Proximity Pattern Detection

After a fraudster gets blocked, a common escalation pattern is recruiting someone else to place the next order. This could be a friend, family member, or paid associate. The proxy uses a different name, different email, and different payment method, but ships to the same delivery zone and follows the same behavioral patterns.

Standard fraud tools do not catch this because each proxy identity appears clean when reviewed in isolation. SpotFraud connects these identities by analyzing geographic and behavioral overlap across the entire network.

Monitor: Every order's shipping address is analyzed for proximity to known fraud cases. We map delivery clustering, zip code overlap, and address proximity against our fraud database in real time.

Analyze: The proximity engine cross links shipping addresses, delivery clustering patterns, order timing, product category selection, and claim type frequency against every known fraud case in the geographic area. When multiple orders from the same delivery radius match fraud behavioral patterns, the system recognizes the ring.

Act: Proxy fraud rings are flagged as a group. All connected orders are elevated in risk scoring, and the geographic area is marked for enhanced scrutiny on future orders. The brand receives a brief detailing the ring structure and recommended responses.

No single identity fraud tool catches proxy rings because each proxy identity passes individual verification. Only geographic and behavioral clustering analysis reveals the pattern.

If your fraud prevention only evaluates individual orders in isolation, proxy fraud rings will operate undetected through your system indefinitely. SpotFraud connects the dots that other solutions cannot see.