  Churn prediction in credit cards, fraud detection in insurance, and loan
default prediction are important analytical customer relationship management
(ACRM) problems. Since frauds, churns and defaults happen less frequently, the
datasets for these problems turn out to be naturally highly unbalanced.
Consequently, all supervised machine learning classifiers tend to yield
substantial false-positive rates when trained on such unbalanced datasets. We
propose two ways of data balancing. In the first, we propose an oversampling
method to generate synthetic samples of minority class using Generative
Adversarial Network (GAN). We employ Vanilla GAN [1], Wasserstein GAN [2] and
CTGAN [3] separately to oversample the minority class samples. In order to
assess the efficacy of our proposed approach, we use a host of machine learning
classifiers, including Random Forest, Decision Tree, support vector machine
(SVM), and Logistic Regression on the data balanced by GANs. In the second
method, we introduce a hybrid method to handle data imbalance. In this second
way, we utilize the power of undersampling and over-sampling together by
augmenting the synthetic minority class data oversampled by GAN with the
undersampled majority class data obtained by one-class support vigor machine
(OCSVM) [4]. We combine both over-sampled data generated by GAN and the data
under-sampled by OCSVM [4] and pass the resultant data to classifiers. When we
compared our results to those of Farquad et al. [5], Sundarkumar, Ravi, and
Siddeshwar [6], our proposed methods outperform the previous results in terms
of the area under the ROC curve (AUC) on all datasets.
