Skip to main content
Contact Us
use-case-payment-fraud-hero-3
Transaction Risk

You're fighting a problem fraud platforms weren't built for

Payment fraud has evolved faster than the architectures most fraud teams built to detect it, leaving authorization performance and fraud exposure both moving in the wrong direction. Pipl Trust is built for what's hitting your business now, with scoring precise enough to hold both sides of the tradeoff.

See how Pipl is different

Trusted by leading global enterprises

open-table-wht
Sony-wht
allianz-wht
stubhub-wht
trip-advisor-wht
apruvd-wht
abc-wht

The fraud hitting authorization has shifted, but most architectures haven't

Payment fraud has evolved across six dimensions over the past few years, and the shifts reinforce each other, which is why architectures built on prior assumptions are losing ground on both sides of the tradeoff.

scale-blk

Fraud scales without humans

Generative AI produces synthetic identities, deepfaked documentation, and behavioral scripts at industrial scale and near-zero marginal cost. Architectures trained on pre-AI patterns can't separate them from legitimate customers at the point of decision.

warning-blk

Fraud runs as a campaign

What used to be discrete events now operate as coordinated campaigns running across cards, accounts, and channels at the same time. Single-channel scoring sees fragments, not the orchestration underneath them.
settings-blk

Attackers have studied your controls

Sophisticated operators reverse-engineer your approval logic, time attacks to promotional windows, and arrive with clean device profiles built to pass your thresholds. By the time the signals connect, the transaction has already cleared.
score-blk

Your scoring half-life is collapsing

By the time a fraud pattern is identified, modeled, and deployed into your scoring layer, the attack vector has already shifted. Every rule you ship is calibrated to a threat that's already moved.

person-blk

Clean profiles are engineered

Synthetic and manipulated identities are designed to arrive with no negative history. The absence of a red flag used to mean something. Increasingly, it means nothing at all.

search-blk

Every new rail widens your exposure

Real-time payments, embedded finance, cross-border flows, and new partnership rails have multiplied the surfaces fraud operates across. Every product launch widens the perimeter your fraud architecture has to cover, and most architectures weren't sized for it.
payment-fraud-caution-approvals-decreased

Your caution is now the cost

When fraud risk widens, the instinct is to tighten. It worked for a previous generation of fraud, but it doesn't anymore.

Tightening doesn't slow sophisticated, automated attacks; it suppresses approvals you should have made, accelerates the revenue cost of your fraud posture, and widens the gap you're trying to close.

Roughly 10% of card-not-present transactions are falsely declined, and false-decline losses are widely estimated to dwarf direct fraud losses. The response that feels safest is the one making the problem worse.

Authorization’s hidden bill

Authorization performance is the side of the tradeoff carrying the biggest cost, and reporting can't show you the shape of it. Most institutions pay more for caution than they realize.

manual-review-blk

Suppressed approvals don't look like loss

A declined transaction isn't logged as a damaged relationship or wallet share lost to a competitor. It's logged as a successful prevention. The metric that should be flagging suppression is the one hiding it.

 

deny-blk

One decline, three losses

Every approval you should have made costs the transaction, the customer relationship it strains, and the lifetime value at risk when that strain accumulates. Your reporting captures one of those at best.

 

chart-blk

Scale compounds the cost

Every peak, launch, and growth period widens the gap between what your authorization performance shows and what it's costing. Conservative scoring carries a cost in steady state. The cost rises with volume.



wallet-blk

Every decline funds your competition

A customer declined at the moment of payment doesn't shrug it off. They reach for a different option, and the institution that authorizes the transaction earns the next ones, too. Every suppressed approval is a wallet share shift you can't measure.

 

Connected scoring, not blanket caution

Conservative scoring was the right architecture for a generation of payment fraud. Today's attacks are automated, coordinated, and engineered to look like good customers. Pipl Trust reads the relationships between signals that thresholds can't, so authorization performance and fraud exposure stop competing for the same decision.

Single-moment scoring: A real first-time customer and an engineered synthetic look identical.

When Pipl sees a good transaction

A first-time customer with a clean device and a thin file looks like risk to a threshold. Against 5B+ identities and 20 years of connected history, they resolve to a real person, and the purchase is approved the first time.
When Pipl sees fraud

A synthetic identity is engineered to look like a perfect first-time customer. Elephant, Pipl's large risk model, scores the signals in combination and finds what's missing: a fresh email, phone, and device with no real past behind them.

Single-moment scoring: A viral promotion and a coordinated attack look identical.

When Pipl sees a good transaction

A surge of real demand during a promotion or product drop looks like an attack to velocity rules. Connected context recognizes the legitimate customers inside the spike, so your peak revenue moments aren't taxed by tightened thresholds.
When Pipl sees fraud

Coordinated campaigns hide inside high-volume moments, every transaction looking clean on its own. The orchestration shows up in the relationships between signals, and connected scoring catches it in flight, before the money moves.

Single-moment scoring: A loyal customer coming back and a mule account waking up look identical.

When Pipl sees a good transaction

A loyal customer trying a new wallet or paying on a new rail is still the same person. Identity context carries across payment methods, so expanding how customers pay doesn't mean declining more of them.
When Pipl sees fraud

Fraud probes every new rail first, hunting for the coverage gap. Elephant keeps learning from real, current fraud seen across the Trust Network, so defenses on new rails move at the rate of the threat.

See connected scoring on your authorization layer

If your current tools score transactions with generic logic, they're shaping your decision quality, fraud exposure, and review burden, whether you realize it or not. Pipl solutions are built on the first domain-specific large risk model purpose-built for payment fraud, applied to the authorization decisions you actually manage.

Contact us
Learn about the Trust Solutions