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Building Plaid's ML Fraud Detection Application, Signal


Renault Young

Renault Young
Software Engineer,
ML Infrastructure

Kevin Stumpf

Kevin Stumpf

Watch this virtual fireside chat to explore best practices in designing and deploying ML fraud detection applications. 

Our guest speaker, Renault Young, a Software Engineer at Plaid, joined Kevin Stumpf, CTO and Co-founder of Tecton, to do a deep dive into Renault's experiences in designing and developing Plaid’s ML infrastructure for Signal, Plaid's payment fraud detection and prevention application.

In this chat, Renault and Kevin discussed:

  • How the Plaid team set up the data foundations they needed to start building an ML platform
  • The technical challenges they faced in developing the Signal application, including how they solved for out-of-order transaction data for billions of bank transactions around the world
  • How they use On-Demand Feature Views to look for patterns in transaction data to prevent fraudulent transactions in real time
  • The benefits derived from this new architecture—such as improved cost management, and better workspace and access controls—along with a look into future optimizations 

Watch On-Demand