AI in Community Banking

Implementing AI in Community-Based Financial Institutions — From Policy to Practice

A Practical Roadmap for Safe, Sound, and Incremental AI Adoption

The banking industry’s pace of change is accelerating,  facing renewed and emerging challenges like margin compression, higher compliance costs, talent acquisition, technology upgrades, and core deposit growth and retention, Fintech competitors have entered the market at an increasing rate and continue to evolve as regulations have eased and the large regional and mega banks are only getting stronger. To remain competitive and even thrive, community-based financial institutions (FIs) must operate more efficiently and still deliver desired products and services, while continuing to leverage their deep, local market knowledge.

AI presents one of the most significant opportunities for FIs in decades. Yet, unlike large institutions, community banks and credit unions must approach AI through a risk-appropriate, workflow-light or demonstratively strong workflow processes, and vendor-leveraged strategy. AI’s promise is substantial—improved efficiency, enhanced customer experience, stronger credit analysis, better fraud detection, and more resilient compliance functions—but only if implemented with strong governance and phased adoption. A “workflow-light” AI approach refers to AI applications that can be implemented with limited workflow redesign, minimal systems integration, and manageable operational change, while still operating within existing governance, compliance, and human oversight frameworks.

For some, limited budgets, lean staffing, and regulatory scrutiny require a pragmatic approach that can be scaled over time while delivering early wins with applications requiring less onerous investment, limited workflow redesign(“workflow-light”), and data governance implications. Over time, as your institution builds expertise through education and experiential learning and, importantly, put in place effective workflow processes and sound data governance, you position your institution to take advantage of AI’s predictive and decisioning tools, especially tools that guide customers in managing their financial well-being.  We at The NBS Group, LLC (NBS) are seeing AI adoption explode at FIs of all types and sizes.  The time is now to unleash AI, throughout your FI in a well-managed fashion as outlined herein.

This article aims to provide a strategic framework, identifying steps you can take now to put in place an AI risk management program and quickly begin to take advantage of AI technology.

CEO Insight: Do not view AI as “plug and play.” Treat it as a long-term operational discipline designed to improve efficiency and customer experience—not to replace human judgment. Start by educating management and the board, then establish a risk-based governance framework that supports evaluation, adoption, and the phased rollout of low-risk AI solutions to build confidence and generate early wins.

This article offers community bank FIs a structured pathway from policy development to practical, low risk use cases, leveraging:

• Education and training frameworks for the board and management
• AI risk and governance frameworks
• Regulatory guidance across OCC, FDIC, and Federal Reserve
• Readiness assessments
• High-impact, low-cost applications
• A roadmap from immediate wins to advanced analytics

Foundational Principle: AI is Not “Plug and Play”

A core principle for FIs is that AI is not a technology you simply “turn on.” It is an operational discipline that requires governance, oversight, and continuous monitoring.

To realize value from AI investments, FIs must assess business processes, data governance, and data integrity. AI will not fix broken workflows or perform reliably without clean, well-governed data. It also cannot support more advanced use cases—such as product recommendations or credit decisioning—without a strong information security program covering data and applications.

WHERE TO START?

I. Management and Board Education

AI is a relatively new technology and as such management and boards should be rightfully cautious and skeptical of the benefits.  In fact, there is evidence that AI has not delivered the expected results, including ROI, in many instances.  This has been the result of:

• Not spending adequate time educating and driving the desired adoption
• Not preparing the organization for the expected cultural change
• Ineffective workflow processes (AI cannot effectively improve broken workflows)
• Poor or inadequate data availability, data quality and data governance

Together, A Management Education Plan and Board Education Plan need to ensure AI is:

• Safe
• Effective
• Compliant
• Explainable
• Monitored
• Aligned with Strategy

Integration: How Management & Board Training Work Together

CEO Insight: A comprehensive AI adoption education program not only promotes cultural change but ensures consistent understanding throughout your FI in terms of how AI should be applied and how it should be managed.

Board Oversight of Artificial Intelligence

The Board of Directors plays a critical role in ensuring AI adoption aligns with the institution’s strategy, risk appetite, and safety and soundness obligations. While directors are not expected to be AI experts, they should understand how AI is being used, the associated risks, and whether appropriate governance and controls are in place.

