Fraudsters adapt faster than static rule systems.
Strict controls block legitimate users and transactions.
Fraud indicators sit across disconnected systems.
Review teams struggle with high case volumes.
Fake users, sellers, and suppliers create platform risk.
Risk decisions need traceability and validation records.
DataXWorks turns fragmented risk signals into validated fraud intelligence.
Fraud prevention needs more than detection rules. It needs structured risk signals, validated cases, and clear feedback loops that help teams make safer decisions across users, sellers, transactions, and platform activity.
Detect seller abuse, fake accounts, and suspicious platform activity.
Build traceable fraud review workflows with validation records.
Improve fraud models with validated signals and feedback loops.
We identify where fraud signals exist. This includes accounts, transactions, behavior, devices, and entities.
We detect suspicious patterns across high-volume data. Model-assisted workflows help surface risk faster.
We score cases by risk, confidence, and business impact. This helps teams focus on the right cases.
Analysts review flagged cases for accuracy and context. Validated outcomes improve future detection workflows.
Map fraud indicators across transactions, behavior, and entities.
Identify suspicious activity using AI-assisted detection workflows.
Prioritize cases based on severity and confidence.
Verify sellers, users, suppliers, and partner data.
Validate flagged cases and reduce false positives.
Feed reviewed outcomes back into fraud detection systems.
