FinGAN: Chaotic generative adversarial network for analytical customer relationship management in banking and insurance

Abstract

Credit card churn prediction, insurance fraud detection, and loan default prediction are all critical analytical customer relationship management (ACRM) problems. Since these events occur infrequently, datasets for these problems are highly unbalanced. Consequently, when trained on such unbalanced datasets, all machine learning classifiers tend to produce high false positive rates. We propose two methods for data balancing. To oversample the minority class, we proposed an innovative GAN called chaoticGAN, where we employed chaotic noise as input for the generator. We also employed the traditional GAN (Goodfellow et al. in Adv Neural Inf Process Syst, 2014. https://doi.org/10.1145/3422622), Wasserstein GAN (Arjovsky et al. in Wasserstein GAN, 2017. https://arxiv.org/abs/1701.07875), and CTGAN (Xu et al. in Modeling Tabular Data using Conditional GAN. https://arxiv.org/pdf/1907.00503) independently for baseline comparison. On the data balanced by GANs, we employed a host of machine learning classifiers, including Random Forest, Decision Tree, Support Vector Machine (SVM), Logistic Regression (LR), multi-layer perceptron (MLP) and Light gradient boosting machine (LGBM) to demonstrate the efficacy of our approaches. In the second approach, we augment the oversampled synthetic minority class data obtained by GAN and its variants with the undersampled majority class data obtained by one class support vector machine (OCSVM) (Tax et al. in Mach Learn 54:45–66, 2014). We passed the entire modified dataset to build the classifiers. Our proposed approaches outperform earlier studies on the same datasets in terms of the area under the ROC curve (AUC). Further, our proposed chaoticGAN and its hybrid turned out to be statistically similar to the state-of-the-art CTGAN on all datasets while being significant over other methods w.r.t AUC over tenfold cross-validation.