  Capturing the changing trade pattern is critical in customs fraud detection.
As new goods are imported and novel frauds arise, a drift-aware fraud detection
system is needed to detect both known frauds and unknown frauds within a
limited budget. The current paper proposes ADAPT, an adaptive selection method
that controls the balance between exploitation and exploration strategies used
for customs fraud detection. ADAPT makes use of the model performance trends
and the amount of concept drift to determine the best exploration ratio at
every time. Experiments on data from four countries over several years show
that each country requires a different amount of exploration for maintaining
its fraud detection system. We find the system with ADAPT can gradually adapt
to the dataset and find the appropriate amount of exploration ratio with high
performance.
