Uncovering Document Fraud in Maritime Freight Transport Based on Probabilistic Classification

Conference object, Part of book or chapter of book English OPEN
Triepels, Ron ; Feelders, Ad ; Daniels, Hennie (2015)
  • Publisher: Springer
  • Related identifiers: doi: 10.1007/978-3-319-24369-6_23
  • Subject: [INFO] Computer Science [cs] | [ INFO ] Computer Science [cs] | [ SHS.INFO ] Humanities and Social Sciences/Library and information sciences | Data mining | Fraud detection | Freight forwarding | Global supply chains | [SHS.INFO] Humanities and Social Sciences/Library and information sciences
    acm: ComputerApplications_COMPUTERSINOTHERSYSTEMS

Part 4: Data Analysis and Information Retrieval; International audience; 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.
  • References (23)
    23 references, page 1 of 3

    1. Bolton, R.J., Hand, D.J.: Statistical fraud detection: A review. Statistical Science, 235-249 (2002)

    2. Chang, Y.S., Son, M.G., Oh, C.H.: Design and implementation of rfid based aircargo monitoring system. Advanced Engineering Informatics 25(1), 41-52 (2011)

    3. Chickering, D.M.: Learning bayesian networks is np-complete. In: Learning fromdata, pp. 121-130. Springer (1996)

    4. Choi, T.Y., Hartley, J.L.: An exploration of supplier selection practices across the supply chain. Journal of Operations Management 14(4), 333-343 (1996)

    5. Chow, C., Liu, C.: Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory 14(3), 462-467 (1968)

    6. Digiampietri, L.A., Roman, N.T., Meira, L.A., Ferreira, C.D., Kondo, A.A., Constantino, E.R., Rezende, R.C., Brandao, B.C., Ribeiro, H.S., Carolino, P.K., et al.: Uses of artificial intelligence in the brazilian customs fraud detection system. In: Proceedings of the 2008 International Conference on Digital Government Research, pp. 181-187. Digital Government Society of North America (2008)

    7. Edwards, D., De Abreu, G.C., Labouriau, R.: Selecting high-dimensional mixed graphical models using minimal aic or bic forests. BMC Bioinformatics 11(1), 18 (2010)

    8. Eurostat: EU trade since 1995 by HS6 (2015)

    9. Filho, J., Wainer, J.: Using a hierarchical bayesian model to handle high cardinality attributes with relevant interactions in a classification problem. In: IJCAI, pp. 2504-2509 (2007)

    10. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29(2-3), 131-163 (1997)

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