# Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection
## 2 Related Work  
In the literature, numerous solutions have been proposed for maximizing the detection of fraudulent transactions using a variety of approaches centered on machine learning (ML) and deep learning (DL) models. To enhance these models, several strategies have been developed, including the use of statistical processes, mathematical theories, and optimization techniques such as metaheuristic algorithms [19, 20] and Bayesian optimization [21]. Additionally, various methods have been proposed to handle imbalanced learning. In the rest of this section, we provide a critical review of some significant works that aim to detect fraudulent transactions effectively and with higher accuracy. In a recent study on imbalanced classification [22], a novel approach called the clustering-based noisy-sample-removed undersampling scheme (NUS) is introduced to address the challenges faced in applications like credit card fraud detection (CCFD) and defective part identification. The study highlights the difficulties classifiers encounter due to noisy samples in both majority and minority classes. The NUS technique begins by clustering majority-class samples and then utilizes the Euclidean distance from cluster centers to define hyperspheres, identifying and excluding noisy samples. This method is applied to both majority and minority classes to enhance the classifier's performance. The effectiveness of NUS is validated by integrating it with basic classifiers such as Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR) and comparing it with seven other undersampling, oversampling, and noisy-sample-removed methods. The experiments, conducted on 13 public datasets and three real e-commerce transaction datasets, demonstrate that NUS significantly improves the performance of existing classifiers. In another paper [23], the researchers highlight the significant impact of fraud on businesses and individuals globally, where millions of US dollars are lost annually. With the surge in online transactions, credit cards have become a prevalent payment method, but they have also increased opportunities for fraudulent activities. Furthermore, the paper addresses the critical issue of data imbalance in machine learning models used for fraud detection, as fraudulent transactions constitute only a small percentage of the total data. This imbalance can severely hinder the performance of classifiers. To tackle this, the study explores various data augmentation techniques and introduces a novel model called K-means Convolutional Generative Adversarial Network (K-CGAN), which is specifically designed for credit card fraud detection. Additionally, they evaluate the effectiveness of different augmentation techniques, including B-SMOTE, K-CGAN, and SMOTE, using major classification techniques. The findings indicate that K-CGAN achieves the highest precision, recall, F1 score, and accuracy, outperforming other methods and significantly enhancing the detection of fraudulent transactions. In [24], they focused on the importance of accurately classifying fraudulent transactions to protect customers. Using machine learning methodologies, the study tested various models, finding XGBoost to perform well with a precision score of 0.91 and an accuracy score of 0.99. To address the dataset's imbalance, several sampling techniques were applied, with Random Oversampling emerging as the most effective, achieving a precision and accuracy score of 0.99 with XGBoost. The study emphasizes the significance of data-balancing methods in improving the performance of fraud detection models. Otherwise, [PERSON] et al. [25] tackle the challenges of credit card fraud detection by addressing the issues posed by dynamic shopping patterns and class imbalance. They propose a robust deep learning approach, utilizing 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) serving as the meta-learner.