AI in Payments: Why EBAday 2026 Matters

The most useful payments conversations in 2025 aren’t about “whether AI works” anymore. They’re about where AI belongs in the payments stack, who owns the risk, and how you prove outcomes without breaking compliance.

That’s why EBAday 2026 (16–17 June 2026, Bella Centre, Copenhagen) is worth treating as more than another industry date on the calendar. The event’s positioning—an annual summit for payments and transaction banking executives—maps directly to the decisions that will define the next 18–24 months: fraud and scam strategy, instant payments resilience, liquidity and routing optimization, and the infrastructure required to operationalize AI safely.

This post is part of our “AI in Payments & Fintech Infrastructure” series. My stance: the winners won’t be the firms with the flashiest models. They’ll be the ones who turn AI into reliable, auditable, production-grade plumbing across risk, ops, and treasury.

EBAday 2026 is really about operational AI, not demos

EBAday 2026 matters because it sits at the intersection of payments policy, bank-grade infrastructure, and commercial reality. That intersection is exactly where most AI initiatives stumble.

In many institutions, AI still lives in innovation teams. Payments leaders, meanwhile, live in a world of SLAs, scheme rules, exceptions handling, reconciliations, and regulator-ready evidence. The gap between those worlds is expensive.

Here’s what a “serious” AI agenda looks like in payments and transaction banking:

• Decisioning with accountability: You need to explain why a payment was delayed, routed, or blocked.
• Controls that scale: You can’t grow review headcount at the same rate as payment volume.
• Latency and resilience: Models must work inside real-time rails and survive outages.
• Cross-border complexity: Sanctions, local regulations, FX, and corridor-specific fraud patterns.

EBAday is where the people who own those constraints meet. That’s the point.

The myth: “AI strategy” is a single project

Most companies get this wrong: they treat AI like a project with a start and end date.

In payments, AI is better treated as a capability layer—something you embed into fraud operations, transaction routing, customer servicing, and treasury workflows. It’s closer to building an internal platform than deploying a tool.

If you’re attending EBAday 2026, go in with a platform mindset. Ask vendors and peers:

• Where does your model sit in the flow—pre-auth, post-auth, pre-clearing, post-clearing?
• What happens when the model is uncertain—do you step-up auth, hold, or route differently?
• How do you monitor drift and bias without shipping customer data everywhere?

AI fraud detection has shifted from “fraud” to “fraud + scams + mule networks”

The most urgent AI application in payments isn’t simply card fraud scoring anymore. It’s scam prevention and mule detection across channels—faster payments, cards, wires, and even internal book transfers.

Why? Because criminals have become operationally sophisticated:

• They run social engineering playbooks that bypass traditional authentication.
• They fragment funds through mule accounts and rapid onward transfers.
• They test limits across rails and institutions, then scale what works.

AI helps here, but only when it’s built for the job.

What “good” looks like in AI fraud detection (2026 reality check)

A useful fraud/scam stack combines three layers:

1. Behavioral signals: device, session, interaction patterns, payee creation behavior.
2. Network intelligence: graph analytics to surface mule clusters and shared attributes.
3. Decision orchestration: rules + ML + human review routed by confidence and risk.

A snippet-worthy truth: AI doesn’t replace controls; it changes which controls you can afford to run at scale.

If EBAday 2026 sessions touch fraud, the questions that separate mature programs from pilot projects are practical:

• Are you optimizing for loss reduction, false positives, or customer friction—and how do you measure all three?
• Can your approach detect first-party fraud and authorized push payment scams, not just stolen credentials?
• What’s your plan for case management when AI flags a payment—who acts, how fast, and with what evidence?

AI-driven transaction routing is becoming a treasury and resilience tool

AI in transaction routing is often pitched as cost optimization. That’s true—but it’s not the main story.

The bigger value is resilience and predictability:

• Predicting scheme performance and failure rates by corridor and time of day
• Choosing routes that minimize repair rates and investigation workload
• Reducing liquidity surprises by forecasting outflows and return patterns

Where routing AI actually pays off

Routing decisions touch multiple cost centers at once:

• Direct fees (scheme, correspondent, FX)
• Operational cost (repairs, investigations, exception queues)
• Customer experience cost (late payroll, supplier disruption, angry treasury teams)
• Risk cost (routing into weaker controls or unstable corridors)

A practical way to frame this for your 2026 planning: optimize for total cost of payment ownership, not “cheapest path.”

