Payment Identity Fraud

The identity was fake, but the payment is real

Payment identity fraud is becoming harder to spot and more expensive to miss. Legacy systems rely on transaction history or surface-level signals to flag risk, but fraudsters know how to build profiles that look clean. Elephant helps you catch synthetic identities, credential farms, and coordinated fraud rings at the point of payment before the damage is done.

See how we're strategic about trust

What payment identity fraud actually looks like

Fraud at the point of payment doesn’t always look suspicious. It mimics real behavior, slips past static rules, and exploits gaps between tools. But the patterns are there, if you know where to look.

A new user with a clean device, low cart value, and zero history

It’s really a synthetic identity testing designed to look safe, with no history because it’s never been caught

A spike in first-time purchases during a limited-time promotion

It’s really a fraud operation exploiting your campaign using pre-aged synthetic identities built to bypass basic checks

A series of low-risk transactions spread across accounts

It’s actually coordinated probing to test risk thresholds and map your approval logic before scaling up

Dozens of unrelated accounts transacting across different verticals

It’s actually all connected by a shared infrastructure; the same IP, device, or delivery patterns pointing to a single fraud ring

Stopping fraud is reactive;  
recognizing trust is strategic

Legacy fraud tools weren’t built to help you say yes. They were built to say no fast and often, with as little context as possible. That’s why most platforms tune their systems around what looks risky instead of what proves trustworthy. But fraud doesn’t always look risky. And trust isn’t something you match; it’s a score you measure.

Here’s how platforms are evolving their fraud approach to unlock trust at scale:

If your system is built to do this:

Block users based on mismatched or incomplete signals

Score based on past transaction behavior

Flag edge cases for manual review

Apply strict thresholds to control fraud exposure

Measure fraud losses as cost centers

A trust-first approach focuses on this:

Connect signals dynamically to uncover real users faster

Score based on identity integrity in real-time context

Confidently approve trusted users without delay

Calibrate risk based on signal quality, not quantity

Measure trust as a revenue driver

What changes when you trust the identity behind every payment

When you can trust the identity behind every payment, everything works better. You approve more real users without raising fraud risk. You reduce friction for people you want to keep. And you unlock the efficiency and revenue that legacy fraud tools were never designed to deliver.

Less friction

Confident identity trust at the point of payment means fewer step-ups, shorter delays, and smoother transactions. Customers move faster and stick around longer.

Shorten checkout time
Eliminate unnecessary steps
Improve customer retention

Know who to trust, wherever and however you work

Noisy signals, brittle integrations, and privacy pressure have made identity decisions harder than they should be. We’ve created two flexible solutions that are fast to integrate, privacy-native by design, and built to fit your workflow, not force a new one.

Trust API

Plug real-time trust scores and signal intelligence directly into your decision logic, so you can automate approvals, reduce fraud, and skip the rework.

Learn about Trust API

Trust Insights

A visual tool for fraud analysts to spot risk fast. Surface identity signals and connections at a glance, ideal for confident manual reviews and edge-case decisions.

Learn about Trust Insights

Insights and intelligence we recommend

Your fraud system is confident, but it’s also wrong

You’ve evolved your philosophy, but your model still punishes the unfamiliar, and it’s costing you.

Three tests to run to see if your fraud stack is learning

Three fast, field-tested ways to tell if your system is adaptive, without buying a thing.