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A major challenge when trying to detect fraud is that the fraudulentactivities form a minority class which make up a very small proportion of thedata set. In most data sets, fraud occurs in typically less than 0.5% of thecases. Detecting fraud in such a highly imbalanced data set typically leads topredictions that favor the majority group, causing fraud to remain undetected.We discuss some popular oversampling techniques that solve the problem ofimbalanced data by creating synthetic samples that mimic the minority class. Afrequent problem when analyzing real data is the presence of anomalies oroutliers. When such atypical observations are present in the data, mostoversampling techniques are prone to create synthetic samples that distort thedetection algorithm and spoil the resulting analysis. A useful tool for anomalydetection is robust statistics, which aims to find the outliers by firstfitting the majority of the data and then flagging data observations thatdeviate from it. In this paper, we present a robust version of ROSE, calledrobROSE, which combines several promising approaches to cope simultaneouslywith the problem of imbalanced data and the presence of outliers. The proposedmethod achieves to enhance the presence of the fraud cases while ignoringanomalies. The good performance of our new sampling technique is illustrated onsimulated and real data sets and it is shown that robROSE can provide betterinsight in the structure of the data. The source code of the robROSE algorithmis made freely available.

robROSE: A robust approach for dealing with imbalanced data in fraud detection