  Machine learning has opened up new tools for financial fraud detection. Using
a sample of annotated transactions, a machine learning classification algorithm
learns to detect frauds. With growing credit card transaction volumes and
rising fraud percentages there is growing interest in finding appropriate
machine learning classifiers for detection. However, fraud data sets are
diverse and exhibit inconsistent characteristics. As a result, a model
effective on a given data set is not guaranteed to perform on another. Further,
the possibility of temporal drift in data patterns and characteristics over
time is high. Additionally, fraud data has massive and varying imbalance. In
this work, we evaluate sampling methods as a viable pre-processing mechanism to
handle imbalance and propose a data-driven classifier selection strategy for
characteristic highly imbalanced fraud detection data sets. The model derived
based on our selection strategy surpasses peer models, whilst working in more
realistic conditions, establishing the effectiveness of the strategy.
