Usage Scope
Learn how Socure Email Risk predicts fraud risk and validates email-to-identity correlation.
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.
Updated 3 months ago