Payments Modernization: AI Playbook for PSP Growth

Global merchant growth isn’t being held back by demand. It’s being held back by payments plumbing.

If you’re a PSP or acquirer trying to support merchants selling across borders—especially heading into the year-end peak and post-holiday returns cycle—modernization isn’t a nice-to-have. It’s the difference between approving a legitimate cardholder in São Paulo at 8 p.m. local time, or declining them and watching the merchant lose the sale.

Here’s the stance I’ll take: payments modernization without AI is incomplete modernization. You can upgrade rails, add payment methods, and re-platform your stack—and still bleed margin and authorization rate if you can’t make smart, real-time decisions on fraud, routing, and exceptions.

This post is part of the AI in Payments & Fintech Infrastructure series, and it focuses on how PSPs and acquirers can modernize in a way that directly accelerates global merchant growth: higher approvals, lower fraud, fewer outages, and better unit economics.

Payments modernization is really about decisioning at scale

Modern payments infrastructure isn’t defined by one big migration. It’s defined by thousands of micro-decisions made per second—across risk, routing, authentication, and reconciliation.

When merchants expand globally, complexity multiplies fast:

• More issuers with different approval behaviors
• More payment methods (cards, wallets, A2A, BNPL)
• More regulatory regimes (SCA, data residency, sanctions)
• More fraud patterns and more false positives
• More edge cases (partial captures, split shipments, returns)

Modernization succeeds when a PSP can make these decisions quickly and consistently, without hardcoding every rule. That’s where AI earns its keep.

The real KPI: authorization rate net of fraud and cost

Many teams optimize one metric at a time—approvals, fraud rate, cost per transaction, chargebacks. Global growth requires balancing all of them at once.

A practical “modernization KPI” I like is:

Maximize approved volume while minimizing fraud losses and processing cost per approved transaction.

AI helps because it can optimize across competing objectives—especially when you feed it the right signals (issuer response codes, device intelligence, behavioral patterns, and routing outcomes).

Why global merchant growth breaks legacy PSP stacks

Legacy payment stacks were built for a world where you processed mostly domestic card payments, updated rules monthly, and reconciled exceptions by hand.

That’s not global commerce in 2025.

The holiday season makes this painfully obvious: traffic spikes, fraud spikes, and customer support tickets spike. Merchants want you to keep approval rates stable while everyone else is melting.

Common failure points I see in PSPs and acquirers

1) Rigid routing rules Static routing (e.g., “always send Visa to Acquirer A”) ignores real-time issuer behavior, regional outages, and network congestion.

2) Fraud tools that aren’t connected to payment orchestration If risk decisions live in a silo, you’ll either block good customers (false declines) or approve bad ones (missed fraud), because the system can’t adapt per route, per market.

3) Authentication done as a checkbox Treating 3DS/SCA as a binary step misses the nuance: when to step up, which 3DS version/flow, and how it affects conversion in different geographies.

4) Data that’s too fragmented to learn from Modernization fails when you can’t answer basic questions like: “Which issuer BIN ranges are underperforming on which routes at which times?”

AI doesn’t fix a broken data foundation—but it forces you to build one.

AI-driven fraud detection: the fastest path to higher approvals

If you’re serious about global merchant growth, fraud detection isn’t just loss prevention—it’s conversion strategy. The biggest hidden cost for merchants expanding internationally is false declines.

AI-based fraud detection improves outcomes in three specific ways:

1) Better separation of fraud vs. unfamiliar behavior

Cross-border customers often look “weird” to rule-based systems: new device, different IP geography, unusual purchase sizes, gifting behaviors, freight forwarding addresses.

Machine learning models can learn patterns of legitimacy (not just patterns of fraud), reducing false positives.

2) Adaptive models during peak periods

Fraud patterns shift rapidly during high-volume seasons. AI models can adapt faster than manual rule updates—if you have strong feedback loops (chargebacks, representment outcomes, issuer responses).

3) Smarter step-up authentication

Instead of challenging everyone (and hurting conversion), AI helps decide:

• When to frictionlessly approve
• When to step up with 3DS/SCA
• When to decline outright

Done well, you get a cleaner funnel: fewer challenges for good users, more friction for risky sessions.

Snippet-worthy truth: If your fraud stack can’t explain why it declined a customer, it can’t be trusted to scale globally.

Practical example: reducing false declines without raising fraud

A PSP supporting a marketplace expanding into LATAM might see higher declines from issuers sensitive to cross-border risk. A blended approach works well:

• Use ML scoring to segment transactions into low/medium/high risk
• Auto-approve low risk
• Step-up medium risk with tailored 3DS (frictionless-first)
• Decline or hold high risk for review (merchant-dependent)

The modernization win is measurable: more approvals with a controlled chargeback rate—and less time spent chasing exceptions.

