CoreCard

CoreCard operates as a card processing infrastructure provider, meaning fraud losses ripple directly to the issuer clients it serves — banks and fintechs that depend on CoreCard's decisioning accuracy to protect their cardholders. Legacy rule-based fraud controls created a compounding problem: overly broad rules generated high false positive rates that declined legitimate transactions, damaging cardholder experience and merchant relationships. Simultaneously, more sophisticated fraud patterns — account takeover, card-not-present attacks, synthetic identity use — were evading static rule thresholds entirely. The combination of rising fraud losses for issuer clients and unnecessary friction for genuine customers made the existing approach untenable at production scale.

CoreCard integrated Feedzai's RiskOps platform directly into its transaction processing pipeline to replace and augment rule-based controls with machine learning-driven decisioning. The platform applies machine learning and predictive analytics to evaluate each transaction in real time — modeling behavioral baselines per cardholder, analyzing contextual signals such as device, location, and merchant category, and scoring fraud risk in milliseconds before authorization is returned. This embedded integration meant no change to CoreCard's issuer-client interfaces; Feedzai's models operated within the existing processing flow. The system was designed to differentiate between genuine fraud and legitimate transactions that merely appear unusual — reducing both fraud losses and unnecessary declines through a single scoring layer that adapts as fraud patterns evolve.

Following the Feedzai RiskOps deployment, CoreCard achieved a 64% reduction in fraud losses across its issuer client base — the headline outcome of shifting from static rules to adaptive ML models. Fraud decisions are now returned in milliseconds, keeping authorization latency within issuer and network SLA requirements. Beyond the fraud reduction figure, the implementation produced meaningful improvements in the cardholder experience: fewer legitimate transactions were declined due to false positives, reducing friction for genuine customers and lowering dispute and chargeback volumes for issuer clients. The dual improvement — less fraud and fewer false declines — reflects the core value proposition of behavioral ML over threshold-based rules.

• Middleware processors carry fraud risk on behalf of clients — a 64% reduction in losses at the infrastructure layer compounds across every issuer and cardholder in the network, making the ROI case stronger than single-institution deployments.
• Millisecond decisioning is non-negotiable in payment rails — any AI system integrated into a card authorization path must meet network latency requirements or it will be bypassed in production.
• False positive reduction is as important as fraud detection — measure model performance on legitimate declines, not just fraud catch rate, to capture the full business impact.
• Behavioral baselines require transaction history to mature — plan for a model warm-up period before expecting peak detection performance on newly onboarded issuer portfolios.