Right Features, Right Time: Modernizing Real-Time Fraud Feature Serving on Databricks

Fraud does not wait for batch. To block funds from moving, Coinbase models need the right features at the right time—fresh, consistent, and served with predictable low latency.This session explains Coinbase’s Databricks-based feature platform for real-time fraud ML: Real-Time Mode on Structured Streaming; declarative Feature APIs + CI/CD (features as code); AI/agent-assisted migration and feature creation; and Lakebase feature serving targeting p99 <50ms with autoscaling and workload isolation.Early testing reduced typical streaming feature freshness from ~770ms to ~100–200ms (3.8–7.7×) and suggests potential for >90% streaming infra cost reduction and up to >95% p99 freshness improvement. Moving self-built batch feature sets to a managed feature store is also driving an estimated ~25–35% productivity gain by retiring bespoke pipelines and shrinking the on-call surface. Attendees leave with a reference architecture + migration playbook across latency, reliability, and TCO.