How to Evaluate AI Vendors Like a Regulator Is Watching (Because They Are)

Start by identifying specific pain points through direct customer and employee feedback. Then map potential solutions against two axes: strategic relevance and business criticality. A digital account opening platform might be business-critical for a bank expanding into new markets but merely convenient for one with stable customer acquisition. The resources you dedicate to vendor evaluation should reflect this reality, not industry hype.

Internal Alignment Matters More Than Vendor Features

Here’s what kills AI implementations: your IT team learns about the new vendor relationship when they receive the contract to review. Or your compliance officer discovers the system processes customer data only after it’s live. Or your branch staff gets no training and actively discourages customers from using the new tool.

Before you contact vendors, get these stakeholders in the same room:

• IT reviews technical architecture and integration requirements
• Operations quantifies actual ROI, not vendor-projected ROI
• Marketing builds a customer adoption strategy with specific metrics
• Compliance identifies regulatory exposure before contracts are signed
• Frontline staff understand what they’re selling and why it matters

This sounds obvious, but many skip these conversations until problems surface. By then, you’ve already committed budget and management attention to a program that half your organization doesn’t support.

Four Questions Your Vendor Should Answer Clearly

Standard TPRM questionnaires weren’t built for AI. You need to probe areas where vendors often provide vague assurances instead of documentation.

1. Data Ownership: What happens to our data inside your system?

Ask directly:

• Does our data train or improve your models?
• Who owns the outputs your system generates?
• What happens to our data if we terminate the contract?
• What happens to our data if you’re acquired?
• If the vendor hedges or avoids specifics, walk away.

2. Explainability and Actionability: Can you defend the AI’s decisions?

Pick a recent decision the AI system made. Ask the vendor to explain exactly why. Not high-level model architecture. Not general principles. Why this specific output for this specific input.

This matters because the Equal Credit Opportunity Act requires banks to provide specific reasons for adverse credit decisions. “Our AI model determined you don’t qualify” violates federal law. You need vendors who can document decision logic at a granular level, not just claim their system is “transparent.”

But it’s confusing what transparency actually requires. As Former-Acting Comptroller of the Currency, Mike Hsu, argues in his work on AI governance, bankers and regulators shouldn’t fixate on interpretability—understanding how a model works internally. They should aim for actionability. When your AI denies a loan or flags fraud, you should show:

• The reasoning steps that led to that conclusion
• Which specific step failed when the system errs
• Logging and monitoring tools that trace decision pathways
• How quickly you can fix problems once identified

Test this during the demo. Bring a real scenario from your operations. Ask them to walk through exactly how their system would handle it and where you’d intervene if something broke. If they can’t show you the decision pathway or explain where you’d diagnose a failure, that’s a dealbreaker.

3. Bias Detection and Mitigation: How do you test for fairness and can we see the results?

Every AI vendor will tell you they test for bias. Almost none will show you the actual testing methodology or results. This matters because algorithmic bias violations are strict liability issues. Your intent doesn’t matter if the outcomes are discriminatory.

Request documentation of:

• Disparate impact testing across demographic groups
• How often they run these tests
• What they found and how they addressed it
• Third-party audits (if they exist)

Pay attention to how they respond. Vendors who take fairness seriously have detailed documentation ready. Vendors who don’t will promise to “look into it” and never follow up.

4. Model Governance: Who’s accountable when systems break?

AI systems fail. Models drift. Data quality degrades. Security vulnerabilities emerge. The question is whether your vendor has actual accountability structures or just incident response documentation that nobody follows.

Ask for specifics:

• Who owns this model at your company (name and title)?
• Who conducts independent validation?
• How do you monitor for performance degradation?
• What’s your escalation protocol when something breaks?
• Show us your change management log for the last six months

If they can’t name the person responsible or show you evidence of active governance, that’s a red flag. It means when something goes wrong, you’ll be dealing with a support ticket system instead of people empowered to fix the problem.

What to Actually Put in the Contract

Standard vendor contracts miss critical AI-specific protections. Add these:

Delivery commitments with teeth: If the vendor promises features by specific dates, put penalty clauses in the contract. Early-stage vendors especially will promise roadmap items to win deals. Make them contractually accountable.

Data portability with technical specs: “You can export your data” is meaningless without format specifications and API access. Define exactly how data export works, what format you receive and how long the vendor must support the transition.

Performance thresholds with remediation rights: If the AI system’s accuracy falls below X%, what happens? Define specific metrics, measurement methodology and your rights to terminate without penalty if performance degrades.

Audit rights that you’ll actually use: Most contracts include audit provisions that remain unused. Define when and how you’ll conduct audits, what you’re allowed to review and what happens if you find problems.

Liability caps that reflect actual risk: Standard vendor contracts limit liability to fees paid. For AI systems handling customer data or making credit decisions, that’s inadequate. Negotiate liability terms that reflect the actual regulatory and reputational risk.