  A major challenge when trying to detect fraud is that the fraudulent
activities form a minority class which make up a very small proportion of the
data set. In most data sets, fraud occurs in typically less than 0.5% of the
cases. Detecting fraud in such a highly imbalanced data set typically leads to
predictions that favor the majority group, causing fraud to remain undetected.
We discuss some popular oversampling techniques that solve the problem of
imbalanced data by creating synthetic samples that mimic the minority class. A
frequent problem when analyzing real data is the presence of anomalies or
outliers. When such atypical observations are present in the data, most
oversampling techniques are prone to create synthetic samples that distort the
detection algorithm and spoil the resulting analysis. A useful tool for anomaly
detection is robust statistics, which aims to find the outliers by first
fitting the majority of the data and then flagging data observations that
deviate from it. In this paper, we present a robust version of ROSE, called
robROSE, which combines several promising approaches to cope simultaneously
with the problem of imbalanced data and the presence of outliers. The proposed
method achieves to enhance the presence of the fraud cases while ignoring
anomalies. The good performance of our new sampling technique is illustrated on
simulated and real data sets and it is shown that robROSE can provide better
insight in the structure of the data. The source code of the robROSE algorithm
is made freely available.
