An Efficient Deep Learning Classification Model for Predicting Credit Card Fraud on Skewed Data

Abstract

Due to fast-evolving technology, the world is moving to the use of credit cards rather than money in their daily lives, giving rise to many new opportunities for fraudsters to use credit cards maliciously. Based on the Nilson report, losses related to global cards were estimated to be over $35 billion by 2020. In order to maintain the security of users of these cards, the credit card company must develop a service to ensure that users are protected from any risks they may be exposed to. For this reason, we introduce a fraud detection model, denoted ST-BPNN, which is based on machine and deep learning approaches to identify fraudulent transactions. ST-BPNN was applied on real fraud detection data provided by the European bank. Comparing the obtained results from ST-BPNN with a recent state-of-the-art approach shows that our proposed model demonstrates high predictive performance for detecting fraudulent transactions.

Hybrid Undersampling and Oversampling for Handling Imbalanced Credit Card Data

“…The performance of the classifiers on balanced data showed that RF with SMOTE and SMOTE-Tomek were the best. Two other papers, [6] and [7], applied SMOTE-Tomek to a credit card transaction dataset to solve the problem of data imbalance. They found that using SMOTE-Tomek improves the learning rate and outperforms the detection model performance with imbalanced datasets.…”