# Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection
## 1 Introduction
The imbalance makes it difficult for classifiers to effectively learn from the limited examples of fraudulent transactions [12]. Traditional methods designed for balanced datasets often focus on overall accuracy, which can result in poor performance in detecting the minority class. To tackle this issue, several techniques have been developed [13]. Data-level methods, such as oversampling and undersampling, aim to adjust the dataset to mitigate the imbalance. Oversampling increases the number of fraudulent transaction samples by duplicating them, while undersampling reduces the number of legitimate transactions, potentially at the cost of losing valuable information [4]. Advanced methods like the Synthetic Minority Oversampling Technique (SMOTE) generate synthetic examples of fraudulent transactions [14], and its variants--like the Adaptive Synthetic Sampling Approach (ADASYN) [15], Borderline-SMOTE, Majority Weighted Minority Oversampling Technique (MWMOTE), and Weighted Kernel-Based SMOTE--generate synthetic samples to better balance the dataset [16], helping to balance the dataset without risking overfitting. These approaches are essential for improving detection rates and effectively managing class imbalance in fraud detection systems. Generative modeling has recently garnered significant attention due to its effectiveness in handling diverse types of data and simulating sample behaviors [17]. Its applicability extends to various domains, including image generation and noise reduction [18]. This paper aims to leverage the capabilities of generative modeling to address the challenge of imbalanced credit card fraud detection. Specifically, we propose several models: an Autoencoder, a Variational Autoencoder (VAE), a Generative Adversarial Network (GAN), and a hybrid architecture that combines a GAN with an Autoencoder. These techniques are used for data augmentation by exploiting their ability to mimic synthetic datasets. The choice of these models is justified by their proven effectiveness in generating synthetic images and tabular datasets across various fields. To evaluate the proposed solutions, we conducted extensive experiments using a real-world credit card dataset. We utilized various standard evaluation metrics and introduced a new metric, the Balanced Fraud Detection Score (BFDS), which combines these metrics for more accurate results and to identify the best-performing methods. Our contributions can be summarized as follows:  
* Proposal of Machine Learning and deep learning Models: Several advanced machine learning and deep learning models are proposed for detecting fraudulent transactions. * Generative Models for Handling Imbalanced Learning: To address the issue of class imbalance, we propose multiple generative models to create synthetic fraudulent samples based on historical datasets, including Autoencoders, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and a hybrid model combining GANs with Autoencoders. These models aim to balance the dataset and improve the detection of rare fraudulent transactions. * Introduction of a New Evaluation Metric: We introduce a novel metric called the Balanced Fraud Detection Score (BFDS) that combines accuracy, precision, sensitivity, G-mean, and specificity to provide a comprehensive assessment of model performance. * Empirical Validation and Comparison: Extensive experiments are conducted using a real-world credit card dataset. The results demonstrate the effectiveness of our generative modeling solutions in classifying transactions and highlight their superior performance compared to traditional methods like SMOTE and ADSYN based on the BFDS metric. These efforts aim to advance the field of fraud detection by providing innovative solutions to class imbalance and enhancing the performance of detection systems.