Last year, we sat in on a sales call with a mid-sized lending startup based out of Bengaluru. They were pitching a large NBFC for a co-lending partnership. The product was solid, the unit economics made sense, the team was sharp. They lost the deal anyway — not on pricing, not on compliance, but because a competitor could show real-time fraud scoring on every loan application and this startup still relied on manual underwriting checks with a rules engine from 2019.
That’s the pattern we at Speqto Technologies keep seeing across BFSI and fintech startups in India right now. Founders assume they’re losing deals because of price, brand, or funding stage. In reality, they’re losing because the buyer’s due diligence checklist has quietly changed, and AI capability is now on it — even when nobody says “AI” out loud in the RFP.
The Procurement Conversation Has Already Shifted
Banks, insurers, and NBFCs doing vendor evaluations today ask very specific questions: How fast can you flag a suspicious transaction? Can your system auto-triage claims before a human touches them? What’s your model for credit risk beyond bureau scores? These aren’t “nice to have” questions anymore — they’re disqualifiers.
We worked with a claims-processing startup serving mid-sized insurers. Their manual SLA was 48 hours for first-level claim triage. A competitor using a simple document-classification and anomaly-detection layer brought that down to under 4 hours. The insurer didn’t care about the underlying tech stack — they cared about the SLA number on the contract. Our client lost two consecutive RFPs before they came to us to rebuild their claims pipeline with an AI-assisted triage layer. They won the next deal with the same insurer, same pricing, just a faster, smarter pipeline.
Why Founders Delay AI Adoption
In our conversations with 30+ BFSI-focused founders over the past two years, the delay almost always comes down to three reasons:
- “AI is a phase 2 problem.” Founders want to nail the core product first and treat intelligence layers as a later upgrade. But in BFSI, the core product increasingly includes decisioning speed and accuracy — it’s not separable anymore.
- Cost perception is outdated. Many founders still think AI adoption means hiring a data science team and building models from scratch. It rarely does. Most of the fraud detection, document parsing, and credit scoring use cases we build for clients run on fine-tuned existing models plus clean data pipelines — a few weeks of work, not a six-month R&D project.
- No one on the team owns it. Product teams assume engineering will handle it; engineering assumes it’s a product decision. It falls through the cracks until a lost deal forces the conversation.
What “Losing the Deal” Actually Looks Like
It rarely looks like a straight rejection. It looks like:
- A pilot that goes well technically but doesn’t convert because the enterprise buyer compares your manual fraud review process against a competitor’s automated one.
- A due diligence call where the buyer’s risk team asks “how do you handle edge cases at scale” and your answer is “we have analysts review flagged cases” — which signals you can’t scale past a certain transaction volume.
- Investors asking why your cost-to-serve per customer isn’t improving as you scale, because your operations are still linear with headcount instead of flattening with automation.
We saw this with a payments startup that was raising a Series A. Their burn rate looked fine at current volume, but when investors modeled 5x scale, the support and reconciliation headcount required made the unit economics fall apart. We helped them build an AI-based reconciliation layer that cut manual matching from 6 FTEs to 1.5 at the same transaction volume. That single change became a slide in their next pitch deck — and a reason the round closed faster.
What Early Adoption Actually Buys You
It’s not about having an “AI feature” to market. It changes three things in a sales or funding conversation:
- Speed becomes a proof point, not a promise. You can show live numbers — turnaround time, fraud catch rate, false positive rate — instead of roadmap slides.
- Scale stops looking scary. Enterprise buyers and investors both want to see that your cost structure doesn’t break as volume grows.
- You control the narrative in diligence calls. Instead of answering “how do you plan to use AI,” you’re walking them through what’s already running in production.
Where to Start, Practically
You don’t need a six-person ML team. Start with the one process in your business that’s manual, repetitive, and directly visible to your buyer or investor — underwriting checks, document verification, claims triage, transaction monitoring. Fix that one thing properly, with clean data and a model suited to your actual volume, before trying to “AI-enable” the whole product.
At Speqto Technologies, this is usually the first conversation we have with BFSI and fintech founders — not “what AI should we build,” but “which manual process is costing you deals right now.” That question alone has changed the outcome of more RFPs and funding rounds than any amount of feature-building ever did.
