Fraud Score Overview

Fraud Score trains a custom fraud detection model for your business, using your own confirmed fraud outcomes combined with Fingerprint device intelligence. Unlike a generic risk score, Fraud Score learns what fraud looks like in your traffic specifically. Your fraud patterns are unique to your business. Fraud Score learns from your data, not a generic model built on someone else’s traffic.

How it works

Fraud Score follows a continuous loop:

You submit labels, confirmed fraud and non-fraud outcomes attached to specific events
We build a training dataset
Fingerprint trains one model per fraud type, per client type.
The model is reviewed by Fingerprint, then deployed manually or automatically depending on your settings
Predictions appear in your API response, Webhook, and Sealed Client Results
The model retrains automatically as you submit new labels

Supported fraud types

Category	Examples
`payment_fraud`	Chargebacks, stolen card use
`account_takeover`	Credential stuffing, session hijacking
`fake_account`	Synthetic identity, duplicate registration
`promotion_abuse`	Bonus farming, referral fraud
`multi_accounting`	Ban evasion, duplicate accounts
`platform_abuse`	API abuse, toll fraud
`content_abuse`	Spam, fake reviews
`other_fraud`	Custom use cases

API response

Predictions appear in the labels array inside your existing identification response.

{
  "labels": [
    {
      "label": "chargebacks",
      "category": "payment_fraud",
      "prediction": true,
      "ml_score": 0.872,
      "threshold": 0.650,
      "version": "4"
    }
  ]
}

One entry appears per active model. A customer with payment fraud and account takeover models on Web and iOS will see up to four entries.

Field	Description
`label`	Your label name as submitted
`category`	Fraud type from Fingerprint taxonomy
`prediction`	`true` if the score exceeds the threshold
`ml_score`	Model confidence score (0-1)
`threshold`	Configured cutoff for this model
`version`	Active model version

Prerequisites

Fingerprint Smart Signals must be active on your subscription.
You need at least 1,000 confirmed fraud outcomes per fraud type per client type to train your first model. For better results, aim for 10 confirmed non-fraud events for every fraud event.
Fraud Score is a paid add-on. Contact your account team to enable it.

Understanding Fingerprint

Identify browsers

Identify mobile devices

AI detection

Protect your implementation

Integrate with your server

Integrate Smart Signals

Fraud Score

Rules Engine and Flow (Beta)

Tips and advanced guides

Platform updates

Resources

Legal

How it works

Supported fraud types

API response

Prerequisites

​How it works

​Supported fraud types

​API response

​Prerequisites

How it works

Supported fraud types

API response

Prerequisites