Real Time Risk Scoring

SpotFraud's risk scoring engine processes every order, return request, and customer support interaction the moment it occurs. Unlike static rule sets that update quarterly or rely on manually configured thresholds, our ML backend continuously learns from new fraud patterns as they emerge across the entire merchant network.

Each risk score is a composite of hundreds of signals. These include purchase velocity, device entropy, payment method behavior, return frequency, claim type distribution, geographic anomalies, and behavioral timing patterns. Every signal is weighted and contextualized against the latest threat intelligence drawn from every merchant in the SpotFraud ecosystem.

The system becomes measurably more accurate with every transaction it processes. This means the longer you use SpotFraud, the better it performs for your specific business, product categories, and customer base.

Monitor: Every incoming order and return triggers an instant evaluation. The scoring engine pulls in device fingerprint data, address verification results, payment method signals, and behavioral metrics in real time. No batch processing. No delayed scoring.

Analyze: AI algorithms compare the current transaction against known fraud patterns, recent threat intelligence from monitored channels, and historical data from across the network. The score reflects both the individual transaction risk and the broader threat context. Human analysts review high confidence flags within hours to validate new patterns.

Act: Based on the risk score and your configured thresholds, the system can automatically hold a refund, flag an order for review, send an alert to your team via Slack or email, or escalate to manual investigation. All actions are logged and auditable. You receive a clear brief of what was found and what to do next.

Anomaly scoring assigns each order a composite score based on 20+ signals. High scoring orders are held for review or flagged for enhanced documentation on return. Method matching compares new orders against the archive of known fraud methods extracted from monitored channels, flagging orders that match the exact pattern being shared in groups.

Temporal pattern analysis detects order spikes that correlate with fraud forum post times. If a brand is posted at 2pm and 40 orders arrive over the next 3 hours, the engine flags the entire batch. Network graph analysis maps relationships between known bad actors so that when one ring member is caught, the graph surfaces all connected accounts for review.

Cluster detection identifies groups of orders placed independently but originating from the same fraud ring through address proximity, order timing, and SKU overlap.

Static fraud rules from legacy providers cannot keep pace with how quickly fraud tactics evolve. A rule that catches one pattern today will be obsolete next month when fraudsters adapt their approach. Our models evolve continuously, which means your protection never falls behind the threat landscape.

Every fraud ring caught, every method decoded, and every chargeback fought feeds back into the scoring engine. This compounding data flywheel is the core long term competitive advantage of the SpotFraud network. The more merchants participate, the smarter the system becomes for everyone.