At signup you know less about an applicant than you ever will again, and most controls make the call on attributes alone. Pipl solutions resolve every application against 5B+ identities with years of connected history, powered by Elephant, our large risk model, so real people get in and fabricated ones don't.
Account opening fraud has evolved across six dimensions, and the shifts compound. Controls that check attributes one at a time are losing ground at every one.
When signup fraud rises, the instinct is to add verification: another document, another OTP, another review queue. It worked for a previous generation of fraud, but not this one. Synthetics are built to pass those checks, while real applicants abandon the flow they're forced through.
GenAI can deepfake a person's face and voice, but it can't create an email address that already has ten years of history, which is why the answer is connected history, not more hoops. The response that feels safest is the one making the problem worse.
A falsely declined signup isn't logged as lost lifetime value; it's logged as a successful prevention. The customer joins a competitor the same afternoon, and your metrics call it a win.
The synthetic that clears signup doesn't only cost you at signup. It costs you in promo abuse, chargebacks, and account integrity cleanup, in line items that never trace back to the onboarding decision that let it in.
Every extra verification step sheds real applicants who won't finish the flow. Those losses land in conversion dashboards, not fraud reports, so the fraud posture causing them never gets the bill.
