After integrating Verisoul and receiving decisions on accounts - either through the API or dashboard - you’ll need to implement strategies to block bad users while minimizing friction for legitimate users and eliminating false positives. Verisoul provides flexible options for implementing these decisions in your system.
Verisoul aims to automate the data science work needed for accurate user decisions. While Verisoul provides the decisions, you’ll need to implement the actual blocking mechanism in your system. This is typically done alongside an authentication provider (e.g., Auth0’s blocking API).The decision field can be tuned in two primary ways:
The machine learning models can be fine-tuned for your specific ecosystem:
Adjust sensitivity for multi-accounting detection
Configure proxy/VPN detection thresholds
Customize device risk parameters
Tune bot detection sensitivity
Modify email risk scoring
Calibrate how sub-scores contribute to the overall account score
Contact the Verisoul team to modify these configuration values. They can help evaluate different scenarios or analyze disposition data based on downstream metrics like chargebacks.
To minimize friction for legitimate users while maintaining strong security, Verisoul supports a progressive security framework using step-up verifications. This approach allows you to:
Start with lightweight, frictionless checks for all users
Progressively add stronger verification requirements only when needed
Reduce false positives and support workload
Maintain security without compromising user experience
For organizations with existing fraud detection systems, Verisoul’s decisions and signals can serve as additional inputs to enhance your models. This allows you to:
Combine Verisoul’s insights with your internal data