Customs Fraud Detection in the Presence of Concept Drift
This addresses fraud detection for customs agencies, but it is incremental as it builds on existing drift-aware methods.
The paper tackles the problem of customs fraud detection under concept drift by proposing ADAPT, an adaptive method that balances exploitation and exploration based on performance trends and drift amount, showing it can adapt to different countries' data with high performance.
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.