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Fraud Detection in Supply Chain 4.0: A Machine Learning Model

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International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 714))

Abstract

The fourth industrial revolution has begun with the introduction of artificial intelligence into the manufacturing environment. As a result, flow management have accelerated and worldwide Supply chain have become more automated than ever. In this context, Supply Chain financial risks have increased which requires companies to be more vigilant about fraud detection in their interactions with the different stakeholders. In this paper, we establish a machine learning model to predict fraudulent operations between the different links of Supply Chain. Three models are deployed to that purpose: Random forest, K-Nearest Neighbor and logistic regression. The most optimal model is then upgraded by using grid search cross validation. Results shows that the forecasting model is more efficient when cross validation is employed with a score of 97,7%.

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Author information

Authors and Affiliations

  1. Mathematics and Computer Science Department, Faculty of Sciences and Techniques, Lab L.M.I.E.T Hassan Ist University, Settat, Morocco

    Houria Abouloifa & Mohamed Bahaj

Authors
  1. Houria Abouloifa
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  2. Mohamed Bahaj
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Corresponding author

Correspondence to Houria Abouloifa .

Editor information

Editors and Affiliations

  1. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

  2. Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

  3. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

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Abouloifa, H., Bahaj, M. (2023). Fraud Detection in Supply Chain 4.0: A Machine Learning Model. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. Lecture Notes in Networks and Systems, vol 714. Springer, Cham. https://doi.org/10.1007/978-3-031-35245-4_19

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