Device and network intelligence
Identify trusted users, detect fraud earlier and make smarter decisions with advanced risk signals
PII alone is rarely enough to detect sophisticated fraudsters
Fraudsters often have access to stolen or synthetic PII, making it harder to distinguish trusted customers from bad actors using identity data alone. Device and network intelligence helps reinforce identity and fraud decisions by analyzing device traits, IP information and other session signals to recognize trusted users and identify suspicious activity. Together, these signals support fraud prevention across account opening, authentication, payments and account management, helping businesses stop more fraud and better recognize legitimate users.
90%
of businesses are confident in device intelligence for customer recognition
62%
of consumers say device recognition creates a stronger feeling of security
2x
increase in fraud detection through high-risk device and behavioral linkage
Source: Experian NeuroID case study
Device Intelligence
Network intelligence
What it evaluates
Device intelligence determines what device is being used and whether it displays high-risk traits.
Device signal capabilities
- 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 instrumentation and tampering frameworks
- Monitoring velocity and device-sharing patterns across accounts
- Identifying trusted or previously known high-risk devices
What it evaluates
Network intelligence evaluates where the interaction is originating and whether the connection presents elevated risk.
Network signal capabilities
- IP intelligence and geolocation
- VPN detection
- Public proxy detection
- TOR detection
- Remote access detection
- GPS spoofing detection
- Incognito browsing detection
- Network reputation analysis
- IP allowlist and blocklist identification
- Detection of environmental inconsistencies and location anomalies
NeuroID
Stop third-party fraud at onboarding, login and beyond through behavioral analytics with device and network signals
Frequently asked questions
Device and network intelligence uses signals from a user's device and connection to help verify identity, recognize trusted customers and identify potentially fraudulent activity. These signals can include device characteristics, IP address intelligence, geolocation patterns, device reputation, and network risk indicators like location mismatches, abnormal VPN usage and bot frameworks. Together, they provide a more complete picture of digital interaction, helping organizations make smarter fraud and authentication decisions.
Device intelligence analyzes what device is in use, while network intelligence determines where the connection is coming from. Device intelligence focuses on the characteristics and behavior of the device being used, such as device configuration, operating system and device familiarity (whether the device has been previously associated with the account it's trying to access). Network intelligence focuses on signals from the connection itself, including IP address reputation, proxy or VPN usage, geolocation consistency and network-related risk indicators.
Device and network intelligence helps identify suspicious patterns that may indicate fraud, such as devices associated with previous fraudulent activity, unusual location changes, high-risk network connections, behavioral anomalies or inconsistencies between a customer and their typical digital profile. By analyzing these signals in real time, organizations can detect account takeover attempts, synthetic identity fraud, new account fraud, bot activity and other emerging threats while allowing legitimate transactions to move forward.
Usually, device intelligence solutions operate behind the scenes and require no additional effort from the customer. Unlike more visible authentication challenges — like passwords, MFA codes and knowledge-based questions — device and network data are collected passively, allowing risk to be assessed in real time without friction. In fact, accurate recognition of trusted, returning devices can streamline user experiences by removing unnecessary friction.
Device and network intelligence can be highly effective when used as part of a layered fraud prevention strategy. These signals provide valuable context that can help distinguish trustworthy users from potentially risky activity, particularly when combined with identity, behavioral and transactional data. While no single signal can detect every fraud attempt, combining device and network intelligence with other fraud and identity tools can improve risk assessment, strengthen customer recognition and help reduce false positives.
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