Knowledge Sharing via Domain Adaptation in Customs Fraud Detection


  • Sungwon Park KAIST
  • Sundong Kim Institute for Basic Science
  • Meeyoung Cha KAIST Institute for Basic Science



AI For Social Impact (AISI Track Papers Only), Humans And AI (HAI)


Knowledge of the changing traffic is critical in risk management. Customs offices worldwide have traditionally relied on local resources to accumulate such knowledge and detect tax frauds. 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.




How to Cite

Park, S., Kim, S., & Cha, M. (2022). Knowledge Sharing via Domain Adaptation in Customs Fraud Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12062-12070.