Most fraud teams can trust their tools to spot identity theft and synthetic identities. First-party fraud is harder: the name, DOB and SSN are real, the applicant is who they claim to be, and the loss only shows up months later as a charge-off.
In 2026 retrostudies, SentiLink found the average percent of funded loans/booked accounts that score as high-risk for first-party fraud was 2.89%, and that on average, institutions likely have 5x more first-party fraud on their books than identity theft or synthetic fraud combined. And even more concerning, on average, first-party fraud accounted for 72% of preventable fraud losses across identity fraud.
Join us for a candid conversation on why first-party fraud is so hard to define and detect, what the signals actually look like, and how teams can assess their stack and better find first-party fraud.
In this session, we’ll discuss:
- Why first-party fraud resists a clean definition, and how that ripples across fraud tracking
- Why identity theft and synthetic fraud tools miss first-party fraud, and the signals to better focus on at application stage
- How to treat high-risk applications without adding friction for good customers
- What good measurement looks like, and separating fraud losses from credit losses
