Stripe builds Shepherd ML feature engineering platform on Chronon to power fraud detection at scale
Stripe's ML feature engineering infrastructure needed to meet strict requirements for both low latency and high feature freshness across massive transaction volumes, and their existing platform could not adequately scale to these demands.
Shepherd's SEPA fraud detection model, with over 200 features, outperforms the previous model and blocks tens of millions of dollars of additional fraud per year, while streaming feature updates achieve p99 freshness of 150ms.
Frequently asked questions
What did this team achieve with this AI workflow?
Shepherd's SEPA fraud detection model, with over 200 features, outperforms the previous model and blocks tens of millions of dollars of additional fraud per year, while streaming feature updates achieve p99 freshness…
What tools did this team use?
Chronon, Shepherd, Flink, Airflow, Spark SQL, Iceberg, Hive.
What results were reported?
Additional fraud blocked per year: tens of millions of dollars; P99 feature freshness: 150ms; SEPA fraud model feature count: over 200; SEPA fraud model performance vs previous model: outperformed our previous model (source-reported, not independently verified).
How is this finance ops AI workflow structured?
Feature definition via API → Offline batch computation → Streaming updates via Flink → Feature serving from KV store → Fraud model inference → Consistency monitoring.