  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.
