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Hybrid bio-inspired model for fraud detection with correlation based feature selection

Abstract The credit card is one of the most used forms of online transactions accepted by almost all the service payment systems. The increasing popularity of credit card use is increasing the challenge and fraud cases. This paper proposes a hybrid model based on the feature selection method, firefly bio-inspired algorithm, and random forest ensemble classifier (namely HBRF) for fraud detection and overcomes the imbalanced dataset issue in the banking domain. The firefly bio-inspired algorithm and the correlation-based feature selection (CFS) method have been applied to optimize the predictive features. The proposed HBRF model has been implemented on the Brazilian credit card dataset, and its performance has been compared with machine learning techniques such as Naive Bayes, KNN, Logistic Regression, Support Vector Machine, and existing methods. The proposed HBRF model has achieved an accuracy of 96.23% with a low error rate of 3.77%. It has been identified that the HBRF model outperforms state-of-the-art classifiers and existing techniques.