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Why Pipl

Pipl built the industry's only large risk AI model

Enterprise fraud teams are under pressure to make faster, more defensible decisions at the exact moment identity data is becoming harder to use with confidence. We built Elephant, the industry's only large risk AI model, on more than 20 years of experience making fragmented global identity data usable in high-stakes decisions, powering our solutions that turn identity, behavioral, and device data into usable, explainable intelligence calibrated to the fraud environment your team actually manages.

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Why generic fraud tools can't solve the problem Elephant was built for

When fraud tools can't reflect the conditions your team manages, decision quality weakens, tradeoffs compound, and the gap gets covered by manual effort that was never meant to be the answer. Elephant is the industry's leading large risk AI model, built specifically for the complexity of real-world payment fraud environments.

Elephant is an entirely different kind of intelligence

Most fraud tools start with a general model and adapt it for payment use cases. Elephant was built from the ground up for payment fraud decisioning, trained on more than 20 years of global identity data across the world's most complex payment environments. It evaluates how identity, behavioral, and device signals relate, reinforce, or conflict with one another rather than treating them as isolated inputs. That connected context gives fraud teams more usable, explainable intelligence at the moment of decision, and it doesn't degrade as it moves across systems, review workflows, and compliance environments.

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Twenty years of data depth no competitor can replicate

Building a large payment AI model requires the kind of depth, coverage, and global signal quality that takes decades to accumulate. Pipl has more than 20 years of experience in making fragmented global data usable in high-stakes enterprise decisions. An identity graph of more than 5 billion identities and 740 billion signals. Already operating at scale across the world's largest payment networks, ecommerce marketplaces, and digital wallet platforms, the environments where intelligence quality gets tested hardest and where generic logic gets exposed fastest.

 

Enterprise teams choose Pipl

Generic fraud tools are built for broad applicability. Pipl solutions are built for the fraud environment your team actually manages. That's why some of the world's largest payment networks, ecommerce marketplaces, and digital wallet platforms trust Pipl.

Intelligence that reflects your environment, not the average one

Our solutions that leverage Elephant reflect your actual fraud patterns, thresholds, and approval goals rather than applying generic logic across environments that behave very differently. Scoring stays aligned as conditions shift, without constant manual compensation.

The only large payment AI model that powers almost everything

Elephant isn't adapted from a general model. It was built specifically for payment fraud decisioning, trained on more than 20 years of global identity data, and calibrated to the conditions real payment environments produce. 

Results that show up where teams feel the most pressure

Teams  are under pressure to improve authorization performance without widening fraud exposure. This is where Elephant's calibration to real payment environments produces the most measurable impact, from authorization rates to manual review burden.

Measured results in complex fraud environments

A global marketplace improved authorization performance by 19.44 percentage points at the same fraud threshold, without loosening fraud controls or widening exposure.
A national enterprise retailer drove an estimated $52.3M in chargeback reduction after deploying fraud scoring calibrated to their actual fraud environment.
A global ecommerce brand cut unnecessary analyst workload by 27% when more accurate scoring resolved decisions earlier in the workflow.
A travel booking platform reached 0.87 ROC-AUC in one of the most signal-noisy booking environments, where generic models consistently underperform.