Bot Detection

Feature Snapshot

Summary:
Bot Detection is a feature that leverages ActiveProspect’s proprietary algorithm to analyze signals captured during the TrustedForm certificate process to determine whether a lead was generated by an automated bot instead of a human.

Key Benefits:

• Protects lead quality by identifying and flagging non-human submissions
• Prevents wasted marketing spend on fraudulent or invalid leads
• Enhances regulatory compliance and reduces legal risk by identified invalid records created by a bot

Typical Use Cases:

• Filtering out web form submissions generated by automated scripts
• Preventing bot traffic from skewing lead quality and conversion metrics
• Integrating with lead distribution systems (such as LeadConduit) to automatically reject or route leads flagged as bots

How Bot Detection works

Bot Detection evaluates a visitor’s execution environment, on-page display characteristics, and behaviors for patterns that are highly correlated with automated tools. We look for signals that indicate an automation-specific browser context, inconsistencies between a device’s claimed type and its display or interaction patterns inconsistent with human use to determine if a bot was used. The checks are lightweight, privacy-conscious, and continually updated. The outcome is provided as a property named “bot_detected” having a Boolean value (true/false) or null for unsupported certificate types.

Step‑by‑Step Instructions

Integrate Bot Detection via the Certificate API

1. Make the API Request:
   Include “bot_detected” in the list of properties requested when making an API call to TrustedForm Insights.
2. Review the API Response:
   Examine the returned value of “bot_detected”:
   • If true, the certificate resulted from automated activity.
   • If false, no bot activity was detected.
   • If null, bot detection is unsupported for this certificate type.

Integrate Bot Detection via LeadConduit

1. Implement in LeadConduit:
   a. Open your LeadConduit flow and navigate to the “Steps” tab.
   b. Add a TrustedForm Add-On step configured for Insights and select the Bot Detection data point.
2. Apply a Filter:
   Optionally, introduce a filter step to automatically reject or route leads where “bot_detected” is true.
3. Save and Test Your Flow:
   Save your configuration and submit a test lead to verify that the “bot_detected” property is correctly captured and triggers the intended filtering action.

Expected Result:
The API response and appended lead data will include a “bot_detected” field indicating true if bot-like behavior is detected (leading to a rejection or alternate routing) and false if human behavior is confirmed. For unsupported certificate sources, the field will return null.

Best Practices

• Automatically filter or flag leads where “bot_detected” is true to maintain high lead quality and mitigate compliance risk.
• Alert partners if you notice a high or increased rate of bot generated leads

Troubleshooting

Frequently Asked Questions (FAQ)

Q: What does it mean if “bot_detected” is returned as true?
A: It indicates that the algorithm has identified signals that indicate the lead was generated by automated processes.

Q: Why might “bot_detected” return null?
A: A null value means bot detection was not performed, typically because the certificate originates from sources like Meta Lead Ads or native mobile applications where detection is unsupported.

Q: Can sophisticated bots bypass this detection mechanism?
A: While Bot Detection is designed to minimize false positives, highly advanced bots might occasionally mimic human behavior well enough to evade detection.

Q: How do I integrate Bot Detection into my existing lead processing system?
A: The steps to do this will vary based on the system you are using. Reach out to the system operator for help.

Q: Why might TrustedForm Bot Detection produce a different result than another fraud or bot-detection service?
A: Fraud or bot-detection systems use proprietary methods, signals, and thresholds. Some emphasize reputation data, while TrustedForm evaluates behavioral and environmental signals captured during the form session, so results may differ without being contradictory.

Glossary

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