across core banking, lending, card, payments, and digital banking platforms supplied by different vendors at different times.

To address this challenge, institutions can add an integration layer, often referred to as middleware or an API layer. This layer makes selected legacy data available through controlled interfaces while preventing AI applications from connecting directly to core systems.

Better connectivity also helps solve a common customer experience problem: the inability of a bank or credit union to recognize a customer consistently across channels. When systems are connected, an institution can create a more complete and current view of a person’s products, transactions, service history, and needs.

More advanced AI use cases may also require access to information held by other financial providers. Customers can grant permission through open banking connections or data aggregation services, and they should be able to review, manage, and revoke that permission.

Open standards such as the Model Context Protocol can help AI applications connect to approved information in a consistent way. The same standards may allow financial institutions to decide whether and how their own data can be accessed by external AI platforms.

With properly governed connections, large language models and AI agents can work across the right data sources without forcing an institution to replace its entire technology environment. Each approved source can add context to the customer relationship and improve the usefulness of financial applications.

Trust Depends on Controls and Transparency

Financial institutions evaluating AI governance should begin with three fundamental questions:

• Is the model accurate and fair?
• Can the institution impose firm limits on what the system can do, including human intervention?
• Can every important output be explained, reviewed, and audited?

The answers will shape the confidence of customers, employees, regulators, and other stakeholders.

Model Validation

AI models require extensive testing for accuracy and potential bias, particularly when they are used in lending, risk analysis, financial guidance, or other high-impact areas.

Accurate data and a careful balance between rules-based processes and probabilistic AI can improve performance, but no model will be perfect. Bias can also appear even when protected characteristics such as race, age, or sex are excluded. Other information may act as a proxy and produce unintended disadvantages for certain groups.

Hiller said the industry still has work to do in managing algorithmic bias. For that reason, institutions should keep eligibility decisions within established underwriting, compliance, and risk management processes. AI can help customers understand debt, compare options, or explore potential consolidation strategies, but existing controls should determine whether a customer ultimately qualifies for a product or service.

Guardrails and Human Oversight

Guardrails limit what an AI system can say and do. Basic safeguards may block harmful content or dangerous requests, while financial services guardrails may prohibit unauthorized investment advice, unsupported claims, or actions that exceed an institution’s policies.

When a request reaches a guardrail, the system should provide an appropriate next step instead of ending the interaction abruptly. That might include referring the customer to a qualified employee, directing them to a secure service channel, or explaining what information is required.

Guardrails should be precise. If an assistant refuses too many reasonable questions, customers may quickly lose confidence in the tool.

Human oversight provides another layer of protection. Institutions should determine when an employee must review or approve an AI-supported action based on the system’s accuracy, the institution’s risk tolerance, and the potential impact on the customer.

Customer approval should also increase with the risk of the action. A customer may not need to confirm every routine informational response, but explicit approval may be appropriate before an AI system transfers funds, changes account details, submits a dispute, or takes another consequential step.

These controls are familiar in traditional financial applications, but they become more complex with generative and agentic AI. Unlike fixed workflows, these systems may determine their own sequence of actions. Institutions therefore need safeguards that operate in real time and can evaluate decisions as they occur.

Audit Trails and Explainability

Traceability should be built into an AI system from the beginning rather than added later. Regulators and financial institutions need to understand how an AI decision or recommendation was produced.

A complete audit trail should document the data used, the tools and processes called by the system, the guardrails applied, and the model and configuration in operation at the time. This is particularly important because generative AI systems are probabilistic and may not produce identical results from the same type of request.

Transparency can also improve the customer experience. When appropriate, institutions can show the information or policy basis for an answer, recommendation, or action. A governed API layer further supports auditing by making it easier to track the data moving between systems.

The AI Challenge Ahead for Financial Institutions

As banks and credit unions expand their customer-facing AI capabilities, their underlying technology foundation will become increasingly important. Whether an institution is introducing a basic conversational assistant or developing autonomous AI agents, its success will depend on the quality of its data, system connectivity, governance, and controls.

Many institutions are not yet ready for advanced deployment. They may understand the customer experience they want to create, but lack the systems and processes needed to deliver it consistently.

A virtual assistant may appear to be an easy starting point. However, a chatbot that refuses too many questions, cannot recognize the customer’s full relationship, or stops before completing a basic request may damage trust rather than build it. A customer who has a poor first experience may decide not to use the tool again.

With the right foundation, financial institutions can expand AI in line with customer expectations and their own ability to support the technology responsibly.

Key Questions for Banks and Credit Unions

Financial institution leaders planning their next phase of AI adoption should consider the following questions:

• Which data repositories are essential to creating a complete view of each customer or member?
• Do those sources use consistent taxonomies across customers, products, accounts, and transactions?
• Can approved data be accessed through a common and secure interface?
• Are governance policies managed centrally, or are they divided among separate departments and systems?
• Which AI actions require customer approval or employee review?
• Can the institution explain and audit every important AI-generated recommendation or decision?

Answering these questions will help define the work required to deploy AI responsibly. A scalable foundation allows banks and credit unions to improve customer experiences over time while protecting accuracy, compliance, security, and trust.

Source: Thefinancialbrand.com