Stablecoin Payments at Scale Need AI-First Controls

Bitso Business says its stablecoin-powered payments platform is on track to process $82 billion in annualized total payment volume (TPV) in 2025, with its Mexico payout rails reaching $15.6 billion annualized volume through local real-time payments. Those numbers aren’t just bragging rights. They’re a signal that stablecoins have crossed a threshold: they’re no longer a “crypto thing,” they’re increasingly a payments infrastructure thing.

Most companies still talk about stablecoins like a treasury experiment or a niche cross-border option. That’s outdated. At $82B annualized, stablecoin flows start to look like what they really are: a high-throughput transaction business where risk controls, monitoring, routing, and reconciliation matter as much as the asset itself.

This is where the “AI in Payments & Fintech Infrastructure” series gets practical. Stablecoin payments and real-time rails compress settlement time, shrink the window for manual review, and raise the cost of operational mistakes. If you’re building or buying infrastructure in 2026 planning cycles, the question isn’t whether stablecoins will show up in your flows—it’s whether your systems can manage them safely at scale.

Why Bitso’s TPV is a stablecoin adoption signal (not a headline)

Answer first: When a platform can annualize $82B in stablecoin TPV, it proves that stablecoins are being used for repeatable, production-grade payment use cases—especially cross-border payouts and business disbursements.

Stablecoins win in B2B payments for a simple reason: they’re a programmable value layer that can move across borders without waiting for correspondent banking chains to wake up. In practice, stablecoins often function as the “bridge asset” between two local endpoints:

• A sender funds in local currency (or a major currency).
• Value moves via stablecoin over blockchain rails.
• A recipient gets paid out through local rails (bank transfer, instant payment, wallet payout).

That model fits what Bitso Business is describing: a stablecoin-powered platform paired with local real-time payment infrastructure in Mexico.

Stablecoins are becoming the “middle mile” of payments

Card networks and domestic ACH-style systems are excellent at the last mile inside one country. Cross-border is the messy middle: multiple banks, FX spreads, compliance hops, and unpredictable settlement. Stablecoins increasingly serve as the middle-mile transport, while domestic systems handle endpoints.

The point isn’t ideology. It’s operations:

• Fewer intermediaries to coordinate
• Faster confirmation of funds movement
• Cleaner programmability for reconciliation and status updates

But speed creates pressure. When settlement compresses, your controls have to be automated.

Real-time payments + stablecoins: faster money, faster fraud

Answer first: Real-time payments and stablecoin settlement reduce delay-based controls, so platforms need AI-driven fraud detection and transaction monitoring that works in seconds, not hours.

Bitso’s Mexico number—$15.6B annualized—is a reminder that growth isn’t only on blockchain rails. It’s also happening on instant payments and real-time payout rails. Combine the two, and you get a stack that can move value quickly end-to-end.

That’s great for customer experience and working capital. It’s also a magnet for:

• Account takeover and social engineering
• Mule activity and rapid cash-out
• Refund and dispute abuse (where applicable)
• Sanctions/AML evasion attempts via structuring and velocity

Old-school controls assume time exists: batch reviews, end-of-day reconciliation, manual exception handling. Real-time rails remove that buffer.

The practical risk shift: from “catch later” to “block now”

In real-time systems, the cost of a false negative (missing fraud) rises sharply because funds can be irrecoverable once paid out. At the same time, the cost of a false positive (blocking a good payment) also rises because instant payments are often tied to urgent payouts—gig wages, supplier settlement, remittances, insurance claims.

This is exactly the kind of trade-off AI is good at handling—if it’s implemented with the right data and governance.

Where AI actually helps in stablecoin payment platforms

Answer first: AI is most valuable in stablecoin and real-time payment systems when it’s used for (1) risk scoring in milliseconds, (2) smart routing across rails, and (3) automated reconciliation and exception handling.

AI doesn’t replace compliance or payments ops. It changes what’s possible under tight time constraints.

1) AI-driven fraud detection and transaction monitoring

Stablecoin payment platforms sit at a complicated intersection: blockchain activity (on-chain signals), customer behavior (platform signals), and bank/wallet endpoints (off-chain signals). The strongest monitoring systems fuse all three.

High-performing models typically score risk using features like:

• Velocity: bursts of payouts to new beneficiaries, rapid repeats, time-of-day anomalies
• Graph behavior: shared identifiers across accounts, circular flows, clustering around mule hubs
• Counterparty risk: new addresses, weak history, known exposure patterns
• Device and session signals: unusual login paths, emulator use, IP anomalies
• Payment purpose consistency: mismatches between historical patterns and current memo/reference data

A useful rule: if your team can explain a fraud pattern in a postmortem, you can probably encode it as features and let AI detect it earlier.

