A Hybrid Machine Learning Model for Credit Card Fraud Detection

Abstract

The rapid evolution of digital banking and e-commerce puts online payment fraud problems at another level. Because of the large class imbalance present, coupled with the ever-changing nature of fraud, in the instance of credit card fraud, it becomes doubly challenging to achieve real-time detection. Fraudsters continuously evolve and modify their strategies to capture the weaknesses in the digital banking system. To contribute to this problem, this paper provides an approach to detect credit card fraud in the context of machine learning, with a comprehensive set of data preparation methods, the Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance, normalization, feature selection by the information gain, and a hybrid approach using two different supervised learning techniques. The study is based on a readily accessible dataset of European credit card transactions from September 2013, which is composed of 284,807 transactions, of which only 492 (0.172%) are considered to belong to the fraudulent class. In this approach, a Support Vector Machine (SVM) is used to define the critical decision boundaries, and probabilistic outputs from the SVM are used as input to train an artificial neural network (ANN). This hybrid SVM-ANN approach leverages the strengths of both models and improves the classification performance using margin-based learning and nonlinear representation technologies. The study tackles some of the major issues of fraud detection, such as the use of feature sets transformed by PCA for confidentiality, the evaluation of metrics for models that capture rare events, and the generalization of models to be used in imbalanced situations. The proposed approach performs most other traditional classifiers, as evidenced by the experiments and the stated evaluation metrics, which include accuracy, precision, F-measure, and recall. The results show how important it is to use hybrid modeling and strong preprocessing techniques to build scalable and reliable systems for credit card fraud detection.