Scalable Machine Learning Techniques for Highly Imbalanced Credit Card Fraud Detection: A Comparative Study

In the real world of credit card fraud detection, due to a minority of fraud related transactions, has created a class imbalance problem. With the increase of transactions at massive scale, the imbalanced data is immense and has created a challenging issue on how well Machine Learning (ML) techniques can scale up to efficiently learn to detect fraud from the massive incoming data and to respond faster with high prediction accuracy and reduced misclassification costs. This paper is based on experiments that compared several popular ML techniques and investigated their suitability as a "scalable algorithm" when working with highly imbalanced massive or "Big" datasets. The experiments were conducted on two highly imbalanced datasets using Random Forest, Balanced Bagging Ensemble, and Gaussian Naive Bayes. We observed that many detection algorithms performed well with medium-sized dataset but struggled to maintain similar predictions when it is massive.