Use Cases Track

Finding Bad Acorns

Within fintech catching fraudsters is one of the primary opportunities for us to use streaming applications to apply ML models in real-time. This talk will be a review of our journey to bring fraud decisioning to our tellers at Capital One using Kafka, Flink and AWS Lambda. We will share our learnings and experiences to common problems such as custom windowing, breaking down a monolith app to small queryable state apps, feature engineering with Jython, dealing with back pressure from combining two disparate streams, model/feature validation in a regulatory environment, and running Flink jobs on Kubernetes.

Authors

Andrew Gao
Software Engineer Capital One
Andrew Gao

Andrew is a Software Engineer with a focus in the real-time fraud decisioning space. Most recently Andrew has been working with his team to create a Kubernetes-based streaming platform following the Kappa architecture model for Capital One Bank.

Jeff Sharpe
Senior Software Engineer Capital One
Jeff Sharpe

Jeff is a senior software engineer working for Capital One in Virginia. He’s been an engineer for almost 18 years, with major projects spanning five different languages. Though he began his work on kernel drivers and web applications, he’s been repeatedly drawn into high volume, high throughput data processing projects.

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