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