Payments Modernisation: AI Playbook for PSP Growth

Payments modernisation isn’t a “nice to have” project anymore. If you’re a PSP or an acquirer, it’s the difference between approving more good transactions (revenue) and drowning in declines, fraud, and operational cost (margin erosion).

Here’s the uncomfortable truth I keep seeing: a lot of “modernisation” programmes are really just re-platforming. Teams move workloads, refresh APIs, swap a gateway—and the merchant experience barely changes. The winners are doing something else: they’re modernising the stack and putting AI in the decision layer that drives routing, risk, and reliability.

This post is part of our AI in Payments & Fintech Infrastructure series. It’s built around the themes you’d expect from a “Payments Modernisation” webinar aimed at global merchant growth—PSPs, acquirers, infrastructure choices—but it goes further: what to modernise first, where AI actually pays for itself, and what “global scale” really means in 2025.

Payments modernisation is really about approval rate and reliability

Answer first: Modern payments infrastructure is measured by outcomes—higher approval rates, lower fraud losses, faster settlement ops, and fewer outages—not by whether you run “microservices.”

Global merchants don’t care how elegant your architecture diagrams are. They care that:

• Their authorisation rate is stable across regions, issuers, and local payment methods
• Fraud controls don’t block good customers during peak season
• Checkout latency stays low even when traffic spikes
• Disputes, chargebacks, and reconciliation don’t become a back-office fire

December is a useful stress test. Holiday traffic spikes, fraud attempts rise, cross-border volumes climb, and issuer behaviour gets weird (more step-ups, more conservative declines). If your platform can’t maintain performance during peak volatility, merchants will find a provider that can.

The hidden cost of “declines you could’ve saved”

Declines aren’t just lost revenue; they create second-order problems:

• Customers retry with different cards (extra interchange and processing cost)
• Support tickets increase
• Merchants add friction (hurting conversion)
• Fraud models get noisier because behaviour changes under friction

Modernisation should be scoped around reducing avoidable declines while keeping fraud and chargebacks in check. That’s exactly where AI is most effective—when you give it the right data and tight operational constraints.

PSPs and acquirers win or lose on decisioning at the edge

Answer first: The modern PSP/acquirer isn’t just moving money; it’s making thousands of real-time decisions per second—risk, routing, retries, authentication, and exception handling.

Traditional rule-based systems still matter, but they hit limits quickly in global commerce:

• Rules don’t generalise well when you add new geographies and payment methods
• Fraud patterns mutate faster than manual tuning cycles
• Issuer behaviour varies by corridor, time of day, device, and merchant category

AI adds value when it improves decision quality under uncertainty. In payments, that means AI should be evaluated against a short list of measurable outcomes:

1. Net approval uplift (approved good transactions minus approved fraud)
2. Fraud loss reduction (including first-party fraud and account takeover)
3. Chargeback rate reduction (especially friendly fraud categories)
4. Latency impact (milliseconds matter at checkout)
5. Operational load reduction (fewer manual reviews, fewer false positives)

Routing is where infrastructure meets revenue

Multi-acquirer and multi-processor setups are now common for merchants with scale. But having multiple rails isn’t the same as using them intelligently.

AI-driven transaction routing can consider signals like:

• Issuer response patterns by BIN range and corridor
• Historical approval rates by MCC, currency, and amount band
• Soft decline reasons and optimal retry timing
• Network token availability and token performance
• 3DS step-up probability and abandonment risk

A practical stance: routing should be treated like a prediction problem with guardrails. You can’t “optimize approvals” in the abstract; you optimize approvals subject to risk, cost, and scheme compliance constraints.

Snippet-worthy: Modern payments routing is a real-time optimization problem, not a static configuration.

Where AI fits in a modern payments stack (and where it doesn’t)

Answer first: AI belongs in the layers that translate data into decisions—fraud detection, authentication strategy, routing, and operations intelligence—and it must be paired with strong controls.

A clean way to think about the stack:

1) Data foundation: unify signals without slowing checkout

You can’t run good models on fragmented data. The baseline requirement is a data layer that can join:

• Transaction events (auth, capture, refund, dispute)
• Device and behavioural signals
• Merchant context (SKU mix, basket size, seasonality)
• Network and issuer response codes
• Risk outcomes (fraud confirmed, chargeback, manual review result)

The trap: teams overbuild a “perfect” lakehouse while fraud and routing decisions remain rule-based. A better approach is incremental: instrument events, standardise schemas, and feed models in stages.

2) Real-time decisioning: fraud + authentication + routing

This is where AI earns its keep.

