Data-driven fraud detection in international shipping.

Abstract

Document fraud constitutes a growing problem in international shipping. Shipping documentation may be deliberately manipulated to avoid shipping restrictions or customs duties. Well-known examples of such fraud are miscoding and smuggling. These are cases in which the documentation of a shipment does not correctly or entirely describe the goods in transit. In an attempt to reduce the risks of document fraud, shipping companies and customs authorities typically perform random audits to check the accompanying documentation of shipments. Although these audits detect many fraud schemes, they are quite labor intensive and do not scale to the massive amounts of cargo that is shipped each day. This paper investigates whether intelligent fraud detection systems can improve the detection of miscoding and smuggling by analyzing large sets of historical shipment data. We develop a Bayesian network that predicts the presence of goods on the cargo list of shipments. The predictions of the Bayesian network are compared with the accompanying documentation of a shipment to determine whether document fraud is perpetrated. We also show how a set of discriminative models can be derived from the topology of the Bayesian network and perform the same fraud detection task. Our experimental results show that intelligent fraud detection systems can considerably improve the detection of miscoding and smuggling compared to random audits.