Machine Learning Applied to Point-of-Sale Fraud Detection

  • Christine HinesEmail author
  • Abdou YoussefEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10934)


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.


Machine learning Classification Outlier detection Fraud detection Point-of-sale data 



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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.George Washington UniversityWashington, DCUSA

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