DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection

Sundong Kim, Yu Che Tsai, Karandeep Singh, Yeonsoo Choi, Etim Ibok, Cheng Te Li, Meeyoung Cha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Intentional manipulation of invoices that lead to undervaluation of trade goods is the most common type of customs fraud to avoid ad valorem duties and taxes. To secure government revenue without interrupting legitimate trade flows, customs administrations around the world strive to develop ways to detect illicit trades. This paper proposes DATE, a model of Dual-task Attentive Tree-aware Embedding, to classify and rank illegal trade flows that contribute the most to the overall customs revenue when caught. The strength of DATE comes from combining a tree-based model for interpretability and transaction-level embeddings with dual attention mechanisms. To accurately identify illicit transactions and predict tax revenue, DATE learns simultaneously from illicitness and surtax of each transaction. With a five-year amount of customs import data with a test illicit ratio of 2.24%, DATE shows a remarkable precision of 92.7% on illegal cases and a recall of 49.3% on revenue after inspecting only 1% of all trade flows. We also discuss issues on deploying DATE in Nigeria Customs Service, in collaboration with the World Customs Organization.

Original languageEnglish
Title of host publicationKDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages2880-2890
Number of pages11
ISBN (Electronic)9781450379984
DOIs
Publication statusPublished - 2020 Aug 23
Event26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020 - Virtual, Online, United States
Duration: 2020 Aug 232020 Aug 27

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020
CountryUnited States
CityVirtual, Online
Period20-08-2320-08-27

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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