Community Banks Move from Pilots to Pragmatism on AI

Executive Summary

Responses indicate that community banks are incorporating AI/ML primarily to drive operational efficiency, strengthen fraud detection, and enhance customer engagement, while cautiously testing generative AI. Motivations center on cost savings, better service, and compliance accuracy, tempered by liability, bias, and third‑party risks. Banks are evolving governance with stronger risk management frameworks, explainability, and human oversight. Respondents seek clearer regulatory support, including supervised sandboxes, to scale safe adoption.

Key takeaways:

• About 40% of community banks have started incorporating AI/ML into their strategic vision or operations.
• Common use cases include operational efficiency, fraud detection, customer engagement, document processing, and call center automation.
• With generative AI, banks are exploring better decisions, risk management, and customer satisfaction; 60% use or plan to use it.
• Primary motivations are cost savings, efficiency, and better service.
• Risk management is being strengthened via strong frameworks, attention to unique risks and liability, explainable/testable tools, and human supervision.
• Regulatory clarity and support—such as a supervised AI sandbox—are requested to aid adoption.
• Adoption remains uneven: some banks focus narrowly on fraud detection, and at least one has not yet incorporated AI.

Bottom line:

Community banks are adopting AI/ML in targeted, efficiency-first use cases and cautiously piloting generative AI, while reinforcing governance with explainability, human oversight, and third‑party controls. Clear regulatory support and pragmatic risk frameworks are viewed as essential to scale benefits safely.

The Question (Ref #7)

Use of Artificial Intelligence and Machine Learning: How are community banks incorporating AI and machine learning (ML) into their digitalization strategies and initiatives? How has this use evolved as new forms of AI become commercially available, such as generative AI? Are banks using AI primarily for cost savings and efficiency, revenue-generating activities, or other reasons? How are banks evolving their risk management to address the use of AI and ML, including when introduced through a third-party relationship? How can regulators support community banks’ adoption of AI and ML?

Direct Response to the Catalog Question

Incorporation: Banks are applying AI/ML to operational efficiency and compliance, fraud detection, risk modeling, customer service, document processing, and call center automation.

Evolution with generative AI: Community banks are exploring generative AI to improve decisions, manage risks, and boost customer satisfaction; 60% report use or plans to use it.

Motivations: Early uses target cost savings, efficiency, better service, and improved compliance accuracy, with some citing potential to expand credit access.

Risk management: Banks are building strong risk frameworks to address unique AI risks and liability, favoring explainable, testable, human‑supervised tools and tighter third‑party oversight.

Regulatory support: Respondents seek clearer guidance and a supervised AI sandbox, alongside regulatory engagement that promotes safe innovation and competition.

Introduction

Question 7 asks how community banks are incorporating AI and machine learning into digitalization strategies; how use has evolved with generative AI; whether motivations are cost, efficiency, revenue, or other; how risk management is changing, including with third parties; and how regulators can support adoption. The responses collectively portray measured progress anchored in efficiency and control.

Historic Lessons in the Evidence

Respondents emphasize that community banks advance AI by starting with clear, efficiency‑oriented wins and maintaining strict governance. Caution arises from recognition of unique AI risks, liability considerations, and uneven maturity versus larger peers. Human oversight and explainability have emerged as non‑negotiable guardrails, especially when capabilities are introduced through vendors.

Recent Developments

Respondents report piloting AI in quality control and exploring fraud detection, document processing, and call center automation, while moving into generative AI for decisioning and customer satisfaction—60% use or plan to use it—and about 40% have incorporated AI/ML into strategy or operations.

The Challenge

Practical hurdles include regulatory uncertainty, bias risks, and explicit liability concerns when deploying or sourcing AI tools. Adoption is uneven and resource‑constrained, with some banks not yet using AI and others focused narrowly on fraud detection. Third‑party introductions of AI heighten governance complexity and demand transparency to manage model risk and compliance.

Evolving Metrics

Respondents justify AI through operational and compliance efficiencies, meeting expectations for convenience and speed, and outcomes such as reduced costs, improved compliance accuracy, and better decisions and customer satisfaction. They stress explainability, testability, and human supervision as evidence of control, and use pilots (e.g., quality control) to validate effectiveness before scaling.

A Framework Inspired by the Inputs

An implicit pattern emerges begin with self‑service automation and staff augmentation to capture efficiency gains; deploy explainable, human‑supervised models; embed AI/ML via vetted vendors; and strengthen governance to address unique risks and third‑party accountability. Pilots de‑risk adoption, with iterative scaling as controls and value are proven.

Case Study

A representative bank pilots AI for mortgage quality control, then explores fraud detection, call center automation, and document processing. It favors explainable, testable tools under human supervision and works closely with vendors while tightening third‑party risk oversight. Efficiency and customer engagement drive the roadmap, tempered by bias and regulatory scrutiny.

Recommendations

1. Start with low‑risk, efficiency‑first pilots in fraud detection, quality control, document processing, and call center automation.
2. Establish a strong AI/ML risk management framework that addresses unique risks and clarifies bank liability for AI use.
3. Require explainability, testability, and human supervision for all AI tools, including those introduced by third parties.
4. Strengthen third‑party risk management and demand vendor transparency on models, data, testing, and controls.
5. Engage regulators early and participate in supervised AI sandboxes or similar mechanisms to validate use cases safely.
6. Prioritize use cases that deliver cost savings, efficiency, and better service while actively monitoring for bias and compliance impacts.
7. Build governance and staff capabilities to meet customer expectations for convenience, speed, and accessibility as AI scales.
8. Phase in generative AI where benefits and controls are demonstrable, guided by clear policies and ongoing monitoring.

Conclusion

Community banks are integrating AI/ML into digital strategies through targeted, practical use cases while cautiously testing generative AI. Their priorities—efficiency, fraud prevention, compliance accuracy, and customer experience—are balanced with strengthened governance, explainability, and third‑party oversight. Respondents call for clear regulatory guidance and supervised experimentation to scale adoption. Taken together, these signals answer Question 7: safe, value‑oriented deployment with supportive supervision is the path forward.

This analysis will continue in our next publication. Don’t miss the next installment.

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