/
Contact usSee pricingStart building

    About Stytch Fraud and Risk

    Introduction
    Use cases
      Overview
    • Recipes

      • Remembered device flow
        New device notifications
        Block traffic by country
        Prevent free trial abuse
    Device Fingerprinting
      Overview
      Fingerprints
    • Verdicts

      • Verdicts overview
        Allow
        Block
        Challenge
        Not Found
    Email IntelligenceComing soon
      Overview
    Getting started
      Device Fingerprinting API
      DFP Protected Auth
    Decisioning
      Decisioning overview
      Setting rules with DFP
      Overriding verdict reasons
      Intelligent Rate Limiting
    Enforcement
      Enforcement overview
    • Protected Auth

      • Overview
        Handling challenges
    Integration steps
      Go-live checklist
      Configure custom domains
      Add external metadata
      Test your integration
      Privacy and compliance considerations
    Reference
      Warning Flags (Verdict Reasons)
      Raw Signals
Get support on SlackVisit our developer forum

Contact us

Fraud and Risk Prevention

/

Guides

/

About Stytch Fraud and Risk

/

Use cases

/

Recipes

/

Prevent free trial abuse

Prevent free trial abuse

In this guide we'll walk through how to use Stytch Device Fingerprinting to prevent free trial abuse.

Free trials are vulnerable to account creation abuse, in which a single actor signs up for multiple accounts in order to claim more free trial credits. Rewards programs and giveaways are also at risk for this kind of fraud.

Stytch Device Fingerprinting provides powerful tools to prevent free trial abuse.

Goals and overview

The business purpose of a free trial is to enable real new users to try your product. This means our goal is to reduce "wasted" free trial resources that are given to abusers, without impacting real users.

Using Stytch Device Fingerprinting, you will be able to make better decisions by:

  1. Identifying automated requests, including scripts, bots, and browser automation
  2. Identifying confirmed repeat users using the Visitor ID
  3. Identifying suspected repeat users based on fingerprint and traffic patterns

Then using these identifications, you can add enforcement actions to stop users from signing up, or add additional friction to protect your free trial resources.

This guide uses the terminology of the Stytch fraud prevention framework. Follow the link to learn more about the framework.

Signal gathering

You should collect and look up a fingerprint at account creation time.

See Getting Started with the Device Fingerprinting API to add Device Fingerprinting to your application and start collecting data.

You should also send external metadata related to the account creation, which will help you correlate the fingerprints with additional application context.

Decisioning

Using the fingerprint data and the Stytch verdict, you will be able to classify different kinds of new accounts.

1. Identify automated and malicious requests

Device Fingerprinting detects automation tools, including browser automation and programmatic access. By default, any request which is detected as automation receives a verdict with recommended action BLOCK. Other warning signs like the use of Tor or high-velocity traffic will also receive a BLOCK verdict.

You should block any request that receives a BLOCK verdict from Stytch.

2. Identify confirmed repeat users

In Device Fingerprinting, the Visitor ID is guaranteed to be unique for each user. If you see multiple attempts to create an account that share the same Visitor ID, these accounts are being created from the same device.

You should block any request that shares a Visitor ID with earlier requests.

3. Identify suspected repeat users

In Device Fingerprinting, the Visitor Fingerprint is a collection of various device characteristics. The Visitor Fingerprint does not change when using incognito, clearing cookies, or using a VPN.

However, the Visitor Fingerprint is not guaranteed to be unique. It will be identical between two devices with the same hardware and software configuration. High traffic that shares the same Visitor Fingerprint may be manual abuse: a bad actor repeatedly creating accounts by clicking around on your website.

You can still use the Visitor Fingerprint to identify traffic with suspicious characteristics. As a general rule, if 1% of your traffic shares the same Visitor Fingerprint, this is suspicious and worth further investigation.

By combining Visitor Fingerprint with other attributes like IP address, you can also get more granular views. These can also be rate-limited or restricted based on your expected traffic.

Stytch support can help you determine reasonable rate limits for your traffic level and use case.

Enforcement

Now that you have identified different categories of users, you can take action.

As described in Enforcement with Device Fingerprinting, you have multiple options.

For each of the three categories above, you could:

  • Immediately block sign-ups and do not create an account.
  • Or, restrict the accounts until they pass stricter uniqueness checks, like SMS 2FA phone number or credit card number.
  • Or, shadow-ban the account and provide a limited or worse experience.
  • Or, allow the creation of accounts, but log the information and use it to periodically perform "ban waves".

Each of these strategies has different trade-offs depending on how expensive your resource is, how quickly it can be exploited, and how quickly you give feedback to an attacker.

As a starting point, we recommend:

  • For sign-ups with a BLOCK verdict, immediately block the sign-up with a generic error message.
  • For confirmed repeat users, allow account creation but provide a shadow-banning experience.
  • For suspected repeat users, log the information and review regularly. Once you have established baseline activity metrics, you can block more proactively using appropriate rate limits.

Analysis and feedback loop

You can monitor your results through the Device Fingerprinting Dashboard, as well as your own logging and analytics systems.

DFP Dashboard

Using these logs and metrics, you should:

  • Track the costs associated with free trial abuse
  • Exclude fake users from growth metrics and marketing data
  • Observe traffic patterns over time

You may also receive reports of false positives from users asking to be unblocked. These reports may be social engineering attacks from bad actors who are trying to evade your defenses. If you do confirm the identity of the reporter, you can Set Rules to allowlist them.

What’s next?

Learn how to get started with Device Fingerprinting in just a few minutes.

If you are interested in stopping free trial abuse, please reach out to Stytch.

Contact sales

Goals and overview

Signal gathering

Decisioning

1. Identify automated and malicious requests

2. Identify confirmed repeat users

3. Identify suspected repeat users

Enforcement

Analysis and feedback loop

What’s next?