The invention relates to a
risk control method for cross-border trade data. Cross-border trade behaviors are divided into three levels, namely a
system type layer of national
system / policy triggered transaction, a business type layer of a process chain and an execution type layer of
data submission / customs clearance /
payment behaviors. Modeling a behavior stability mapping relation among the three hierarchies through
sparse matrix reconstruction and adversarial
noise regression; introducing an interlayer fluctuation tension coefficient for each transaction to measure offset appearing in a behavior chain; when the tension
peak value exceeds a training threshold value, even if single-
point data is not abnormal, the event is marked as a
potential risk transition event; according to the method, cross-border transaction behaviors are divided into a
system type layer, a business type layer and an execution type layer, and an interlayer fluctuation tension coefficient is introduced, so that a cross-level collaborative imbalance phenomenon in a transaction chain is effectively captured. Even under the condition that no obvious field anomaly exists, the pseudo-legal transaction mode caused by behavior
chain structure mutation can be recognized, and early warning of non-dominant structure anomaly is achieved.