How Risk Scoring Works

Every validation request returns a risk_score between 0 and 100. This score is calculated by running multiple independent checks, each contributing a weighted score based on how strongly the signal indicates fraud.

The scoring model

Fidro runs up to 23 checks per request across five categories:

Email checks

IP checks

Stripe card & payment checks

These run automatically on chargeback prevention, no extra integration needed.

The card fingerprint checks are covered in depth in Card Fingerprint Tracking.

Anomaly checks

These only run on /validate calls that carry an event or a user_id, and they look at your account's own history rather than at the email or IP in isolation. See Velocity and Anomalies for the exact rules.

Optional merchant metadata checks

These only run when you pass additional metadata on your Stripe PaymentIntent. See Passing Customer Metadata for setup.

How scores combine

Each failed check adds points to the score. Checks are weighted by fraud correlation and adjusted by severity, a Tor exit node is a stronger fraud signal than a missing phone number.

The final score is capped at 100. Multiple moderate signals can combine to push a user into the middle range even if no single check is alarming on its own.

Score ranges and recommendations

Signup and login validation (/api/validate):

An anomaly with a floor (IP signup burst, email pattern burst, impossible travel) returns at least challenge whatever the score.

Stripe chargeback prevention uses its own labels on the same 0 to 100 scale:

The chargeback thresholds are configurable in your chargeback settings if you're on the Pro plan.

Reading check results

The data.checks array in the response shows exactly which checks ran and what they found:

{
  "risk_score": 72,
  "recommendation": "refund",
  "data": {
    "checks": [
      {
        "name": "disposable_email",
        "passed": false,
        "score": 25,
        "detail": "Domain mailinator.com is a known disposable email provider"
      },
      {
        "name": "prepaid_card",
        "passed": false,
        "score": 30,
        "detail": "Prepaid card detected"
      },
      {
        "name": "avs_mismatch",
        "passed": false,
        "score": 35,
        "detail": "Address verification failed"
      }
    ]
  }
}

Use these details to build custom logic, for example, you might allow prepaid cards but always block disposable emails combined with AVS failures.

Tips for tuning

• Start with defaults. The built-in thresholds are calibrated across thousands of real-world fraud patterns.
• Monitor your review queue. If you're reviewing too many legitimate users, consider raising your review threshold.
• Use blocklists. If you see repeat offenders, add them to your custom blocklist for instant blocking on future attempts.
• Pass optional metadata. Adding account age, order count, and device fingerprint data gives Fidro significantly more signal for chargeback scoring.