Payment Fraud
Payment performance suffers when scoring can't separate real from risk
Most fraud scoring responds to that tradeoff by becoming more conservative, tightening thresholds until legitimate payment activity starts to fall away. Pipl Trust takes a different approach: AI-native scoring powered by Elephant, Pipl's large payment AI model, calibrated to your actual fraud profile, so teams can hold fraud thresholds with more confidence without suppressing authorized volume.
What payment fraud looks like when scoring is forced to guess
Payment fraud rarely looks obvious at the transaction layer. The signals are often there, but generic scoring can't interpret what they mean together. Treating each signal as an isolated input is where models start to guess, and where payment performance starts to suffer.
Synthetic identities that arrive with no visibly negative history
At the transaction layer, there's little to flag. In context, it may be a manipulated identity designed to pass basic checks by looking clean on arrival.
Coordinated fraud blending into peak demand
A spike in purchases can look like healthy demand. In context, it may be coordinated fraud using timing, volume, and approval pressure to blend into normal payment activity.Probing behavior disguised as low-value noise
A stream of low-value transactions can look harmless. In context, it may be testing approval logic and mapping fraud controls in preparation for larger attacks.
How we make payment fraud decisions more precise
Most fraud scoring forces payment teams into a choice with no good answer. Pipl Trust is built to change that by making scoring precise enough that teams don't have to choose between authorization performance and fraud control.
When payment fraud scoring reflects your actual environment
When payment fraud scoring reflects the actual environment, decisions become more precise. Pipl Trust helps payment teams hold fraud thresholds with more confidence, reduce review burden, and make stronger authorization decisions without widening fraud posture.
The tradeoff generic scoring can't solve
When scoring lacks the context to separate real risk from legitimate variation, teams face a choice with no good answer: accept more fraud exposure to protect authorization rates, or tighten controls until good transactions start to fall away. Pipl Trust is built to break that tradeoff by making scoring precise enough that teams don't have to choose.
Scoring that adapts as fraud patterns shift
Pipl Trust uses connected identity, behavior, and device signals to improve scoring accuracy in real time. As fraud patterns shift across channels, geographies, and customer segments, authorization decisions don't have to depend on static logic that falls out of step with the payment environment.
The foundation underneath every Trust solution decision
Pipl Trust draws on Pipl's identity graph and Elephant, Pipl's large payment AI model, to give payment teams the signal depth and scoring precision to separate real risk from normal variation at the threshold they're trying to hold.
5+ Billion
Global Identities
28+ Billion
Unique Identifiers
740+ Billion
Trust Signals
Built for production inside live payment workflows
Integrated into your workflow.
Integrated into your workflow.