Intelligent payment routing: AI that pays for itself

Routing is where modernization becomes margin.

Most PSPs talk about routing like it’s a technical feature. Merchants experience it as: “Do I get paid, quickly, with high approval rates, at a reasonable cost?”

What “AI payment routing” should actually mean

AI routing isn’t “pick the cheapest acquirer.” It’s multi-objective optimization across:

• Authorization uplift (issuer acceptance probability)
• Total cost (interchange impact, network fees, acquirer fees)
• Latency and timeouts (conversion killer)
• Resilience (failover when a route degrades)
• Risk posture (fraud and dispute exposure by route)

A mature routing engine uses real-time features like:

• Issuer response codes by BIN/region
• Historical approval performance by MCC and amount bands
• Time-of-day and day-of-week effects
• Soft decline patterns and retry logic outcomes
• Outage signals and SLA degradation

A simple routing upgrade that often lifts approvals

One of the most reliable improvements is smart retries:

1. Detect a soft decline vs. hard decline correctly
2. Retry with a different message format, descriptor, or 3DS step-up
3. If needed, reroute to an alternate acquirer with proven performance for that issuer segment

This is where AI helps: it learns which retries are worth doing, and which just add cost and latency.

Memorable one-liner: The best routing decision is the one you don’t make twice.

Modernizing the PSP stack: the infrastructure patterns that matter

“Modernization” gets framed as re-platforming, but the winning pattern is more specific: build a payments operating system where intelligence is a shared service.

The modern architecture: composable, observable, and feedback-driven

If you’re upgrading infrastructure to support AI, prioritize these building blocks:

• Event-driven processing: publish transaction events and outcomes in near real time
• Unified feature store: consistent risk/routing features across services
• Decision APIs: risk, routing, and authentication exposed as callable services
• Observability by merchant and corridor: dashboards that show approval and fraud by country, issuer, payment method
• Human-in-the-loop tooling: fast rule overrides, reason codes, and audit trails

AI needs clean inputs and fast feedback. Infrastructure modernization provides both.

Data you need to capture (and teams often miss)

To make AI useful for PSPs and acquirers, don’t stop at “approved/declined.” Capture:

• Decline reason and issuer response codes
• 3DS frictionless vs. challenge outcomes
• Latency per hop (gateway, risk, acquirer, network)
• Retry attempts and their outcomes
• Chargeback reason codes and representment results
• Refund/return behavior by merchant segment

If you can’t measure these reliably, your models will be blind—and your merchants will feel it.

People Also Ask: modernization + AI for PSPs and acquirers

What’s the fastest modernization win for a PSP supporting global merchants?

Start with AI-assisted fraud + step-up authentication. Reducing false declines typically produces a faster revenue impact than a full acquiring expansion.

How does AI improve cross-border authorization rates?

AI improves authorization rates by predicting issuer acceptance and adjusting routing, authentication, and retry strategies in real time.

Will AI routing increase costs due to more retries?

Not if you govern it correctly. The goal is fewer, smarter retries—and only when expected uplift exceeds added cost and latency.

Do merchants care about AI, or outcomes?

They care about outcomes: approvals, fraud, payout speed, dispute handling, and uptime. AI is valuable when it changes those numbers.

A practical modernization roadmap (that won’t stall for 18 months)

Most companies get this wrong by aiming for a “big bang” rebuild. A better approach is phased modernization where each phase funds the next.

1. Instrument everything (30–60 days): unify logs, reason codes, and performance metrics by corridor.
2. Deploy AI-assisted risk (60–120 days): focus on false decline reduction and step-up strategy.
3. Add intelligent routing (90–180 days): start with top corridors and top issuers, then expand.
4. Operationalize learning (ongoing): model monitoring, drift detection, and merchant-level controls.

If you’re running a PSP or acquiring platform, I’d insist on one rule: every model must have a rollback plan and a business owner.

Where this is going in 2026: payments ops becomes predictive

The next step in the AI in Payments & Fintech Infrastructure series is one I’m seeing more leaders prioritize: predictive payments operations.

Instead of reacting to disputes, outages, and approval drops, AI systems will:

• Flag issuer degradation before merchants notice
• Forecast fraud spikes by corridor during seasonal peaks
• Recommend routing changes with expected uplift and cost impact
• Auto-generate dispute evidence packs from transaction context

That’s what real modernization looks like: fewer fire drills, more controlled growth.

If you’re modernizing payments infrastructure to accelerate global merchant growth, focus on the intelligence layer: AI-driven fraud detection, smart authentication, and routing optimization. Then build the data foundation that keeps it honest.

Where do you see the biggest drag on global expansion right now—false declines, fraud, or routing cost?