RBI Licence: What It Unlocks for Cross-Border Payments

Cross-border payments don’t fail because teams lack ambition. They fail because compliance, fraud risk, FX, and settlement complexity compound faster than most fintech stacks can handle.

That’s why the recent news that Unlimit has received an RBI cross-border licence (as reported in the original RSS item, though the source page was access-restricted) matters beyond a single company milestone. An RBI approval is a signal that a provider can operate inside one of the world’s most demanding payments environments—and that’s increasingly where the next wave of global fintech infrastructure is being built.

This post is part of our “AI in Payments & Fintech Infrastructure” series, so I’m going to take a clear stance: cross-border success in 2026 won’t come from adding more payment rails—it’ll come from using AI to operate those rails safely, provably, and at scale. RBI licensing is the permission slip; the operating model is the real work.

What an RBI cross-border licence actually changes

An RBI cross-border licence changes one thing immediately: it formalizes who is allowed to touch regulated parts of the flow in India—and therefore who can offer “direct” service versus who must rely on intermediaries.

For fintech buyers (PSPs, marketplaces, payroll platforms, SaaS platforms paying global contractors), this matters because the shape of your integration and risk model changes depending on whether your provider can legally and operationally handle:

• Cross-border remittances / pay-ins / pay-outs tied to India
• KYC/AML and reporting obligations aligned with RBI expectations
• Settlement and reconciliation workflows that meet local requirements
• FX handling and transparency rules (where applicable)

Why licensing is now a product feature

A few years ago, licensing was seen as “legal paperwork.” That view is outdated. Licensing is product functionality because it determines:

1. How many hops your money takes (and how many parties add fees and latency)
2. Who owns the risk controls (you, your provider, or a chain of subcontractors)
3. How fast you can ship new corridors without rebuilding compliance each time

In practical terms: fewer intermediaries usually means cleaner exception handling, more consistent SLAs, and better data quality for fraud and reconciliation.

The hidden cost of cross-border: exceptions, not transactions

Cross-border payments look simple on slide decks: sender → rail → recipient. Reality is a mess of exceptions—and exceptions are where margin and trust go to die.

The biggest operational drains I see in cross-border programs are:

• Name mismatches (transliteration, initials, ordering, spacing)
• Sanctions and watchlist false positives (especially for common names)
• Purpose-of-payment and documentation gaps (varies by corridor)
• Return and recall handling with inconsistent reason codes
• Reconciliation breakage across PSPs, banks, and local partners

A useful industry benchmark: SWIFT has publicly discussed that a large share of payment failures are driven by poor or incomplete data (often cited around the “single digit to low double digit” range in various industry discussions). Whether your program sees 1% or 8% exception rates, the unit economics change dramatically when you start managing exceptions at scale.

RBI-regulated corridors raise the bar on data discipline

India-linked flows often require stronger data rigor. That’s not a complaint; it’s a reality. Regulatory expectations force better hygiene, which benefits the ecosystem when done right.

But it also means providers need more than rule-based checks. You need systems that can:

• infer intent from messy inputs,
• detect anomalies early,
• and route transactions to the right handling path.

That’s where AI becomes infrastructure, not a feature.

Where AI fits: compliance, fraud, and routing that can prove itself

AI helps cross-border payments scale when it’s applied to three specific jobs: (1) data quality and identity confidence, (2) risk decisions, and (3) transaction routing. If you can’t explain how your models support those jobs, you’re probably “doing AI” as a demo, not as a system.

1) AI for KYC/AML: reducing false positives without weakening controls

The goal isn’t to “approve more.” The goal is to approve correctly with less manual review.

AI can help by:

• Entity resolution: matching the same person/business across spelling variants and document types
• Risk scoring: combining behavioral signals, device intelligence, network patterns, and historical outcomes
• Alert triage: ranking AML alerts by likelihood and potential severity

A good outcome is measurable: fewer Level-1 reviews, faster clearance times, and lower investigator workload per 1,000 transactions.

A payments team that can cut manual reviews by 30% without increasing loss rates doesn’t just save cost—it buys back launch speed.

