Financial institutions are losing over $190 billion to credit card fraud annually, with half a billion personal records compromised in 2025 alone. The institutions gaining ground on fraud are not doing so with better models — they're winning because their data infrastructure can run those models at scale and in real time. This playbook makes the case that fraud detection is a data layer problem first. It outlines the infrastructure requirements for high-velocity transaction data ingestion, real-time detection pipelines, and AI-readiness. It also addresses the dual pressure of accelerating fraud sophistication and tightening compliance regulations that can cost organizations up to 2% of revenue for non-compliance. Designed to help financial services technology and fraud teams evaluate their current data foundation and chart a path to infrastructure that matches the pace of modern fraud.
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Financial institutions lose over $190 billion annually to credit card fraud — the bottleneck is rarely the model, it's a data infrastructure that cannot ingest, store, and process transaction data fast enough.
A real-time, S3-compatible object storage foundation enables fraud models to run at scale across both cloud and on-premises environments without cost-prohibitive egress or latency penalties.
Compliance regulations now threaten up to 2% of revenue for non-compliance, making AI-ready, unified transaction data infrastructure both a fraud prevention and a regulatory imperative.
Fraud prevention leaders, data infrastructure engineers, and financial services technology executives accountable for detection systems, data architecture, and compliance posture.
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