Uncovering Document Fraud in Maritime Freight Transport Based on Probabilistic Classification

Deficient visibility in global supply chains causes significant
risks for the customs brokerage practices of freight forwarders. One of the
risks that freight forwarders face is that shipping documentation might
contain document fraud and is used to declare a shipment. Traditional
risk controls are ineffective in this regard since the creation of shipping
documentation is uncontrollable by freight forwarders. In this paper, we
propose a data mining approach that freight forwarders can use to detect
document fraud from supply chain data. More specifically, we learn models
that predict the presence of goods on an import declaration based on
other declared goods and the trajectory of the shipment. Decision rules
are used to produce miscoding alerts and smuggling alerts. Experimental
tests show that our approach outperforms the traditional audit strategy
in which random declarations are selected for further investigation.