2) AI for fraud detection in cross-border flows

Cross-border fraud isn’t just card fraud exported to new geographies. It includes:

• mule networks moving funds across corridors,
• synthetic identities exploiting onboarding gaps,
• account takeover paired with fast cash-out,
• refund/chargeback abuse in merchant-led remittance use cases.

AI-driven fraud detection works best when models are trained on confirmed outcomes and supported by a strong feedback loop:

1. transaction → decision
2. investigation / customer confirmation
3. outcome label (fraud / not fraud)
4. model update and policy refinement

If your provider can’t describe that loop, you should assume the model will drift.

3) AI for transaction routing and payment optimization

Cross-border payment routing is often treated as static: pick a partner, send the payment. That’s a missed opportunity.

AI can optimize routing by learning which path is most likely to succeed based on:

• corridor and currency pair
• beneficiary bank behavior
• time-of-day / holiday calendars
• recent failure patterns
• compliance friction likelihood
• expected settlement time and total cost

The best routing systems don’t just optimize for price. They optimize for completion probability and time-to-settlement—because a “cheap” route that fails is the most expensive route you can choose.

From approval to execution: what changes for fintech operators

An RBI cross-border licence is a milestone, but operators should care about what it enables in day-to-day execution. Here’s what tends to change when a provider matures into regulated scale.

Faster corridor launches, if the compliance architecture is modular

Regulated expansion only helps if the provider has a modular compliance design:

• reusable KYC components
• configurable transaction monitoring policies
• corridor-specific rule packs
• auditable decision logging

If every corridor requires bespoke engineering, your “global” roadmap will stall.

Better auditability becomes a sales advantage

By late 2025, procurement questions have shifted. It’s no longer “Do you have an AML policy?” It’s:

• Can you show model governance (versioning, testing, drift monitoring)?
• Can you demonstrate explainability for adverse actions?
• Can you provide traceable logs for routing and screening decisions?

AI in payments is under more scrutiny, not less. The winners will treat auditability as a first-class engineering requirement.

Operational resilience matters more during seasonal peaks

This timing (December) is a reminder: cross-border volumes spike during seasonal commerce and year-end payroll cycles. Licensing and infrastructure maturity show up when:

• payment investigations surge,
• support queues back up,
• and teams need consistent handling across time zones.

A strong provider combines compliance with operational tooling: case management, reason-code normalization, replayable event logs, and clear remediation paths.

A practical checklist: what to ask your cross-border provider in 2026

If you’re evaluating a provider connected to India corridors—or any regulated cross-border corridor—use questions that expose whether AI and compliance are real capabilities or just marketing copy.

Compliance and licensing

• Which regulated entities in the flow are you using, and who is the regulated principal?
• What are your data retention and audit log commitments?
• How do you handle regulatory reporting and change management when rules update?

AI governance (non-negotiable)

• What model types are in use (rules, supervised ML, graph models), and where?
• How do you monitor drift and performance over time?
• Can you provide reason codes or explanations for holds/rejections?
• How is human review integrated (approval thresholds, escalation policies)?

Routing and performance

• What’s your measured completion rate per corridor (and definition of “completion”)?
• What’s your median and p95 settlement time?
• How do you optimize routing when a partner degrades?

Exceptions and reconciliation

• How do you standardize failure reasons across rails?
• What’s your reconciliation approach: event-based, ledger-based, both?
• Can we get real-time webhooks for state changes and investigations?

If you can’t get crisp answers, you’re buying uncertainty.

What this RBI licence signals for global fintech infrastructure

RBI approvals for cross-border capabilities—whether for Unlimit or any other provider—signal a broader shift: regulators are shaping the architecture of global payments, and the best fintech infrastructure companies are building to that reality instead of trying to route around it.

My view is simple: cross-border payments are becoming a discipline of regulated systems engineering. The companies that win will be the ones who treat compliance and AI as a single operating layer—where every decision is logged, explainable, and continuously improved.

If you’re building products that move money across borders, the real question isn’t “Can we add more corridors?” It’s: Can we scale corridors without scaling risk and manual operations at the same rate?

If you want help pressure-testing your cross-border stack—fraud controls, AI governance, routing strategy, and compliance workflows—bring your current flow diagram and your exception data. We’ll get to the truth quickly.