WCO DATE to help customs detect potential fraudulent transactions

Comptroller General of Nigeria Customs Service Hameed Ali

A network model that will help customs to better detect transactions presenting risks of fraud has been developed by the World Customs Organisation (WCO).

The model known as Dual-Attentive-Tree-aware-Embedded (DATE) is art of the WCO BACUDA (Band of Customs Data Analysts) project with the Institute of Basic Science (IBS) and the National Cheng Kung University (NCKU).

The DATE model has been accepted by KDD2020[1] Conference (Applied Data Science Track) and will be published in the KDD2020 proceedings as a full paper[2].

The WCO BACUDA project was launched in September 2019 as a collaborative research platform focused on data analytics. With the participation of Nigeria Customs Service (NCS), BACUDA experts successfully developed the DATE model, and have been implementing a pilot test to verify its performance with real-time import data of the two Nigerian ports in Tin Can (in Lagos) and Onne (in Port-Harcourt) since March 2020.

The model employed a cutting-edge Artificial Intelligence (AI) mechanism called “ATTENTION” that is used as a language translation tool and for self-driving cars. Thanks to this innovative technology, the model has outperformed other traditional machine learning models (such as XGBoost) in detecting potential fraudulent transactions. The model noticeably outperforms even with relatively small-sized training data (from countries with low trade volumes) and low inspection rates (from countries with huge trade volumes).

How does the DATE model works?

Imagine that you are the head of a Customs Targeting Centre (neural network) composed of 100 risk analysts (decision trees). You want the analysts to report the probability of undervaluation and estimate the additional revenue from the inspection (dual-task).

Analyzing 100 different reports in order to make a final decision is a tedious task. Averaging the different predictions will lead to a loss of valuable information that may be hidden in any of the 100 reports. That is where the DATE model becomes handy as it keeps all the information while focusing on more important data. Some of the advantages of the model are:

If there are a majority group of reports significantly similar to each other, it allows you to pay more attention to those reports;

If you have analysts specialized in specific HS codes and importers of your target (i.e., the given import), you may pay more attention to their reports; and

Your final decision will reflect the reports that attract higher attention.

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