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AI in Fintech: Where the Payments Sector Will Hire Next

Dilip Asbe, the MD and CEO of India's National Payments Corporation of India (NPCI), told TechCrunch on June 27 that artificial intelligence will be central to the next phase of digital payments expansion — not as a side experiment, but as the core mechanism for reaching the next half-billion users on the Unified Payments Interface (UPI) platform.

NPCI operates UPI, which currently processes more than 750 million daily transactions and is targeting one billion, according to TechCrunch's reporting. That gap — roughly 250 million additional daily transactions — is where Asbe says AI earns its place.

The use cases he cited are not abstract. NPCI has already deployed FIMI, a payments-focused language model that handles disputes and mandate cancellations and now serves more than one million users. Beyond that, Asbe pointed to fraud detection, credit distribution to users and merchants who have digital footprints, and voice-based multilingual onboarding as priority areas where AI will do the heavy lifting. He also noted that India's fintech ecosystem has a distinct opportunity to build small language models trained on local datasets, describing the goal as making models "sharp, specific, and as deterministic as possible," per The Next Web's coverage.

The market context is significant. PhonePe and Google Pay together control approximately 80% of UPI transaction volume. BHIM, NPCI's own app, sits at around 1% market share. That concentration means the pressure to innovate through AI falls hardest on the infrastructure layer — on companies building the rails, compliance tooling, fraud systems, and credit pipes underneath consumer apps — rather than solely on the dominant consumer-facing players.

What this means for job seekers

When the operator of a payments network processing hundreds of millions of daily transactions says AI is its primary growth lever, the hiring signal runs much deeper than "AI engineers wanted." Reviewing that landscape, we find the real opportunity sits in adjacent functions that become critical when a traditionally conservative sector digitizes at speed.

Fraud and risk roles are the most immediate beneficiary. AI-powered fraud detection at scale requires analysts who understand both machine learning outputs and regulatory obligations — a combination that compliance-first institutions value and that pure AI labs rarely train for. If you have data skills and any background in finance or risk, fintech fraud teams are a concrete target.

Credit and data roles follow closely. Distributing credit to users with digital footprints — rather than traditional credit histories — is a data modeling problem. Fintech lenders and payment processors expanding into credit need analysts who can build and audit those models, especially under regulatory scrutiny.

Product and compliance positions round out the picture. As payments networks add agentic commerce features and multilingual voice interfaces, product managers and compliance specialists who understand how AI systems interact with financial regulation become hard to backfill quickly. These are not roles that require building models; they require knowing enough about how models work to write clear requirements and catch failure modes before regulators do.

The pattern here mirrors what we covered in our analysis of how to navigate job searching in the AI era — the winners are not always those who build AI, but those who can operate confidently inside systems that run on it. Fintech is one of the clearest examples of a sector where that positioning translates directly to hiring demand. If your background touches data, risk, product, or compliance, a sector going AI-first at the infrastructure level is worth adding to your search list now.