How to Evaluate AI Vendors for Lending Operations

The Vendor Landscape is Noisy

Every technology vendor in the lending space now claims to offer "AI-powered" solutions. The problem for mid-market lenders: most of these claims are either overstated, irrelevant to your specific workflows, or require 12+ months of implementation before delivering value.

After helping dozens of lending operations evaluate and deploy AI, here's the framework we use to separate signal from noise.

The Five Questions That Matter

1. Does it solve your specific workflow problem?

The most common mistake: buying a platform that solves a general problem instead of your specific one. A "document AI" product that works great for mortgage documents may be useless for MCA bank statements.

Ask the vendor: "Show me a demo using our actual document types, not your demo data." If they can't do this within a week, they don't have a model trained on your domain.

2. What's the time-to-value?

Enterprise AI platforms often require 6–12 months of implementation. For a mid-market lender, that timeline is unacceptable — you need ROI visible within weeks, not quarters.

Ask the vendor: "When will this be processing our live documents in production?" If the answer involves "Phase 1 discovery" and "Phase 2 configuration" and "Phase 3 go-live," you're looking at a consulting project, not a product deployment.

The right answer is measured in weeks. Anything beyond 8 weeks for initial deployment should raise questions.

3. What happens when it's wrong?

Every AI system makes errors. The question is how the system handles them. Look for:

• Confidence scores on every output so your team knows which results to trust and which to review
• Human-in-the-loop workflows that route low-confidence outputs to manual review
• Error feedback loops that use corrections to improve future accuracy
• Audit trails for every automated decision, especially in regulated workflows

Ask the vendor: "What's your accuracy rate on our document types, and what's the workflow when the model is wrong?" If they quote only accuracy without explaining the error handling, dig deeper.

4. Who owns the data and the model?

This is where many vendors get opaque. Key ownership questions:

• Data residency: Where is your borrower data processed and stored? For lenders with state-level data protection obligations, this matters.
• Model ownership: If you stop paying, do you lose the trained model? Can it be exported?
• Training data: Is your data used to improve models for other customers? For competitive reasons, this may not be acceptable.
• Self-hosting: Can the solution run on your infrastructure if required?

Ask the vendor: "Can I run this on-premise or in my own cloud account?" If the answer is no, understand exactly what data flows through their systems.

5. What's the total cost of ownership?

AI vendor pricing is often structured to look cheap up front and become expensive at scale:

• Per-document pricing that's affordable at 100 documents/month becomes punishing at 10,000
• Seat-based pricing that requires every user who touches the system to have a license
• Implementation fees that exceed the first year of subscription costs
• Training data preparation costs that the vendor doesn't mention until after the contract is signed

Ask the vendor: "What does this cost at 5x our current volume?" The answer reveals whether the pricing model scales with your business or against it.

Red Flags in AI Vendor Evaluations

Watch for these signals during the evaluation process:

• No domain-specific demo — they show generic document processing, not lending documents
• Accuracy claims without methodology — "99% accurate" means nothing without knowing the test set, the document types, and the definition of "accurate"
• No customer references in your vertical — if they can't connect you with a lender who's been using the product for 6+ months, they don't have validated product-market fit
• Sales-led vs. engineering-led conversations — if the technical architect isn't in the room by the second meeting, the product may not be as capable as the sales pitch suggests
• Vague integration timelines — "We integrate with everything" usually means "We have an API and you'll need to build the integration yourself"

The Alternative: Forward Deployed AI

The vendor evaluation framework above applies to product purchases — software you buy, configure, and maintain. There's an alternative model worth considering: forward deployed AI engineering.

In this model, an AI engineer embeds with your operations team and builds custom automation directly into your existing systems. The advantages:

• Fits your exact workflows — no configuration needed because it's built for you
• Deploys in days, not months — weekly shipping cadence
• You own the code — full IP ownership, no vendor lock-in
• Cost scales predictably — monthly subscription, not per-document pricing

The tradeoff: it requires hands-on engineering time, which means it's better suited for lenders who have specific, complex workflows rather than those looking for off-the-shelf solutions.

Making the Decision

Use this simple decision tree:

1. Do you have a well-defined, high-volume workflow? → Evaluate vendors using the five questions above
2. Is your workflow unique or complex enough that off-the-shelf tools don't fit? → Consider forward deployed AI engineering
3. Are you unsure which workflows to automate first? → Start with a readiness assessment before evaluating any vendor

The worst outcome is buying a tool that solves the wrong problem. Spend the time to define the problem clearly before evaluating solutions.