Fraudsters can rotate their email, IP address, phone number, and payment details in seconds. Their devices, however, tell a different story. In 2025, 44% of fraudsters used developer tools to simulate device behavior. Synthetic identity fraud in the US rose 300% in a single year. The attack surface is shifting, and device data is one of the few signals they can't easily fake.
Read our guide for using Device Intelligence, a practical playbook for fraud teams who need to use device data to make better decisions earlier, and across the full user lifecycle.
Whether your challenge starts at signup, login, account recovery, or payout, this guide shows you how to interpret device signals in context, combine them with identity, behavioral, and payment data, and apply friction precisely, without blocking the legitimate users you need to keep.
What's inside:
- What Device Intelligence actually reveals and the critical difference between device fingerprinting and Device Intelligence
- A full fraud lifecycle map: how device signals apply from registration through to refunds and investigations
- The five core fraud use cases where Device Intelligence creates the most practical value: fake accounts, multi-accounting, ATO, payment fraud, and payout abuse
- How to combine device, identity, behavioral, and payment signals for sharper, more proportionate decisions
- A decision-making framework: when to approve, monitor, step up, review, or block
- The six most common mistakes fraud teams make with device data, and how to avoid them
