Knowledge Sharing via Domain Adaptation in Customs Fraud Detection
This addresses the problem of tax evasion and illicit trade for customs administrations, particularly in infrastructure-weak countries, though it appears incremental as it builds on existing domain adaptation techniques.
The paper tackles customs fraud detection by proposing a domain adaptation platform (DAS) that enables knowledge sharing across countries while protecting local data, resulting in up to 2-11 times improvement in fraud detection for participating nations.
Knowledge of the changing traffic is critical in risk management. Customs offices worldwide have traditionally relied on local resources to accumulate knowledge and detect tax fraud. This naturally poses countries with weak infrastructure to become tax havens of potentially illicit trades. The current paper proposes DAS, a memory bank platform to facilitate knowledge sharing across multi-national customs administrations to support each other. We propose a domain adaptation method to share transferable knowledge of frauds as prototypes while safeguarding the local trade information. Data encompassing over 8 million import declarations have been used to test the feasibility of this new system, which shows that participating countries may benefit up to 2-11 times in fraud detection with the help of shared knowledge. We discuss implications for substantial tax revenue potential and strengthened policy against illicit trades.