mlsanityData to 7 Oct 2026

Knowledge Sharing via Domain Adaptation in Customs Fraud Detection

arXiv 2201.06759 ↗First posted 18 Jan 2022cs.AI cs.CY cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 25 of its 39 references that are in the map. First posted on arXiv 18 Jan 2022.

Builds on

25 of 39 references.

References · 25

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13 more
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Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 1.

5 papers.

  1. 2022FedX: Unsupervised Federated Learning with Cross Knowledge Distillationcited by 40
  2. 2023FedDefender: Client-Side Attack-Tolerant Federated Learningcited by 12
  3. 2022Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Fieldcited by 1
  4. 2024FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learningcited by 0
  5. 2025CleverCatch: A Knowledge-Guided Weak Supervision Model for Fraud Detectioncited by 0