Real-Time Fraud Detection: Faster AI Development, Reliable Production

With fraud attacks evolving faster than ever, businesses are finding that off-the-shelf solutions aren't always enough. To stay ahead, teams need the agility to deploy custom detection models using their unique data and domain expertise. Building these capabilities—whether for transaction fraud detection, AML monitoring, or risk decisioning—requires integrating streaming transactions, historical patterns, and third-party signals at millisecond latencies.

Yet even with strong data science teams and promising models, organizations struggle to productionize these advanced fraud detection systems. The challenge lies in building and maintaining production-grade data infrastructure that can scale reliably while meeting strict latency requirements.

Join us to learn how leading organizations like Coinbase, Plaid, and Tide are improving their fraud detection capabilities by modernizing their data infrastructure. Through technical deep-dives and real-world examples, we'll explore:

• Common challenges and best practices for productionizing fraud detection data
• Approaches to unifying batch and streaming data processing
• Methods for ensuring the delivery of fresh features at scale
• Strategies for reducing data engineering complexity without compromising reliability

You'll learn proven approaches for building fraud detection infrastructure that can adapt as quickly as fraud tactics evolve, drawing from examples of organizations that have reduced their model deployment time from months to minutes.