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Unifying the fraud context layer

A 1% improvement in fraud detection saves hundreds of millions a year. European regulators are fining institutions millions for missing fraud safeguard deadlines. And over 42% of fraud attempts are now AI-driven.

The pressure is real. The window to act is closing.

This session is built for fraud engineering and risk platform teams who are struggling to assemble a fraud stack from five vendors and want to see what production-grade fraud infrastructure actually looks like. Presentation, a live demo, and a reference architecture you can take back to your team are just some of the things you should expect from this session.

What you will learn:
  • Why the model is never the bottleneck and what the context layer failure actually costs you
  • How to stream live transaction data into your fraud agents at runtime so they score on what is happening now, not last night's batch
  • How ML teams and fraud agents can operate on the same live, consistent data without maintaining separate pipelines
  • How to retain cross-session fraud signals so coordinated attacks that look clean in isolation get caught before losses compound
  • How leading financial institutions are hitting the 300ms payment network decisioning window at petabyte scale
Redis

Mehul Modha

Sr. Solution Architect

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