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Case Studies

Real outcomes from real-world challenges

These platforms aren't just stopping fraud. They're making better risk decisions in real time, so legitimate customers face as little friction as possible.

Enterprise Retail

$2.3M saved without changing a single rule

With fraud rules frozen in place, this national retailer needed a scoring layer that could deliver results without re-architecting workflows.

Pipl Trust’s adaptive scoring saved $2.3M in chargebacks and reduced reviews by 15%. No rules rewritten. No approvals lost.

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Global Marketplace

Approval rates lifted 19.4pp

This global marketplace used Pipl Trust to calibrate their model scoring and trust tiers, unlocking a 19.4pp lift in approval rates without raising chargeback risk.

The results were smarter approvals, sharper signal separation, and estimated savings of $120M annually.

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Travel Booking Platform

False declines cut, trust scores optimized

One booking platform reduced false declines and manual reviews by plugging calibrated trust thresholds directly into their decision engine. ROC-AUC jumped to 0.87.

Risky bookings were caught earlier without disrupting trusted travelers.

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Social Media Platform

40% more risky signups flagged

Faced with growing bot attacks and identity abuse, this global social platform turned to Pipl to isolate trust signals earlier in the funnel.

Pipl Trust surfaced 40% of risky registrations missed by their native model and saw precision lift across regions and user types.

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Consumer Ecommerce Brand

27% reduction in manual review

This ecommerce leader needed a way to lower friction for legitimate users while maintaining strict fraud controls.

Adaptive trust signals reduced review volume and helped cut unnecessary friction without increasing risk.

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