FinTec Buzz | Five Essential Questions Every Community Banker Should Ask Before Saying Yes to AI
Community banks are facing a tension in the boardroom. They know they need to explore and adopt AI. And there’s optimism about the advances that AI can bring to their operations, customer relationships and bottom lines. At the same time, there’s unease about compliance exposure, customer trust and regulatory scrutiny from AI rollouts.
Some companies have adopted new AI applications before the right infrastructure was in place, but they have to reverse course when the technology couldn’t perform as promised.
Companies are looking for ways to build the right infrastructure to fully benefit from the broad power of AI systems. To implement AI systems successfully, start by asking vendors these five questions.
Question 1: Can you explain exactly how this model makes decisions?
A major concern among community bankers is the black box effect, where AI makes decisions, but humans don’t understand how it arrives at those conclusions. There are practical and important reasons for this concern. Regulators require companies to document and defend the decisions made by AI systems. The OCC and FFIEC have indicated that model risk management and explainability are non-negotiable.
If a vendor can’t show you how its model reaches a conclusion, that’s a potential compliance problem. If your AI recommends declining a borrower, you have to be able to explain why in plain language to the customer,your board or an examiner.
Ask vendors whether their outputs can be audited and validated, not just explained in a demo. It may be easy to explain in a sales pitch, but it must stand up to regulatory review.
Internal staff also need to be clear on why the system flags some things and not others. Otherwise, they’ll either ignore the AI recommendations or overrely on it. The black box issue is also a trust problem with customers. Banks that use conversational AI with customers should make clear that they’re talking to AI.
Question 2: How does this AI fit into our existing workflows, and what breaks first?
Most banks stall AI because the transformation feels overwhelming. Or they leap into deploying something their organization isn’t ready for. To avoid this, make sure your vendor can clearly map out your current workflows before introducing anything new.
Ask whether the rollout can be modular. If a vendor wants a full transformation on day one, that’s a red flag. With a phased deployment, you can measure results, catch problems early and build confidence before expanding.
Before introducing AI systems, redesign workflows as needed. If a vendor’s implementation plan drops new technology onto existing processes without asking whether those processes make sense, you’re just automating problems, rather than solving them.
Also consider which staff roles get disrupted, which handoffs disappear, which compliance checkpoints need to be rebuilt. Ensuring that new systems bring minimal disruption to existing workflows is smart risk management for institutions that can’t afford to pause lending or compliance while a new system comes online.
Question 3: What does your data quality and readiness assessment look like?
Data quality is a foundation for an accurate and quality AI system. If a model is trained on fragmented, inconsistent or outdated data, it will produce unreliable outputs, regardless of how sophisticated the underlying technology is. Ask vendors what they actually evaluate before onboarding, not what they assume you have. Assess data quality, infrastructure gaps and process inefficiencies. If the vendor’s evaluation is cursory or skips any of these, you’ll pay for that later.
Many community banks are operating across siloed systems. Customer data, loan data and transaction data don’t talk to each other cleanly. A vendor that doesn’t address this issue upfront is building on a shaky foundation. Fixing those data problems after deployment is significantly harder and more expensive than fixing them before. Ask whether the vendor can help prioritize and remediate data issues as part of its implementation or whether they leave that to you.
Question 4: How have other community banks our size used this?
Data readiness and institutional fit are two sides of the same coin. Community banks aren’t smaller versions of big banks. They have different staffing constraints, often carry more legacy infrastructure and typically lack dedicated technology teams for AI deployments.
Look for vendors that have proven experience with similar institutions.
Legacy systems are a reality for many community banks. Find out whether a vendor’s implementation experience includes working around existing core systems, rather than replacing them.
Staffing capacity is a central piece of a successful AI implementation. A community bank with a lean lending team needs a vendor that has helped similar institutions manage change management, including training, adoption and workflow adjustment, not just the technical deployment. Make sure your vendor of choice understands your world and can therefore anticipate potential problems.
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