At A Glance
Discover how to stop fraud across the customer lifecycle and extend device recognition across digital channels.Fraudsters have more access than ever to stolen and synthetic personally identifiable information (PII). A name, date of birth and Social Security number (SSN) are no longer proof of identity on their own, as that data can be exposed in a breach and sold on the dark web or fabricated entirely.
When even seemingly clean identity data is open to question, businesses need additional signals to reinforce fraud decisions. Device intelligence offers a straightforward, trusted solution to close gaps in fraud prevention. According to Experian’s 2026 U.S. Identity and Fraud Survey, 90% of businesses are confident in device intelligence for customer recognition, and 62% of consumers say device recognition creates a stronger feeling of security.
Trust, however, is only the first step. Effectively implementing device intelligence for fraud prevention requires an understanding of how it works, what it’s capable of and how device signals fit alongside other fraud prevention solutions.
What is device intelligence?
Device intelligence analyzes the technical characteristics and history of the device behind a session to determine whether it exhibits high-risk traits or belongs to a trusted, previously recognized user. Instead of asking “does this person’s information match our records,” device intelligence assesses a different risk area: what device is in use, and is there reason to believe it’s being used for fraudulent purposes?
This distinction matters because device signals are collected passively at account opening, login and every transaction in between, without requiring customers to do anything differently. Monitoring device data across sessions is a friction-free, effective way to detect both third-party and first-party fraud at any point in the user interaction.
How device intelligence detects fraud
Modern device intelligence solutions evaluate dozens of signals in real time to build a picture of device risk, including:
- Recognizing known and returning devices
- Identifying new or changed devices
- Detecting emulators and virtual machines
- Identifying jailbroken or rooted devices
- Detecting cloned applications
- Identifying recent factory resets
- Detecting bot frameworks
- Monitoring velocity and device-sharing patterns across accounts
- Flagging devices previously associated with confirmed fraud
Together, these signals reveal previously unseen patterns and, in turn, uncover risks that fly under the radar of PII- or credential-based fraud prevention strategies. For example, device signals can indicate when a single device repeatedly returns to a business’s application and opens multiple accounts, or when a device is factory reset shortly before a high-value transaction.
Device intelligence in action
During onboarding, device intelligence can help reveal coordinated new account fraud strategies. Consider a fraudster equipped with enough stolen PII from a single identity to fill every field on an application: on paper, the submitted identity information looks legitimate. But device intelligence uncovers more critical information, including whether the device in use has been associated with fraud elsewhere, or that it shows signs of being manipulated to hide its history.
After onboarding, device intelligence provides a critical layer of protection against account takeover fraud, transaction fraud and other unauthorized activity. Say an account was created using an iPhone 17, with multiple subsequent logins from that same device. A sudden login attempt from an iPhone X, especially one that is connected to other accounts, should raise red flags. Device intelligence reveals this and enables businesses to stop high-risk actions before losses occur.
Using network signals alongside device intelligence
Device intelligence answers “what device is this?” Network intelligence answers a related but different question: “Where is this connection really coming from, and does it look risky?” Network signals include IP reputation and geolocation, VPN and public proxy detection, TOR exit node detection and GPS spoofing — risk indicators that live in the connection rather than the hardware.
Given modern fraudsters’ ability to mask data to circumvent fraud controls, every layer of defense provides additional security. Combining device and network intelligence gives fraud teams a more complete view of the device behind each session and where the connection is truly coming from; that’s why most solution providers already package device and network intelligence together.
How we can help
We bring device intelligence together with network signals and behavioral analytics to help businesses stop fraud across the customer lifecycle and extend device recognition across digital channels.
Explore our device and network intelligence solutions to see how these signals fit into your fraud stack, or talk to an expert to explore how to fortify your fraud stack today.