Off-the-shelf models only see part of the picture, so good users get blocked, and bad users get through. The best fraud teams leverage their own business insights to spot and block fraud. Chalk enables your fraud fighters and data scientists to incorporate 3rd-party signals alongside product usage, messaging and support history, and even password reset data to make high quality decisions for your unique user base.

Chalk makes it easy to fetch fraud data only when it’s needed. Each model specifies exactly the data staleness that it can tolerate to give you fresh data cheaply. By employing a layered approach to fraud, you can reject bad candidates without fetching expensive data.

The best fraud programs stop bad users and welcome good ones. Striking the right balance requires constant iteration. With Chalk’s Branch Deployments, it’s easy to experiment with new signals before going to production. Branch Deployments show how proposed features would have impacted your previous decisions.

Great fraud systems rely on dozens of data vendors and constant iteration to stay ahead of fraudsters. Providers like SentiLink, Socure, Emailage, Whitepages, and Early Warning Systems help you to form a complete picture of each user and transaction. However, when you have a new insight, integrating a new vendor or signal often requires significant time and engineering work. With Chalk, ship production-quality integrations on proof-of-concept timelines.

Traditionally, teams write one pipeline to fetch data for training and another competing pipeline to fetch that same data for production. To make matters worse, teams often re-write pipelines for each model that relies on that data. You wind up with dozens of implementations to fetch the same data, and they’re never all the same. Needless to say, discrepancies between pipeline code can lead to significant bugs. Chalk solves this with a unified feature catalog — a single pipeline to fetch data which is then accessible for all training and production models.

Chalk integrates with alerting systems like Pagerduty and Slack to keep your team informed about issues. Configure alerting thresholds for when data distributions don't match your expectations.