Data-driven fraud detection in international shipping
| Autor: | Ron Triepels, Hennie Daniels, Ad Feelders |
|---|---|
| Přispěvatelé: | Sub Algorithmic Data Analysis, Department of Technology and Operations Management, Research Group: Information & Supply Chain Management, Center Ph. D. Students, Department of Management |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
International shipping Logistic regression ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology Audit Computer security computer.software_genre 01 natural sciences Task (project management) Data-driven 010104 statistics & probability Probablistic classification Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 0101 mathematics General Engineering Bayesian network Computer Science Applications Bayesian networks Fraud detection Scale (social sciences) 020201 artificial intelligence & image processing computer Neural networks |
| Zdroj: | Expert Systems with Applications, 99, 193. Elsevier Limited Expert Systems with Applications, 99, 193-202. Elsevier Ltd. Expert Systems with Applications, 99, 193-202. Elsevier Limited Expert Systems with Applications, 99, 193-202. Elsevier Science |
| ISSN: | 0957-4174 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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