Board oversight should focus on:

• Approving the institution’s AI strategy, governance framework, and risk appetite.
• Understanding key AI risks, including fair lending, consumer protection, cybersecurity, data quality, model risk, and third-party vendor dependence.
• Ensuring management has established appropriate policies, controls, monitoring, and staff training.
• Reviewing significant AI initiatives, high-risk use cases, and vendor relationships.
• Receiving periodic reporting on AI performance, risks, compliance, and emerging regulatory expectations.

Key Questions Directors Should Ask Management

• What AI tools are currently in use, and what new applications are planned?
• How are AI risks identified, assessed, and monitored?
• How do we ensure compliance with fair lending, consumer protection, and privacy requirements?
• What controls ensure appropriate human oversight of AI-generated outputs?
• How are third-party AI vendors evaluated and monitored?
• How will success be measured, and what reporting will be provided to the Board?

Effective Board oversight helps ensure AI enhances organizational performance while remaining consistent with the institution’s governance standards, regulatory obligations, and customer-focused mission.

II . Building the Foundation: Governance, Policy, and Readiness

Having educated and motivated the board and management, the FI must begin AI transformation with a strong governance structure. This is non-optional; every regulator expects AI to be managed through your existing model risk, third-party, cybersecurity, compliance and enterprise risk management programs and frameworks.

1. Governance and Policy Requirements

Community-Based FIs should adopt an AI Governance Policy, covering:

• Roles and responsibilities
• Human-in-the-loop review
• Prohibited uses and information security
• Third-party risk expectations
• Explainability requirements
• Model inventory and classification
• Data governance
• Ongoing monitoring and validation

2. Regulator-Specific Expectations

Regulators treat AI the same as any other model: it must comply with model risk management guidance of SR 11-7, information security, fair lending rules, UDAAP, consumer banking laws and regulations and vendor management/third-party oversight.

OCC:

• Highest emphasis on documentation and explainability
• Strong third-party vendor oversight
• Quarterly board reporting for high-risk AI

FDIC:

• AI tied to safety and soundness
• Unfair, Deceptive, or Abusive Acts or Practices (UDAAP) review for customer AI
• Third-party risk rigor for fintech partners

Federal Reserve:

• Full SR 11-7 validation
• Board-level attestation for model governance
• Inventory requirements across bank & holding company

3. AI Readiness Checklist

Your AI readiness checklist should provide a complete assessment across:

• Governance
• Risk management
• Data infrastructure
• Vendor readiness
• Cybersecurity controls
• Compliance
• Change management

This forms the backbone of the AI due-diligence phase.

III. Implementation Roadmap: From Policy to Practice

As noted earlier, AI adoption is accelerating across consumer, education, and business environments—and community-based financial institutions are increasingly part of that shift. As a result, now is the appropriate time to begin. This article outlines a range of low-risk, high-payback opportunities, making Phase 0 the logical starting point for an effective AI program, setting a foundation for cultural change, regulatory demands and risk management discipline.

Phase 0 (Months 0–3): Education, Governance & Due Diligence

• Educate Management and Board (continue Board updates at least quarterly)
• Create and adopt AI Governance Policy and Program
• Establish Management AI Adoption Committee (monthly)
• Conduct overall enterprise Readiness Assessment
• Build AI inventory
• Update Information Security Policy and Procedures
• Update End-user Computing Policy and Procedures
• Update, as needed, MRM and Vendor Management Policies and Procedures
• Classify AI into risk tiers
• Begin introductory staff training
• Document regulatory alignment

This is required before implementing any AI.

Phase 1 (Months 3–6): Quick, Low-Risk Wins

• Marketing content, such as social media posts, customer emails, product descriptions
• Begin internal use only document drafting & summarization tools
• Adopt internal use of Microsoft co-pilot
• Begin internal use only management meeting and board meeting note taking
• Evaluate existing Business Applications (‘BA”) where the vendor is embedding AI
• BA to look for early AI use and wins include: commercial LOS Credit Spreads and Credit Memo drafting, CRM, etc.

These deliver rapid efficiency gains within a unit, department, or even individual positions without triggering heavy model-risk requirements.

Phase 2 (Months 6–12): Moderate Integration

Next there are more advanced uses of AI, again, mostly working with your existing BA vendors for every department throughout your FI.  NBS has not yet observed an active AI functional use case within or interrogating the core banking system or data of a FI, but we are hearing from almost every core provider that programs are in the works.  You should be able to adopt within your AI Programs functional use cases in:

• Website chatbot
• Optical Character Recognition (OCR) ingestion for loan files
• Compliance monitoring tools
• Customer-service agent-assist

Requires controlled deployment and testing but remains low to moderate risk.