2) Intelligent transaction routing across fiat and crypto rails

As stablecoins become “just another rail,” routing becomes a competitive advantage. The cheapest path isn’t always the best path. You care about:

• Total fees (network + FX + payout)
• Success rates and drop-offs
• Confirmation and payout times
• Corridor-specific risk (fraud, returns, compliance)
• Liquidity availability at the endpoint

AI can power dynamic routing decisions by learning from historical outcomes—approving the rail combination most likely to succeed quickly at the lowest all-in cost.

A stablecoin payment stack that can’t route intelligently will lose on margin or lose on reliability. Sometimes both.

3) Reconciliation and exceptions at high TPV

At $82B annualized TPV, reconciliation becomes a product feature. Customers don’t only want “paid.” They want status certainty: what happened, when it happened, and why it failed.

AI helps by:

• Matching on-chain transfers to off-chain payout events (even with imperfect references)
• Flagging anomalies (duplicate payouts, partial fills, strange fee spikes)
• Automating exception classification (“insufficient liquidity,” “invalid account,” “compliance hold”)
• Generating ops-ready work queues with next-best actions

In December, this matters even more. Year-end volumes rise, payout schedules get tight, and ops teams are already stretched with closes, audits, and holiday staffing gaps. Automation isn’t a nice-to-have; it’s how you avoid backlog.

What scaling to tens of billions in TPV forces you to build

Answer first: At stablecoin and real-time payments scale, platforms must treat risk, liquidity, and observability as core infrastructure—not add-ons.

Here are the non-negotiables I look for when a team says they’re ready to scale stablecoin payments.

Observability: “real-time” needs real monitoring

You need dashboards and alerts that answer, in minutes:

• What’s our approval rate right now?
• Where are failures clustering (corridor, bank, chain, asset, beneficiary type)?
• Are confirmation times drifting?
• Are payout queues building?
• Did fraud losses spike for a specific segment?

AI can help prioritize alerts (reduce noise), but you still need disciplined instrumentation. If you can’t see it, you can’t control it.

Liquidity and treasury: stablecoins don’t remove funding problems

Stablecoins can reduce friction, but liquidity still matters:

• You need inventory in the right stablecoin(s)
• You need fiat liquidity for payout endpoints
• You need rules for rebalancing and hedging

AI helps forecast liquidity needs by corridor and customer segment, using seasonality (yes, December spikes are predictable) and leading indicators like queue growth.

Compliance: speed doesn’t reduce responsibility

Stablecoin usage doesn’t exempt you from AML expectations. It often increases scrutiny because flows can move quickly and cross jurisdictions.

AI supports compliance by:

• Detecting structuring and evasive behavior patterns
• Improving sanctions screening context (entity resolution, alias clustering)
• Prioritizing investigations by risk and expected impact

But governance matters: model transparency, audit trails, and human escalation paths are part of the product.

“People also ask” (and what I tell teams)

Are stablecoin payments cheaper than traditional cross-border?

Often yes—especially on corridors with high correspondent fees or slow settlement. But the honest answer is: it depends on routing, liquidity, and payout costs. You don’t win on unit economics by default; you win by operating the stack well.

Do stablecoins make settlement instant?

On-chain transfer can be fast, but end-to-end speed depends on payout rails, compliance checks, and beneficiary readiness. The best systems optimize the whole workflow, not just the chain leg.

What’s the biggest operational risk at high TPV?

Exceptions and fraud scaling faster than headcount. When volume grows, edge cases grow. AI is how you keep exception rates flat while TPV rises.

What to do next if you’re building stablecoin or real-time payout flows

Stablecoin payments at tens of billions in TPV are now a visible reality, and Bitso Business’s reported run-rate is a clean proof point. The interesting question is what happens next: more corridors, more real-time endpoints, and more pressure on risk and reliability.

If you’re evaluating stablecoin payment infrastructure (or adding real-time payouts in markets like Mexico), start with three concrete steps:

1. Measure your true real-time risk window. How many seconds do you have to stop a bad payout? Design controls around that number, not around legacy batch assumptions.
2. Unify signals across on-chain and off-chain systems. Separate dashboards create separate truths—and fraud teams exploit gaps between them.
3. Prioritize AI where speed matters most: real-time scoring, routing, and exception automation. If a use case doesn’t require seconds-level decisions, keep it simpler.

This series is about AI in payments & fintech infrastructure for a reason: the winners won’t be the teams that simply “add stablecoins.” They’ll be the teams that can run high-throughput, high-trust systems where money moves fast—and control keeps up. If stablecoins are becoming the payment middle mile, what would it take for your stack to treat them as safely as cards or ACH?