Identity Intelligence

SpotFraud ties device fingerprints, IP addresses, email behavior, and payment method patterns across every order in our network. This creates persistent identity graphs that follow fraudsters even when they rotate their tools, change their names, or switch payment methods.

Our system detects the specific tools that organized fraud rings rely on. This includes anti detect browsers such as Adspower, Multilogin, and GoLogin, which fraudsters use to create clean browser profiles that appear as unique, legitimate users. We identify these by analyzing browser entropy, canvas fingerprint anomalies, WebGL renderer mismatches, and suspiciously clean cookie states.

We also detect address jigging. This is the technique where fraudsters make small modifications to shipping addresses. They add apartment numbers, abbreviate street names, or swap unit types specifically to bypass standard blocklists. When a session has zero cookies, no browsing history signals, and a freshly provisioned fingerprint, our system treats that as a high confidence fraud indicator.

Monitor: Every session that interacts with your store is fingerprinted in real time. We capture device characteristics, browser configuration, network attributes, and behavioral signals. This data is compared against our global identity graph instantly.

Analyze: The identity engine looks for patterns that indicate a manufactured session. Fresh fingerprints with no history, browsers running in virtual environments, canvas hashes that do not match the claimed operating system, and addresses that are slight variations of known fraud addresses all trigger elevated scrutiny.

Act: When a session is identified as suspicious, the order is flagged and the identity signals are added to the network graph. Any future orders from connected identities are automatically elevated in risk scoring. The affected brand receives a detailed brief with the identity connections and recommended actions.

Anti detect browsers and virtual machine sessions that attempt to simulate unique legitimate users. Address jigging and delivery address manipulation designed to bypass standard blocklists. Email patterns associated with disposable or recently created accounts.

Device fingerprint rotation across multiple orders from the same behavioral origin. Payment method cycling where different cards or payment accounts trace back to the same device or session characteristics. Browser entropy anomalies where the reported browser configuration does not match typical user environments.

Bad actor profiling tracks usernames, aliases, phone numbers, and behavioral patterns of active fraud posters over time, building profiles that connect individuals across multiple groups and channels.

Most fraud prevention providers do not check for anti detect browsers or jigged addresses at all. This is precisely why organized return fraud rings operate through their systems without detection. If your current solution only checks email reputation or basic address matching, you are missing the majority of sophisticated fraud attempts.

The identity graph compounds over time. Every new fraud case adds data points that make future detection faster and more accurate. This is not a system that degrades as fraudsters adapt. It is a system that strengthens with every interaction.