DEAL – ‘Deep Ensemble ALgorithm’ Framework for Credit Card Fraud Detection in Real-Time Data Stream with Google TensorFlow

Abstract

The state-of-the-art Credit Card Fraud Detection (CCFD) systems utilize supervised rule-based schemes to identify fraudulent behaviors in static data. Designing an efficient framework for CCFD is particularly challenging due to the imbalanced distribution of positive and negative classes in data. Conventional works overlook hidden transaction patterns and the performance evaluation methods of algorithm or system. The early models have also been criticized for over-fitting, i.e. models having less prediction accuracy than training or classification accuracy. However, the categorical accuracy is more important than overall accuracy for extremely imbalanced data; most of the previous works do not evaluate their model on the basis of categorical accuracy. In this work, we propose a predictive framework (DEAL) based on novel Deep Ensemble Learning for detecting fraudulent transaction in real-time data stream. The proposed framework is adaptive against data imbalance and robust against latent transaction patterns such as spending behavior. To build the model and predict the fraud, the transactions from real data from a major bank are passed as tensors to ensemble of Deep Learning Network. The adaptive optimization is suggested to minimize the objective function and improve fraud prediction. All Experiments are conducted in Python using scikit-learn, Google TensorFlow, and Keras Deep Learning libraries. The results demonstrate its superiority over state-of-the-art methods in catching frauds.