Building Scalable Account Security with Real-Time Fraud Signals

Account takeover (ATO) fraud reached a staggering $16 billion in losses in 2024, with nearly one in four consumers reporting being victims, according to Javelin Research. As attack methods grow more sophisticated, security teams face mounting pressure to improve detection without creating friction for legitimate users.

The challenge lies in effectively analyzing user behavior across multiple timeframes simultaneously – combining historical patterns, recent activity, and real-time signals – a capability beyond what traditional data infrastructure was designed to handle.

In this webinar, we'll demonstrate how to build a production-grade account takeover detection system that combines these diverse data signals to identify suspicious activities before they result in fraud.

We'll cover:

• Why effective ATO detection requires multiple data timeframes and why single-source approaches fail
• How to implement powerful fraud signals like IP address risk scoring, device fingerprint analysis, and login attempt pattern detection
• A live demo showing how to build, test, and deploy ATO detection features in minutes instead of months
• How companies like Coinbase, Block, and North have reduced false positives while improving detection rates

See firsthand how teams can rapidly experiment with new detection signals, deploy them to production in minutes, and close the time gap between discovering new attack patterns and implementing defenses.

Ideal for data scientists, ML engineers, and security teams looking to modernize their approach to account security.