Every AI vendor pitch sounds the same after a while. “End-to-end solutions,” “proven expertise,” “scalable architecture.” If you’re a decision-maker at a bank, NBFC, or fintech, you’ve probably sat through a dozen of these decks and come out with more confusion than clarity.
At Speqto Technologies, we’ve been on both sides of this conversation — pitching our own work and also being brought in to fix projects that other vendors left half-built. One pattern stands out: the companies that got burned rarely asked hard questions upfront. They picked based on a polished deck, a famous logo in the “clients” slide, or a quote that was 20% lower than everyone else’s.
If you’re evaluating an AI development partner for anything touching credit scoring, fraud detection, KYC automation, or customer-facing chat tools, here are the ten questions that actually separate serious teams from the ones who’ll learn on your budget.
1. Have you shipped anything in a regulated environment before?
Building a chatbot for an e-commerce site and building one for a lending platform are not the same job. Ask for a specific example where the vendor had to deal with RBI guidelines, data residency rules, or audit trails. A fintech client of ours came to us after a previous vendor built a fraud-detection model that worked beautifully in testing but had no explainability layer — which made it useless the moment the compliance team asked “why was this transaction flagged?”
2. Who exactly will work on my project — and for how long?
Many agencies sell you a senior architect in the sales call and staff the actual build with two junior engineers who rotate off mid-project. Ask for names, not just roles. Ask what happens if your lead engineer leaves — is there documentation, or does the knowledge walk out the door with them?
3. How do you handle model drift and ongoing monitoring?
A credit underwriting model that performs well at launch can degrade within months as applicant behavior shifts. Ask what monitoring is built in, who gets alerted, and how retraining decisions are made. If the answer is vague or “we’ll revisit post-launch,” that’s a red flag — drift management should be part of the original scope, not an afterthought.
4. Where does our data actually live, and who can access it?
For BFSI clients, this isn’t a nice-to-have — it’s often a regulatory requirement. Ask about data residency, encryption at rest and in transit, and whether any data touches third-party APIs or foreign servers during development or testing. We’ve seen vendors casually route customer PII through OpenAI’s API during prototyping without flagging it to the client — a mistake that can trigger real compliance headaches.
5. Can you show me a failed project and what you learned from it?
This question makes people uncomfortable, which is exactly why it’s useful. Every team that’s done real work has had something go sideways — a model that didn’t generalize, a timeline that slipped, an integration that broke in production. Vendors who say “we’ve never had a project fail” are either lying or haven’t done enough projects to matter.
6. What’s your approach to explainability, not just accuracy?
A 94% accurate model is meaningless to a risk committee if nobody can explain its decisions. Ask specifically how they handle model interpretability — SHAP values, decision trees as a fallback, rule-based overlays, whatever it is. For loan approval or fraud flagging systems, this question often matters more than raw performance metrics.
7. How do you price change requests after the initial build?
Fixed-price contracts look appealing until week six, when your compliance team adds three new requirements and the vendor quotes you double the original budget for “scope change.” Get clarity on this before signing — not as a formality, but as a real negotiated term.
8. What happens to the IP and code if we part ways?
Some agencies build on proprietary internal frameworks that make it painful, if not impossible, to move to another team later. Ask directly: if this relationship ends in a year, do we own the models, the code, the training pipelines — everything?
9. How do you test for bias in models that affect credit or lending decisions?
This is still underasked. A model trained on historical lending data can quietly encode past discriminatory patterns. Ask what bias testing looks like in practice — across gender, geography, income bracket — and whether it’s a one-time check or ongoing.
10. Can I talk to a client who’s been with you for more than a year?
Launch-day references are easy to arrange — everyone’s happy right after go-live. A reference from a client who’s been through at least one renewal cycle tells you much more about how the vendor handles support, bug fixes, and the inevitable friction that comes after the honeymoon period.
The real takeaway
None of these questions are about technology trends or buzzwords. They’re about accountability, transparency, and whether a vendor has actually been in the room when things got hard. At Speqto, we’d rather lose a deal to a client who asks all ten of these than win one from a client who asks none.
If you’re currently evaluating partners for a BFSI or fintech AI project and want a second opinion on a proposal you’ve received, we’re happy to look at it — no pitch attached.
