Real Time Fraud Detection Models

Learn how to use streaming and real-time data to optimize your fraud detection models.

Needless to say, identifying and preventing fraud is critical to the financial health of any company processing transactions online. Money laundering, payments fraud, and identity theft are common examples of fraudulent behavior that can stress organizations.

ML models can be very effective in preventing fraud. They can improve fraud detection rates, reduce false positives, and be retrained to identify new fraudulent behavior as fraudsters adapt.

However, fraud models require high-quality data that can be difficult to serve in production. Features typically have to be processed at low latency from streaming and real-time data sources to provide the freshest feature values possible. Building custom streaming pipelines can add weeks or months to project delivery times.

Watch this Tech Talk to learn how to stay ahead of fraudsters and achieve a higher rate of fraud detection Tecton automates low-latency streaming and real-time pipelines, allowing data teams to build and deploy new features in hours instead of weeks. Tecton enables you to ingest data from a variety of batch, streaming, and real-time data sources. It allows data teams to create feature definitions using SQL and Python, and deploy their features to production quickly and reliably. We’ll show you how our managed feature store allows you to improve your data to quickly detect and address the latest scams.

Agenda:

• Intro to Tecton
• How Tecton’s feature platform enables critical fraud detection ML use cases
• Demo of the platform