## 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.