Payments Modernisation: AI That Grows Global Merchants

December is when payments teams feel every weak spot in their stack. Volume spikes, fraud spikes, chargebacks creep up, and every percentage point of authorization rate suddenly has a real revenue number attached to it.

That’s why “payments modernisation” shouldn’t be treated like a re-platforming project with nicer APIs. For PSPs and acquirers, modernization is about measurable merchant growth: higher approvals, fewer fraud losses, faster payouts, and the ability to expand into new markets without doubling headcount.

A recent sponsor webinar announcement about Payments Modernisation: How PSPs and Acquirers are Accelerating Global Merchant Growth got one thing right even without giving details: the winners in 2026 won’t be the providers with the most features. They’ll be the ones with the best decisioning layer—and increasingly, that decisioning layer is AI.

Payments modernization is a growth strategy, not an IT refresh

Answer first: Payments modernization works when it improves the two numbers merchants obsess over: authorization rate and total cost of acceptance.

A lot of “modernization” programs stall because they focus on replacing components (gateway, switch, fraud tool) without upgrading the system behavior: how transactions are routed, how risk is assessed, how exceptions are handled, and how quickly the provider can adapt to new schemes, wallets, and regulations.

Here’s the stance I’ll take: modern payments infrastructure is an optimization problem. And optimization is where AI belongs.

Modern PSP and acquirer stacks are expected to do all of this at once:

• Support local payment methods across regions (cards, A2A, wallets, BNPL)
• Meet tightening regulatory rules (SCA, network mandates, data residency)
• Reduce fraud and chargebacks while keeping approvals high
• Offer fast settlement and predictable payouts
• Provide clean reporting that makes reconciliation less painful

If your stack can’t learn from outcomes—approved/declined, fraud/not fraud, chargeback/no chargeback, retry success/failure—you’re stuck tuning rules manually while traffic patterns change weekly.

Modernization that doesn’t improve outcomes is just expensive change.

Where AI fits in PSP and acquirer infrastructure

Answer first: The highest-ROI uses of AI in payments modernization are fraud detection, transaction routing, and authorization optimization—because they act on every transaction.

AI isn’t a single product you “add.” In a mature payments platform, it’s a set of models and decision services that sit alongside your core processing. Think of it as a real-time brain attached to your rails.

1) AI fraud detection that doesn’t crush approvals

Fraud teams know the tradeoff: tighter controls reduce losses, but can also increase false declines, which is a quiet revenue killer.

AI-driven fraud detection improves the tradeoff by using more signals than rule-based systems typically can—device data, behavioral patterns, merchant history, network signals, and post-authorization feedback loops.

What I’ve found works best is a layered approach:

• Pre-auth risk scoring: fast, lightweight model to decide approve/challenge/decline
• Adaptive step-up: apply 3DS or additional verification only when it’s likely to help
• Post-auth monitoring: catch account takeover and refund abuse patterns early

This is where modernization matters: if your platform can’t support real-time feature computation and low-latency scoring, you end up with blunt controls. And blunt controls show up as churn in the merchant portfolio.

2) Smart transaction routing (and smarter retries)

Answer first: AI-based routing raises approvals by picking the best path for that transaction, not the best average path.

Routing used to mean “primary acquirer, backup acquirer.” That’s not enough for global merchants dealing with:

• regional issuer quirks
• scheme changes
• currency and cross-border effects
• varying SCA enforcement
• fluctuating processor performance

AI routing systems can incorporate historical outcomes by issuer BIN range, country corridor, MCC, amount bands, time of day, and authentication method. The model’s job is simple: maximize the probability of approval at the lowest cost, subject to rules.

Retries are the underrated part. A smart platform learns:

• when to retry vs. when it’s pointless
• how long to wait
• whether to adjust parameters (merchant descriptor, 3DS path, token vs PAN, local acquiring)

Done well, this becomes “self-healing payments”: outages and issuer weirdness hurt less because the system adapts.

3) Authorization optimization beyond “more 3DS”

Answer first: Better approvals come from treating auth as a data problem: clean inputs, correct authentication, and issuer-friendly transaction patterns.

AI supports authorization optimization in practical ways:

• Field quality monitoring: catch missing/dirty data that drives issuer declines
• Dynamic 3DS strategy: decide when frictionless 3DS will help vs. when it adds abandonment
• Network tokenization uplift: prioritize tokens where they increase approvals and reduce fraud
• Soft decline handling: automatically route soft declines into the right recovery flow

A common myth is that approvals are “mostly issuer behavior.” Issuers do drive the final decision, but your data hygiene and decisioning absolutely change outcomes.

The modernization roadmap that actually works (and why)

Answer first: The safest path is to modernize in layers—data foundation first, decisioning second, processing changes last.

Teams often attempt the opposite: replace the processor, then figure out data and optimization later. That’s backwards if the campaign goal is global merchant growth.

