Assess Identity Risk

Risk is easier to understand when identity has a history.

Most fraud systems become more confident as an identity generates activity. By then, the organization may have already created the account, extended credit, issued the promotion, approved the transaction, or introduced friction to a legitimate customer.

AtData starts earlier.

Using email as a persistent identity anchor, AtData adds historical, behavioral, network, and correlation intelligence to help determine whether an identity demonstrates the characteristics of a real customer—or the inconsistencies of something manufactured, manipulated, or abused.

What is harder to manufacture is credible continuity over time.

A fraudster can provide a valid name. A real address. A working phone number. A legitimate-looking email.

Has the email existed for years or appeared recently? Does its activity resemble established identities? Is it suddenly appearing across the network at unusual velocity? Do the name, phone, address, and email make sense together? Has the address or domain been associated with fraud elsewhere?

AtData evaluates those signals around the identity so risk teams can make better decisions before relying on downstream behavior to reveal the answer.

AtData combines longitudinal email intelligence with real-time risk signals, identity correlations, and machine-learning models.

Explore Fraud Prevention

History and longevity
Fraud can fabricate attributes. It cannot fabricate years of history.

Date first seen and identity longevity provide context that point-in-time verification cannot. A newly observed email is not automatically fraudulent. But an address with little established history should not carry the same weight as one demonstrating years of consistent activity.

AtData clients have found this distinction meaningful in practice. In one peer-to-peer marketplace, newly created emails were three times more likely to be associated with fraud.

Email Age & Activity

Behavioral activity
Look beyond whether the identity exists to how it behaves.

AtData evaluates behavioral signals such as:

• Recency
• Longevity
• Velocity
• Popularity
• Activity consistency

Together, these provide context around whether an identity displays established, credible behavior or patterns that warrant additional scrutiny.

Behavioral Insights

Identity anomalies
Real data can still create a fake identity.

Synthetic and manipulated identities often combine legitimate individual attributes in combinations that do not belong together.

AtData can evaluate relationships among email, name, postal address, phone, and other available inputs to identify mismatches and anomalies that may be difficult to see when each field is checked independently.

Identity Correlation & Anomalies

Manipulation and automation
Identify when one identity is trying to look like many.

Fraudsters can alter email usernames, exploit aliases, use disposable domains, or generate synthetic addresses at scale to create accounts and bypass controls.

AtData identifies signals including:

• Email tumbling and alias variations
• Disposable and high-risk domains
• Gibberish or machine-generated usernames
• Domain and IP risk
• Validation and toxicity indicators

AtData’s gibberish model, for example, is designed to identify likely random, synthetic, or automated email inputs in real time at capture.

Username Intelligence Trilogy

Assess risk where identity becomes consequential.

• Prevent Application & Account-Opening Fraud

  Evaluate identity credibility before creating an account, extending credit, or granting access.

• Detect Synthetic Identities

  Identify new, inconsistent, manipulated, or weakly established identities before they develop artificial credibility.

• Stop Promotion, Trial, & Incentive Abuse

  Detect repeated identities, disposable emails, automated signups, and systematic attempts to extract value intended for legitimate new customers.

• Reduce Transaction and Payment Fraud

  Add identity history and behavioral context to existing transaction, payment, and fraud controls.

• Enrich Fraud & Risk Models

  Introduce deterministic identity signals, behavioral history, and risk intelligence into proprietary fraud models without replacing the models already tuned to your business.

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Detect more risk without treating every customer like a risk.

Strong fraud prevention is not simply about blocking more activity. It is about becoming more confident about which activity deserves intervention.

A growing international BNPL provider integrated AtData’s Fraud API and custom fraud modeling to identify risky signups earlier while maintaining a frictionless experience for legitimate customers. The organization reported improved fraud detection, fewer unnecessary reviews, faster legitimate approvals, and reduced false positives.

A nationwide lender with more than 1,000 branches similarly uses AtData intelligence to identify risky behavior earlier and make better decisions about when more expensive escalation or review is warranted.

Explore Customer Results

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Why AtData

Identity risk informed by what happened before the transaction.

Many risk signals describe the interaction happening now.

AtData adds something different: memory.

More than 25 years of email identity history and real-time behavioral activity provide context around how an identity has existed, changed, connected, and behaved before it reached your organization.

That intelligence helps risk teams:

• Detect questionable identities earlier
• Add signal without adding customer friction
• Strengthen existing fraud models
• Reduce unnecessary manual review
• Separate new legitimate customers from manufactured identities
• Adapt decision thresholds to their own risk strategy

Give every identity decision more context.

Fraud prevention gets expensive when organizations have to wait for suspicious behavior to prove that an identity was risky. AtData helps you evaluate the identity earlier using history, behavior, relationships, and network intelligence that already exist when the customer first arrives.