Machine Learning With Engineered Features To Identify Fraud In Point-Of-Sale Systems Open Access
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Restaurant servers are an example of an insider threat to the security of restaurant financial data. This paper applies machine learning to detect the digital representation of malevolent behavior of restaurant employees. The results of this research could be used to notify restaurant owners in real time when fraud is being committed. 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. By developing engineered features, and applying undersampling and oversampling class balancing techniques, and statistically generated data we show that ML techniques can improve fraud detection performance. In particular, detection with a Random Forest model using cross validation can be increased 55% by oversampling the minority class to the same size as the majority class. And results with a Neural Net model trained to detect fraud on the first year the restaurant opened, and tested on data from the following year can be improved by 50% by decreasing the majority class to be the same size as the minority class. We show that with statistically generated data, the performance of preprocessing features matches the performance of engineered features and achieve 99.9% F1-Scores.