# Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection
## 2 Related Work
To manage the class imbalance problem, the study employs the SMOTE-ENN method. As a result, they achieve a sensitivity of 1.000 and a specificity of 0.997, outperforming other commonly used machine learning classifiers and methods. This research underscores the potential of combining advanced deep learning techniques with data balancing strategies to improve credit card fraud detection systems. In addition, ref. [26] proposes a two-stage framework that uses a deep Autoencoder for representation learning, followed by supervised deep learning techniques for fraud detection. This approach significantly improves the performance of deep learning classifiers compared to those trained on original data and other methods like PCA. The findings highlight the effectiveness of this advanced method in enhancing fraud detection systems. Likewise, the authors in [27] proposed a framework called HNN-CUHIT that combines a hybrid neural network with a clustering-based undersampling technique, leveraging identity and transaction features. They evaluated their solution on a real dataset from a city bank during the SARS-CoV-2 pandemic in 2020. As a result, the proposed solution outperforms traditional models such as Logistic Regression, Random Forest, and CNN, particularly in handling imbalanced class distributions by achieving the best F1 score in fraud detection, highlighting its superior performance in identifying fraudulent transactions. This innovative approach offers a valuable contribution to improving fraud detection in the financial sector. Furthermore, the study [28] proposes federated learning frameworks, such as TensorFlow Federated and PyTorch. Their solution aims to enhance detection across banks without sharing sensitive data. They compare individual and hybrid resampling techniques, which prove that Random Forest classifiers outperform other models, achieving the best performance metrics. The PyTorch framework yields higher prediction accuracy for federated learning models, though with increased computational time, highlighting its effectiveness in handling skewed datasets. In addition, the study [29] tackles the challenge of acquiring labeled datasets, particularly in highly class-imbalanced domains like credit card fraud detection. It introduces a novel methodology using Autoencoders to synthesize class labels for such data. This approach minimizes the need for expert input by leveraging an error metric from the Autoencoder to create new binary class labels. These labels are then used to train supervised classifiers for fraud detection. Conducted experiments demonstrate that the synthesized labels are of high quality, significantly improving classifier performance as measured by the area under the precision-recall curve. The study also shows that increasing the proportion of positive-labeled instances enhances classifier performance, effectively addressing class imbalance concerns. In [30], the authors focus on developing a real-time fraud detection framework that can adapt to the constantly changing fraud characteristics, handle the class imbalance, and complete separation issues inherent in fraud data. The proposed solution includes a novel approach to managing non-stationary changes in transaction patterns and a robust fuzzy logistic regression model to tackle class imbalance and separation problems. This methodology improves model training efficiency and maintains high specificity and sensitivity, even with small sample sizes. The framework achieves an accuracy greater than 0.99 in identifying fraudulent and non-fraudulent transactions, outperforming other machine learning and fraud detection methods. The enhanced classification performance ensures better precision in detecting fraudulent transactions, reduces false positives, and minimizes financial losses while increasing customer satisfaction. Otherwise, [PERSON] et al. [31] propose a new solution based on Graph Neural Networks (GNNs) for credit card fraud detection.