Understanding your confidence score

When you make an identification request, the response contains a confidence score.

  "requestId": "8nbmT18x79m54PQ0GvPq",
  "visitorId": "2JGu1Z4d2J4IqiyzO3i4",
  "confidence": { 
    "score": 0.995,
    "revision": 1.1

A confidence score indicates how confident Fingerprint is that the browser (or device) in the request has not been misidentified for another existing browser (or device).

A confidence score is a floating point number whose value can range from 0 to 1.

How can you use confidence score for your application?

You can use the confidence score to

  • Decide whether to request additional verification methods (e.g. MFA) from your users. For example, to prevent account takeover, you could trust only those browsers (or devices) with a confidence score of 0.95 or higher.
  • Provide a personalized experience to your returning anonymous users only if their browsers were identified with a reasonably high confidence score.

The thresholds presented here are only for theoretical purposes. The actual threshold will depend on your use case and require iterative testing and analysis.

How is the confidence score calculated?

In simple terms, the confidence score is calculated as

1 - falsePositiveProbability

falsePositiveProbability represents the probability of a false-positive identification deduced from our prediction experiments.

How to interpret the confidence score?

First-time browsers (or devices)

Fingerprint identifies a browser (or a device) as new only when it cannot find a match for this browser in your data. Since a match cannot be found for first-time browsers, the probability of this browser being misidentified for another existing browser (i.e. falsePositiveProbability) is 0, and hence the confidence score for such new browsers (or devices) is 1.0.


Can an existing browser be identified as a first-time browser?

Yes. In those rare cases, the confidence score will still be 1.0. This is because the confidence score does not reflect the probability of a false-negative identification.

Returning browsers


Did you know?

You can use the visitorFound property to distinguish between first-time browsers and returning browsers.

Fingerprint identifies returning browsers (or devices) using either one of the following two methods:

  1. Deterministic Identification

    This method relies on properties (e.g. cookies) that can uniquely identify a given browser (or a device). The uniqueness of these properties makes it impossible for one browser to be identified for another (i.e. falsePositiveProbability is 0), hence the confidence score for events identified deterministically is 1.0
  2. Probabilistic Identification

    When deterministic properties are unavailable (e.g. switching to incognito mode, etc), Fingerprint uses probabilistic identification. This method relies on various properties collected from the browser (or device) which are then combined to create a visitor ID. See The Top 7 Browser Fingerprinting Techniques Explained to learn more about identifying browsers using probabilistic methods.
    For two identical (e.g. same version, same locale, etc) browsers, these properties may not differ causing Fingerprint to incorrectly identify one browser for another (i.e. falsePositiveProbability is greater than 0). Therefore, the confidence score for events identified probabilistically is always less than 1.0