Email Risk Scope

Key outputs

Email Risk Score

Probabilistic score between 0.001 and 0.999 indicating the likelihood of fraud associated with an email address.

• Higher scores = higher risk of fraud.
• Evaluates deliverability, domain type, age, and linked addresses.
• Powered by internal and external data sources.
• Multiple model scores may be returned in a single response.

Name-Email Correlation

Value between 0.01 and 0.99 showing how strongly an email is linked to the consumer’s name.

• Very high confidence for verified full-name matches.
• Supports fuzzy matching and nicknames.
• Reason codes explain the underlying correlation signals.
• Improves trust in onboarding, OTP, and account update flows.

Industries it fits

• Financial services: Account opening, fraud prevention, progressive onboarding
• E-commerce: Checkout fraud prevention, return abuse detection
• Payments: Reduce risk in P2P transfers and high-value transactions
• Gaming & media: Stop fake accounts, bonus abuse, duplicate accounts
• Telecom: Validate new email addresses during profile changes
• Plus other digital onboarding and fraud risk workflows

Business and operational outcomes

1. For fraud and risk teams

• Identify high-risk email addresses in real time.
• Reduce account takeover attempts linked to fraudulent OTP delivery.
• Leverage multiple model scores per call for testing and migration.
• Gain transparency with reason codes for both risk and correlation.

2. For growth and onboarding teams

• Progressive onboarding: Establish trust early with only an email.
• Reduced friction: Allow good users through while blocking suspicious addresses.
• Adaptive signals: Multiple models tuned for different fraud/risk trade-offs.

3. For support and operations

• Fewer escalations: Clearer fraud signals reduce review queues.
• Transparent explanations: Reason codes help agents understand outcomes.
• Lightweight integration: Minimal required fields keep implementation fast.

Performance considerations

• Fast responses: Sub-second latency on average in production.
• Always available: 99.99%+ uptime with global redundancy.
• Built for scale: Supports tens of millions of risk checks per day.
• Smart models: Multiple models run in parallel to optimize accuracy and fraud capture.

Boundary conditions

• Email only is required: Correlation accuracy improves when first and last name are also provided.
• Multiple models: Customers should test challenger vs. production models to tune thresholds.
• Available in USA only: This feature is limited to the U.S.A. and is not supported in other regions.