Phase 3 (Months 12–24+): Advanced / Higher Governance

Next BA use cases in terms of expected risk and complexity include:

• Predictive credit analytics
• Automated underwriting (with explainability)
• Next-best-offer personalization
• Intelligent document processing (KYC, loan docs)
• AI-driven risk monitoring (fraud, AML, model drift)

Requires SR 11-7 validation and ongoing monitoring.

IV. A Practical Application Starting Point

FIs should start with high-impact, low-risk, low-cost use cases that require little workflow redesign (“workflow-light”). In the early part of your AI adoption, we would expect you to emphasize low risk and simple use cases.  Many high-value AI use cases require minimal workflow design because they are standardized, drafting-based, internal, or vendor-managed.

These include:

• Drafting and summarizing
• Document classification
• Credit memo first drafts
• Customer service support
• Meeting notes
• Compliance monitoring
• Fraud vendor tools
• Internal task automation

Initially, this is critical for FIs with limited staff, as it avoids costly process reengineering.

Top Low-Cost, Low-Risk AI Applications

1. AI-Assisted Customer Service (Internal & External)

Quantified ROI / Benefits

• 15–30% reduction in call volume
• 10–25% reduction in average handle time
• 20–40% faster employee onboarding (knowledge access tools) (for instance, “What is our HELOC policy?”)
• Improved customer satisfaction (CSAT) and response consistency

Supporting Research

• AI-powered chatbots and agent-assist tools reduce service costs while improving responsiveness and scalability¹
• Federal Reserve research highlights generative AI as a key enabler of customer support efficiency gains for community basedfinancial institutions²

Strategic Value

• Immediate cost savings without headcount reduction
• Enhances customer experience without requiring workflow redesign

2. AI-Generated Marketing Content

Quantified ROI / Benefits

• 70–90% reduction in content creation time
• 2–5x increase in campaign output capacity
• Improved personalization → higher response rates (10–30%)

Supporting Research

• Generative AI significantly improves productivity in writing and marketing functions³
• Community based financial institutions are increasingly using AI for customer communication and engagement at scale¹

Strategic Value

• No integration required
• Enables smaller FIs to compete with larger institutions in digital marketing

3. Document Drafting & Summarization

Quantified ROI / Benefits

• 50–80% reduction in document preparation time
• Lower reliance on external consultants (compliance, audit, policy writing)
• Faster turnaround for regulatory responses

Supporting Research

• U.S. Treasury identifies document drafting, summarization, and reporting as among the highest-value early AI use cases in financial services¹

Strategic Value

• Near-zero risk when human-reviewed
• Immediate productivity gains across compliance, HR, and operations

Examples:

• Policies
• Procedures
• Job descriptions
• Board packets
• Vendor contracts

4. Credit Memo Drafting (AI-Assisted, Not Decisioning)

Quantified ROI / Benefits

• 30–40% reduction in underwriting preparation time
• Increased consistency in credit narratives
• Allows lenders to focus on analysis vs. formatting

Supporting Research

• Research shows that augmenting underwriting with data and automation improves efficiency and can enhance credit evaluation accuracy⁴
• AI is most effective when used as a decision-support tool rather than a decision-maker in regulated environments¹

Strategic Value

• Preserves full credit governance and policy compliance
• High ROI without triggering full SR 11-7 model validation

Examples:

• First-draft credit memos
• OCR financial extraction (1)
• Covenant drafting

5. Workflow Automation & Internal Copilots

Quantified ROI / Benefits

• 20–40% reduction in administrative workload
• Significant reduction in “swivel-chair” tasks
• Faster internal reporting and communication cycles

Supporting Research

• Generative AI improves productivity in routine knowledge work tasks by 20–60% depending on function³
• Treasury research highlights workflow automation as a key operational efficiency driver¹

Strategic Value

• Improves employee productivity and satisfaction
• No major process redesign required

Examples:

• Meeting summaries
• Email routing
• Auto-task creation
• Executive summaries

Uses Microsoft 365 Copilot or similar.