Fraud detection should combine:

• Supervised models for known fraud patterns
• Anomaly detection for novel attack spikes
• Graph features (shared devices, emails, IP clusters) for organised rings

Authentication strategy (including when and how to invoke step-up) is often missed. AI can predict:

• Probability of issuer approval with and without 3DS
• Likelihood of customer abandonment after challenge
• Risk-adjusted value of routing to a different acquirer vs adding friction

Routing can be model-driven with hard safety rails:

• Don’t route outside scheme/region constraints
• Don’t increase cost beyond an agreed threshold unless approval uplift justifies it
• Throttle experimentation under incident conditions

3) Operations intelligence: stop fighting fires

Modernisation isn’t finished when the auth is approved. AI can reduce operational drag in:

• Dispute triage: predicting which chargebacks are winnable and what evidence matters
• Reconciliation: matching exceptions and detecting settlement anomalies
• Incident detection: spotting processor/issuer degradation faster than dashboards alone

My opinion: ops automation is the fastest “quiet win” because it improves margins without changing checkout.

A practical modernisation roadmap for 2026 planning

Answer first: If you’re budgeting and scoping for 2026 right now, prioritise initiatives that move approval rate and risk outcomes within one or two quarters—not multi-year platform rewrites.

Here’s a roadmap that works for many PSPs and acquirers supporting global merchant growth.

Phase 1: Fix observability and feedback loops (30–60 days)

If you can’t measure it, you can’t improve it. Start with:

• Unified event tracking across auth, 3DS, retries, disputes
• A consistent decline taxonomy (soft vs hard, issuer vs network)
• “Golden metrics” dashboards: approval rate, fraud rate, chargeback rate, latency
• A feedback pipeline so confirmed fraud/disputes retrain models

Phase 2: Deploy AI where you can prove ROI (60–120 days)

Pick one or two use cases with clean measurement:

• Soft decline recovery: model-driven retry logic and timing
• AI-assisted fraud scoring: reduce false positives while maintaining fraud loss targets
• Routing optimization: start with one region or corridor, then expand

Define success like a grown-up:

• Approval uplift (e.g., +30 to +80 basis points) with no increase in fraud losses
• False positive reduction (e.g., -10% manual reviews) with stable chargeback rate
• Latency budget adherence (e.g., p95 decisioning under 100–150ms)

Phase 3: Modernise for resilience, not fashion (quarterly)

Infrastructure matters most when it prevents outages and enables rapid change.

Focus on:

• Active-active routing or failover paths for critical dependencies
• Feature flags for risk/routing experiments
• Model governance (versioning, rollback, monitoring drift)
• Data minimisation and privacy controls across regions

Snippet-worthy: Resilience is a growth feature—because downtime is a conversion killer.

People also ask: the uncomfortable questions about AI in payments

“Will AI increase fraud if we chase approvals?”

Answer: It will if you optimise the wrong metric. The target should be net approval uplift (good approvals minus bad approvals), with explicit fraud-loss and chargeback constraints.

“Do we need generative AI for payments modernisation?”

Answer: For routing and fraud decisions, predictive ML usually does the heavy lifting. Generative AI is most useful for ops workflows—dispute narratives, agent assist, log summarisation, runbooks—where it speeds humans up without deciding whether money moves.

“What’s the biggest failure mode you see?”

Answer: Teams implement models without production discipline. If you don’t have monitoring, backtesting, and rollback, you’ll end up turning the model off during the first incident—and it won’t come back.

What to listen for in a payments modernisation webinar

Answer first: The best sessions don’t talk about “digital transformation.” They talk about approval rates, risk outcomes, architecture trade-offs, and operating models.

If you’re evaluating a webinar focused on PSPs, acquirers, and global merchant growth, I’d listen for:

• How they quantify approval uplift (by region, issuer, payment method)
• How they separate signal from noise in fraud and disputes
• What their migration path looks like (parallel runs, shadow models, feature flags)
• How they handle data residency and privacy across markets
• How they align incentives between risk, product, and revenue teams

A strong presenter will also acknowledge trade-offs plainly: approval optimization can raise cost; fraud controls can add friction; resilience requires redundancy. Modernisation is managing those tensions with better tooling and better decisioning.

Next steps: build your 2026 plan around AI-driven infrastructure

Payments modernisation should feel practical: fewer outages, fewer avoidable declines, lower fraud losses, and happier merchants. AI helps when it’s used as a controlled decision layer—routing, fraud detection, and ops intelligence—built on instrumentation you trust.

If you’re mapping priorities for 2026, do this exercise: list your top three sources of lost revenue (avoidable declines, checkout latency, fraud-driven friction) and tie each to one measurable AI or infrastructure change you can ship in a quarter.

What would happen to your merchant growth targets if you raised net approvals by just 50 basis points across your highest-volume corridors—without increasing fraud loss?