A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Abstract

Credit cards play an essential role in today's digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase in credit card fraud. Machine learning (ML) algorithms have been utilized for credit card fraud detection. However, the dynamic shopping patterns of credit card holders and the class imbalance problem have made it difficult for ML classifiers to achieve optimal performance. In order to solve this problem, this paper proposes a robust deep-learning approach that consists of long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks as base learners in a stacking ensemble framework, with a multilayer perceptron (MLP) as the meta-learner. Meanwhile, the hybrid synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) method is employed to balance the class distribution in the dataset. The experimental results showed that combining the proposed deep learning ensemble with the SMOTE-ENN method achieved a sensitivity and specificity of 1.000 and 0.997, respectively, which is superior to other widely used ML classifiers and methods in the literature.

Development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systems

“…This method aimed to develop trustworthy anticipations for situations that were not only fraudulent but also for situations that were not fraudulent. The study presents a complete analysis of numerous ensemble classifications for credit card fraud identification [5]. This analysis was carried out by regression and voting.…”

Detecting Frauds and Payment Defaults on Credit Card Data Inherited With Imbalanced Class Distribution and Overlapping Class Problems: A Systematic Review

“…The researchers obtained the highest result for all evaluation measurements using deep learning and neural networks. Researchers [92] and [88] used sampling methods with ensemble learning and deep learning, respectively, and both achieved a 100% True Positive Rate (TPR). Researchers [23] and [86] are not far behind, who used neural networks and achieved TPRs of 99.98% and 99.94%, respectively.…”

“…As for the Taiwan dataset, [92] obtained the highest TPR of 98.09% using sampling methods and ensemble learning for detecting payment defaults. It is followed by [88], where the researchers used sampling methods and deep learning to achieve a TPR of 93%. Researchers [63] and [20] were using sampling methods and ensemble learning.…”

Deep convolutional neural network based synthetic minority over sampling technique: a forfending model for fraudulent credit card transactions in financial institution

“…High dimensionality and sparsity in dataset is extra problem, amongst real-time detection, and complexities in the design of a genuine fraud detection models. All these limitations, makes research in this field extremely challenging and raise concerns as research gaps [23,24].…”