  Machine learning has automated much of financial fraud detection, notifying
firms of, or even blocking, questionable transactions instantly. However, data
imbalance starves traditionally trained models of the content necessary to
detect fraud. This study examines three separate factors of credit card fraud
detection via machine learning. First, it assesses the potential for different
sampling methods, undersampling and Synthetic Minority Oversampling Technique
(SMOTE), to improve algorithm performance in data-starved environments.
Additionally, five industry-practical machine learning algorithms are evaluated
on total fraud cost savings in addition to traditional statistical metrics.
Finally, an ensemble of individual models is trained with a genetic algorithm
to attempt to generate higher cost efficiency than its components. Monte Carlo
performance distributions discerned random undersampling outperformed SMOTE in
lowering fraud costs, and that an ensemble was unable to outperform its
individual parts. Most notably,the F-1 Score, a traditional metric often used
to measure performance with imbalanced data, was uncorrelated with derived cost
efficiency. Assuming a realistic cost structure can be derived, cost-based
metrics provide an essential supplement to objective statistical evaluation.
