Why AI in Payments Requires Structured Card Network (Scheme) Knowledge

We were speaking with a bank recently about how they are building AI automation into their payments capabilities.

One issue kept coming up that most institutions underestimate: network knowledge.

Everyone is exploring AI for operations, fraud, and customer experience.
But Visa and Mastercard continue to publish a constant flow of rule changes, fee updates, monitoring thresholds, and technical specifications.

And during that conversation, something became clear:

AI cannot operate effectively on top of fragmented network information.
It needs structure before it can add value.

Most institutions still receive network updates through:
• unstructured PDFs
• email threads
• shared folders
• manual spreadsheets
• individual subject-matter experts

AI struggles in that environment.
Not because the models are weak, but because the underlying knowledge is inconsistent.



The turning point in our discussion was simple:

AI is only as strong as the network context you give it.

Once network updates are captured, interpreted, and structured in a central knowledge base, AI becomes far more useful.
It can:
• summarize changes accurately
• explain impact across teams
• identify the right owner automatically
• reduce manual review cycles
• surface risk earlier
• support more predictable implementation

AI shifts from being an experiment to something operationally meaningful.



This is the missing layer in many digital transformation plans.

Not more automation.
Not more dashboards.
But a clean, reliable foundation of network rules, thresholds, fees, and operational requirements that AI can actually reason over.

The organizations that combine structured network knowledge with AI are already seeing:
• fewer surprises during rule changes
• smoother implementation work
• clearer accountability
• lower operational risk
• faster understanding of complex updates

AI becomes a multiplier, not a patch.



When you think about your own AI roadmap, a simple question helps clarify readiness:

Do you have the structured network foundation that AI needs to work reliably?

Steven Leitman

Steven Leitman is Managing Partner of Consulting Resource Group (CRG), a payments consulting and platform firm that helps issuers, acquirers, and BIN sponsors improve profitability through network (scheme) fee optimization, interchange economics, and disciplined cost governance. CRG's Payment Economics practice (CardTraq) includes a suite of platforms designed to manage Visa and Mastercard network fees, interchange performance, and ongoing network rule changes. CRG works with some of the largest global issuers and acquirers.

His work focuses on the economics beneath card programs: Visa and Mastercard network (scheme) fees, pricing structures, interchange qualification, and the hidden cost drivers that materially impact P&L. A core theme is making network compliance measurable and continuous, with data structures, governance models, and platforms that provide ongoing visibility into compliance-driven cost, risk, and fee leakage rather than relying on one-off interpretation exercises.

Steven brings hands-on experience from senior roles at Visa, American Express, and Deloitte Strategy. He publishes regularly on LinkedIn on Visa and Mastercard fee changes, interchange reform, and network compliance.

https://www.linkedin.com/in/steven-leitman/
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Mastercard Network (Scheme) Fees Are Growing Faster Than Payments Volume. Here’s What That Means for Issuers and Acquirers