Here’s a pragmatic roadmap PSPs and acquirers can apply without freezing delivery for a year.

Step 1: Build a transaction + outcome data loop

Your AI is only as good as your feedback. At minimum, capture and unify:

• auth requests and responses (including reason codes)
• 3DS events and outcomes
• fraud labels (confirmed fraud, suspected fraud, chargebacks)
• refunds and disputes
• routing path taken and processor response time

If you’re missing labels, start with what you have. Even decline reason codes + retry outcomes can support useful routing models.

Step 2: Introduce a decisioning layer you can update weekly

Modernization isn’t just microservices; it’s change velocity.

Create a decision service that can be adjusted without redeploying your entire platform:

• configurable policies (hard constraints, compliance rules)
• model-driven scoring (risk, routing, retry)
• experiment framework (A/B tests on routing and authentication)

If you can’t run controlled experiments, you’ll never know whether a model improved approvals or just shifted losses downstream.

Step 3: Automate exception handling

People don’t scale, systems do.

Payments ops pain typically lives in exceptions:

• reconciliation breaks
• payout delays
• duplicated refunds
• chargeback evidence workflows
• merchant configuration drift across regions

AI can help here too—especially with anomaly detection and document classification—but the real modernization win is building straight-through processing so ops teams focus on true edge cases.

Step 4: Expand rails without creating a spaghetti stack

Global merchant growth requires more rails, but each new method adds operational and risk complexity.

A modern architecture separates:

• rails adapters (cards, A2A, wallets)
• risk and routing decisioning (AI + policies)
• ledgering and reconciliation

When these are coupled, every expansion becomes a custom project. When they’re separated, expansion becomes configuration plus a tested adapter.

What “global merchant growth” looks like in numbers

Answer first: Merchant growth accelerates when PSPs and acquirers can show measurable improvements in approvals, fraud loss rate, and time-to-market.

Even small improvements compound at scale. Consider a merchant processing $50M/month:

• A 1% authorization lift is $500k/month in additional approved volume.
• A 10 bps reduction in fraud loss is $50k/month preserved.
• A 20% reduction in chargeback rate can reduce dispute ops load and scheme monitoring risk.

Those are directional examples, but they’re grounded in how the unit economics work. This is why I’m bullish on AI in payments infrastructure: when it’s deployed at the decision points, the impact is arithmetic.

A practical mini case (typical pattern I’ve seen):

• Merchant expands to two new regions and sees approvals drop due to cross-border routing.
• PSP introduces local acquiring where possible and deploys AI routing by corridor and issuer behavior.
• Fraud rises with new traffic; risk team adds adaptive step-up so 3DS is applied where it reduces loss without crushing conversion.

The end state isn’t “more tools.” It’s one coordinated system that balances growth and risk.

“People also ask” questions (answered directly)

Is AI in payments modernization mainly about fraud?

No. Fraud detection is the most common entry point, but routing and authorization optimization often deliver faster approval gains because they influence every transaction.

Do PSPs and acquirers need real-time AI to see results?

For routing and fraud scoring, yes—milliseconds matter. For reconciliation and anomaly detection, near-real-time (minutes) can still deliver big ops savings.

What’s the biggest blocker to AI-driven payments optimization?

Data quality and feedback loops. If you can’t reliably connect an authorization attempt to its outcomes (approval, dispute, fraud label, retry), model performance will plateau.

How do you avoid AI increasing false declines?

Use multi-stage decisioning (score → step-up → review) and continuously measure false decline proxies such as customer complaints, repeat attempts, and issuer approval differences by segment.

What to listen for in a payments modernization webinar

Answer first: The best modernization discussions focus on outcomes, architecture choices, and operating model—not vendor features.

If you’re evaluating a webinar like Payments Modernisation: How PSPs and Acquirers are Accelerating Global Merchant Growth, I’d listen for specifics in four areas:

1. Where AI is deployed (pre-auth, routing, retries, post-auth disputes)
2. How they measure uplift (A/B tests, holdouts, segmented reporting)
3. Latency and resiliency (what happens during issuer or processor incidents)
4. How quickly they can adapt (policy updates, model retraining cadence, governance)

If the talk stays at the “modern platform” level without touching these, it’s marketing. If it shows real operating mechanics, it’s worth your time.

Next steps: Modernize around decisions, not rails

Payments modernization is about making better decisions at scale. AI is the most practical way to do that—especially for PSPs and acquirers supporting global merchants across messy real-world conditions.

If you’re building your 2026 roadmap, pick one high-volume decision point (fraud scoring, smart routing, or soft-decline recovery), instrument it end-to-end, and run controlled tests. You’ll get clearer results—and you’ll build the data loop you need for everything else in the AI in Payments & Fintech Infrastructure series.

Where do you see the most friction in your current stack: fraud losses, false declines, or cross-border approval rates? That answer usually tells you where modernization should start.