Artificial intelligence is reshaping financial services at a pace few anticipated, yet community banks find themselves caught between AI’s promise and the limitations of their core service providers (CSPs). From fraud detection and credit decisioning to generative AI for compliance automation, the potential is enormous. The challenge, as highlighted in the OCC’s recent Request for Information (RFI), is that the providers community banks depend on are not keeping pace. In this third installment of our series on CCG Catalyst’s perspectives on the OCC’s RFI, I examine AI-specific challenges, how the OCC might help, and where regulatory action could create unintended headwinds.

Large banks and fintechs are deploying machine learning across lending, operations, and customer engagement at scale. Community banks recognize they cannot sit on the sidelines. Yet the concentrated Core Service Provider (CSP) market, where three providers serve over 70% of depository institutions, means that for most community banks, AI adoption is dictated by their provider’s roadmap, not their own ambitions. The ABA’s 2024 Core Platforms Survey reported overall satisfaction of just 3.19 out of 5, with innovation capabilities scoring even lower. AI is rapidly becoming table stakes, and banks that fall behind risk losing customers, talent, and relevance.

Based on the OCC’s RFI data and our own clients’ insights, key challenges in AI implementation, five central themes emerged. Provider shortcomings and pain points primarily concentrated around these issues.

1. Opaque AI implementations. Providers embed AI into fraud monitoring and underwriting without sufficient disclosure on model logic, training data, or governance. Community banks are left relying on black-box systems they cannot validate, creating real compliance exposure under fair lending laws. As generative models introduce new accuracy risks, the transparency gap will only widen.
2. Data access barriers. AI is only as good as the data feeding it. Legacy core systems with proprietary formats, incompatible APIs, and restrictive data policies block banks from harnessing their own customer data for analytics or automation. The data belongs to the bank, but the provider controls the pipes.
3. Talent shortages. Community banks rarely have in-house data science expertise. Providers that do offer AI solutions frequently build them for enterprise-scale clients, leaving smaller banks with tools that are too complex or too expensive to use effectively, and insufficient support to bridge the gap.
4. Regulatory uncertainty. Varying interpretations of model risk management, fair lending, and BSA/AML guidance create hesitation among both providers and banks. Providers move slowly and banks fear supervisory scrutiny. The result is a chilling effect on innovation at exactly the institutions that could benefit most.
5. Vendor dependency. With dominant CSPs controlling the market, community banks have limited leverage to demand AI investment. Switching providers to access better capabilities involves conversion costs. Even when superior AI solutions exist, community banks cannot practically access them.
   
   

We have seen both ends of the spectrum. One community bank partnered with a CSP offering open APIs and modular AI tools, deploying machine learning-based fraud detection within six months at manageable cost. The provider offered sandbox testing and dedicated support, and the result was measurable fraud reduction and improved customer experience.

Conversely, another bank requested AI-enhanced credit decisioning from its core provider and was told the capability was “on the roadmap” with no committed timeline. Third-party alternatives proved prohibitively expensive to integrate with the legacy core. The bank abandoned the initiative and watched fintech competitors capture share with faster, smarter lending. These are not isolated stories. They represent a systemic pattern.

Can the Regulator Help? From my perspective, regulators have several levers to create a more enabling environment. My recommendation:

1. Issue proportionate AI risk management guidance that acknowledges community banks typically use vendor AI tools rather than building proprietary models. Provide clarification what constitutes adequate validation for a $500 million bank versus a multi-billion regional versus a money-center institution.
2. Require provider transparency on AI through its Bank Service Company Act examination authority, pushing CSPs to disclose model governance, bias testing results, and performance metrics.
3. The regulator, specifically the OCC should facilitate collaborative initiatives through programs like Project REACh, convening banks, providers, and AI specialists to develop shared due diligence frameworks and model validation consortia.
4. Publish case studies and range-of-practice documents with anonymized examples of successful AI implementations would give community bankers the practical playbooks they need.
   
   

AI is not a luxury for community banks, I believe we would all agree it is becoming the cost of doing business. Banks that cannot access AI tools through their CSPs will find themselves on the wrong side of an accelerating technology divide. The regulator has a key role in ensuring regulatory frameworks support rather than hinder adoption. But the heavier lift belongs to the CSPs. Providers that treat community banks as partners, investing in open architectures, transparent AI, and proportionate pricing will earn long-term loyalty. Those that offer yesterday’s technology at tomorrow’s prices will face growing pressure from regulators, the market, and their own customers.

CCG Catalyst stands ready to assist community banks in navigating these complexities, from AI strategy development to vendor evaluation and regulatory readiness. Reach out to our team for tailored consulting support. Stay tuned for the next installment in our series Vetting The Promises.