Real-time payment fraud detection
The problem
Card fraud is a vast, adaptive, multi-trillion-dollar problem; rule-based systems can’t keep pace with evolving patterns and generate costly false declines.
The AI approach
Decision Intelligence scores transactions in real time. The 2024 “Pro” upgrade uses a proprietary generative-AI/transformer model (with graph machine learning) trained on ~125 billion annual transactions, assessing the relationships between entities to judge whether a transaction fits a cardholder’s behaviour — in ~50 milliseconds.
Evidence it works
Mastercard reports average fraud-detection improvements of ~20% (and up to 300% in some cases), false-positive reductions of over 85% in its own analysis, and — via a separate 2024 generative-AI tool — doubling the rate of compromised-card detection.
What “good” looks like
Higher fraud catch rates and fewer false declines (the false-positive number matters as much as the catch rate), with sub-100ms scoring at network scale.
Feasibility & cost shape
Network-scale capability built on enormous data and sustained investment (Mastercard cites $7bn+ over five years); most institutions consume it as a service.
A strong example of why data scale and continuous learning beat static rules in adversarial domains. We’d judge any such tool on the false-positive trade-off, not the catch rate alone.
Based on publicly reported information about the Mastercard (Decision Intelligence / Decision Intelligence Pro) work.
This is an industry example included for illustration. It is not a Leia Intelligence project, and no client of ours is implied. Figures are as publicly reported by the original parties.
Sources: CNBC · PYMNTS · Mastercard press releases (Feb & May 2024) · TechInformed