Abstract
This paper applies machine learning (ML) techniques including neural networks, support vector machines Random Forest, and Adaboost to detecting insider fraud in restaurant point-of-sales data. With considerable engineering of the features, and by applying under-sampling techniques we show that ML techniques deliver very high fraud-detection performance. In particular, RandomForest can achieve 91% or better across all metrics when using a model trained on one restaurant to detect fraud in a separate restaurant. However, there must be sufficient fraud samples in the model for this to occur. Knowledge and techniques from this research could be used to develop a low-cost product to automate fraud detection for restaurant owners.
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References
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Acknowledgement
Data and expertise on normal restaurant server practices, and restaurant fraud was provided by an industry expert with over 20 years experience selling, installing, upgrading, troubleshooting, and providing training for POS systems in multiple geographic regions within the United States.
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Hines, C., Youssef, A. (2018). Machine Learning Applied to Point-of-Sale Fraud Detection. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10934. Springer, Cham. https://doi.org/10.1007/978-3-319-96136-1_23
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DOI: https://doi.org/10.1007/978-3-319-96136-1_23
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