If you’re building or buying AI routing capabilities, insist on these features:

• Explainable routing reasons that an ops team can defend
• Fallback logic that’s deterministic when AI confidence drops
• A/B testing so you can prove improvement without gambling core flows

The infrastructure shift: AI is forcing payments teams to standardize data

Here’s the thing about AI in payments: the model is rarely the bottleneck.

The bottleneck is almost always data consistency across rails and systems—different identifiers, mismatched timestamps, missing counterparty metadata, and fragmented case notes. If you can’t tie together the story of a transaction, your AI will be confident for the wrong reasons.

What to standardize before you “scale AI”

If you want 2026 to be the year your AI programs stop stalling, prioritize a few unglamorous moves:

• Canonical payment event model: one internal schema for statuses, returns, rejects, and investigations.
• Entity resolution: consistent customer, account, and merchant IDs across channels.
• Feature store discipline: defined, governed features so models don’t drift into “spreadsheet science.”
• Label quality: clear definitions for fraud, scam, error, and customer dispute outcomes.

A line I’ve found useful with stakeholders: “If we don’t trust the data, we can’t trust the model—or the controls built on it.”

EBAday 2026 is a good forcing function to sanity-check your foundations. If your team can’t answer “where do labels come from?” without a 20-minute detour, you’ve found your real roadmap.

Safe AI in payments: governance that doesn’t kill delivery

Payments and transaction banking teams don’t have the luxury of “move fast and apologize.” If your AI blocks legitimate transactions, you create real-world harm: missed rent payments, delayed payroll, broken supply chains.

But “governance” can’t be a 9-month committee process either. The right approach is thin, explicit guardrails.

A workable governance checklist for AI in payments

Use this as a discussion starter with risk, compliance, and engineering:

1. Model purpose statement: one paragraph on what it does and what it must never do.
2. Human override paths: who can reverse decisions, with what logging.
3. Audit-ready evidence: inputs, features used, score, threshold, and action recorded per decision.
4. Monitoring metrics: drift, false positives, loss rate, and customer friction tracked weekly.
5. Red-team testing: simulate scam scripts and adversarial behavior, not just historical replay.

Snippet for your internal deck: “If we can’t audit it, we can’t automate it.”

This is also where GenAI needs special care. Using large language models to summarize cases, draft SAR narratives, or assist customer support can be valuable—but only if you treat outputs as assistive, with tight access controls and clear boundaries.

What to do before EBAday 2026 (so you don’t waste the trip)

If your goal is to turn EBAday into pipeline, partnerships, or an actionable 12-month plan, show up prepared. Copenhagen in June is great; flying home with vague notes isn’t.

A 30-day prep plan for payments and transaction banking leaders

• Map your top 3 pain points: fraud/scams, exception volume, routing costs, liquidity forecasting, onboarding friction—pick the ones with measurable pain.
• Baseline your metrics (pick 4–6): fraud loss rate, scam loss rate, false positive rate, average investigation time, repair rate, routing success rate, and STP rate.
• List your “decision points”: where a system or person decides to approve/hold/reject/route. AI only helps where decisions exist.
• Define your data gaps: missing fields, inconsistent identifiers, delayed signals.

Then at the event, push every conversation toward proof:

• “Show me how you reduced false positives by a specific percent in a production bank.”
• “What’s your latency at peak volume?”
• “How do you handle model drift after a scheme rule change?”

What vendors should be ready to answer

If you’re evaluating providers at EBAday 2026, these questions cut through marketing quickly:

• What’s the implementation path: weeks, months, or quarters—and which teams are needed?
• Do you support on-prem, private cloud, and hybrid realities?
• How do you provide explainability for both ops and compliance?
• What’s your approach to data minimization and sensitive fields?

Where this fits in the “AI in Payments & Fintech Infrastructure” series

This series has a consistent theme: AI isn’t magic; it’s infrastructure. When it’s treated like infrastructure, it improves security, routing, and operational efficiency in ways you can measure and defend.

EBAday 2026 is a timely checkpoint because it’s close enough to plan for, and far enough out to build properly. The payments teams that arrive with a clear baseline, a data plan, and a governance model will come away with decisions—not just inspiration.

If you’re heading to Copenhagen in June 2026, go with one mission: identify the one AI capability you can productionize in the next two quarters (fraud/scam detection, routing optimization, or operations automation) and the one data standardization move that makes everything else easier.

What would happen to your fraud losses, ops queues, and customer experience if your payments platform made fewer guesses—and more audited decisions?