6. Fraud Detection (Vendor AI Add-Ons)

Quantified ROI / Benefits

• 10–25% reduction in fraud losses (typical vendor benchmarks)
• Improved early detection and fewer false positives
• Faster fraud investigation and resolution

Supporting Research

• OCC risk reports emphasize rising fraud threats and the importance of advanced detection tools⁵
• Machine learning models significantly improve fraud detection accuracy compared to traditional rule-based systems⁶

Strategic Value

• Vendor-managed → minimal implementation burden
• Direct financial impact (loss avoidance)

Examples:

• Card behavior analytics
• ACH/wire anomaly detection
• Identity verification

Usually plug-and-play with existing platforms.

7. Compliance Support Tools

Quantified ROI / Benefits

• 30–50% reduction in time spent reviewing regulatory updates
• Faster policy updates and audit preparation
• Reduced consulting and legal costs

Supporting Research

• AI is increasingly used for regulatory monitoring, compliance documentation, and reporting automation¹
• NIST AI RMF supports AI use for risk identification and governance efficiency⁷

Strategic Value

• Strengthens compliance posture
• Particularly valuable for smaller compliance teams

Examples:

• Drafting responses
• Monitoring regulatory changes
• Training content

Highly efficient, low risk.

Summary Table

 Footnotes-Use Cases

1. U.S. Department of the Treasury. Artificial Intelligence in Financial Services (2024).
2. Federal Reserve (Barr, M.). AI and the Future of Banking (2025 remarks).
3. Brynjolfsson, E., Li, D., & Raymond, L. (2023). Generative AI at Work (NBER Working Paper).
4. FinRegLab. The Use of Cash Flow Data in Underwriting Credit (2025).
5. Office of the Comptroller of the Currency. Semiannual Risk Perspective (Fall 2024).
6. Kou, Y., et al. Machine Learning for Financial Fraud Detection: A Review (2025).
7. National Institute of Standards and Technology (NIST). AI Risk Management Framework (AI RMF 1.0) (2023–2024 updates).

KEY RECCOMENDATIONS

For Boards

• Require inventory, governance, and risk frameworks before implementation
• Start with low risk use cases that improve efficiency
• Monitor internal adoption and regulatory expectations quarterly

For Management

Establish a working committee, or have an existing monthly management committee monitor, manage and guide the roll-out and adoption of AI bank wide.

• Use AI first for internal productivity
• Leverage vendor tools rather than building models
• Establish strong data governance early
• Evaluate and improve workflow processes
• Implement human-in-the-loop controls for all AI outputs

For FI Staff

• Treat AI as a drafting and support tool—not a decision system
• Validate and review all outputs
• Report anomalies in AI behavior

V. Conclusion: A Phased, Safe, and Efficient Path Forward

AI is no longer optional for FIs—but full-scale AI deployments are not necessary to begin achieving meaningful gains. The right approach is phased, governed, workflow-light, vendor-leveraged, and supported by appropriate human oversight.

This article provides community based FIs with a roadmap from policy to practice, ensuring:

• Safety and soundness
• Compliance with regulatory expectations
• Improved operational efficiency
• Enhanced customer service
• Better risk and fraud controls
• A scalable foundation for long-term AI maturity

Appendix: Key AI Technologies Defined

Generative AI (GenAI)
AI systems that create new content (text, summaries, code, marketing content) based on learned patterns.

• Example: Drafting credit memos or policies

Machine Learning (ML)
Algorithms that learn from data to identify patterns and make predictions without explicit programming.

• Example: Fraud detection models

Natural Language Processing (NLP)
AI that understands, interprets, and generates human language.

• Example: Chatbots, document summarization

Optical Character Recognition (OCR)
Technology that converts scanned documents or images into structured, machine-readable data.

• Example: Extracting financial statements from PDFs

These technologies are often embedded in vendor platforms already used by FIs, reducing implementation burden.

WAYNE F. PATENAUDE, CFA
Boston, Massachusetts

Wayne is a financial services executive and board director with 30+ years of leadership in banking and strategic finance. As President and CEO of Cambridge Financial Group and Cambridge Savings Bank, a $7B institution, he drove three strategic plans delivering 21% annual earnings growth over more than a decade. He launched Ivy Bank, which secured $850M in deposits, led a community bank acquisition, and built an Asset-Based Lending division exceeding $200M. Wayne has served on nonprofit and civic boards including the Cambridge Chamber of Commerce and the American Red Cross, with a strong focus on governance, leadership, and community impact.

Email Wayne