2024.09.18 | Stytch Fraud & Risk Prevention

Today we’re excited to launch our Fraud & Risk Prevention solution on Product Hunt, with a suite of new features that enable you to integrate sophisticated fraud prevention and fine-grained traffic shaping into your authentication flows via API. 

Check out our Product Hunt and let us know what you think.

Stytch Fraud & Risk Prevention

Stytch’s solution leverages advanced fingerprinting which identifies every device attempting to access your application with an industry-leading accuracy of four 9s — even when visitors switch across incognito browsing, webviews, VPNs, user agents, IP addresses, and more. It tags every visitor with a unique device fingerprint that persists across multiple sessions and is end-to-end encrypted to prevent reverse engineering and tampering.

These device fingerprints capture core device attributes such as the hardware’s operating system and architecture, browser engine and version, and network. 

Introducing new features

• Intelligent Rate Limiting: Our solution uses predictive analysis of device, user, and traffic sub-signals to detect unusual traffic volumes and apply precise rate limiting. Because it’s built on precision fingerprinting, it won’t restrict legitimate users and will adapt to new attacker profiles in real-time. 

• Security Rules Engine: Our solution allows for programmatic configurability of Stytch’s automated Allow, Challenge, or Block verdicts. This enables easy customization of preset rules via API or with a single click in the Dashboard, making it easier to define bespoke access rules and handle exceptions that are unique to your application.

• ML-Powered Device Detection: Our solution leverages a supervised machine learning model trained on a global device dataset will programmatically detect and assess the risk of new device types to determine if they are malicious. For example, if a new browser is identified claiming to be Chrome, it can evaluate that new browser against every historical Chrome version ever created to determine its validity and risk potential. 

Example app with Adaptive MFA

Our new example app showcases how to leverage Stytch's Device Fingerprinting (DFP) to implement Adaptive MFA, ensuring users only complete MFA when logging in from a new device. The app tracks trusted devices and determines whether future login attempts require MFA. It demonstrates key use cases including new user login and MFA enrollment, returning users logging in on recognized devices, and MFA prompts for unrecognized devices.

Clone our example app on GitHub or visit our docs to get started. 

Where to find us

Stytch developer Slack

Join the discussion, ask questions, and suggest new features in our official Slack!

Get support

Check out the Stytch Forum or email us at support@stytch.com