# Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection
## 1 Introduction  
Recent studies estimate that the fraud detection and prevention market is valued at USD 19.5 billion. According to the Consumer Sentinel Network in the USA, among the 3.2 million identity theft and fraud reports in 2019, 1.7 million involved fraud [1]. Of these cases, 23% reported financial losses, highlighting the significant impact on both institutions and individuals. Rapid detection of fraudulent activities is crucial and should occur as soon as streams containing relevant financial data are received. This urgency results in extensive datasets within financial institutions, which are often complex due to the diverse features recorded in transactions [2]. Figure 1 shows the statistical number discussed above in a better manner. Financial institutions are tasked with the critical challenge of quickly and accurately identifying and isolating fraudulent transactions while maintaining a smooth customer experience. \"Quickly\" emphasizes the need for a detection model that minimizes delays, protecting both customers and institutions from potential issues. Meanwhile, \"accurately\" highlights the importance of precise fraud detection, as false positives can lead to unnecessary resource allocation [3]. Traditionally, fraud detection methods, such as manual review or rule-based models, have shown limited effectiveness. Manual detection is slow, requiring a long time to conclude, while rule-based approaches involve complex rules that must be applied and assessed before a transaction can be labeled as suspicious [4]. Both methods demand significant effort to establish criteria for identifying fraudulent transactions and struggle to detect new, unknown, and sophisticated fraud patterns. For this reason, financial institutions spend a lot of money searching for powerful techniques to prevent fraudulent transactions with higher accuracy by employing artificial intelligence. AI-driven fraud detection systems provide unmatched speed, efficiency, and adaptability [5]. Machine learning models (ML), such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, XGBoost, LightGBM, K-Nearest Neighbors, Naive Bayes, AdaBoost, and Bagging Classifier, offer a range of solutions for detecting fraudulent transactions [6]. These models are effective at handling various data patterns and can be used individually or combined to enhance fraud detection capabilities [7]. Likewise, deep learning techniques (DL), including Long Short-Term Memory networks, Artificial Neural Networks, and Recurrent Neural Networks, further strengthen these solutions by analyzing large datasets and uncovering intricate patterns [8]. The integration of these ML and DL models into hybrid algorithms provides a comprehensive approach to fraud detection. Hybrid models leverage diverse techniques to improve detection accuracy, reduce false positives, and adapt to evolving fraud tactics [9]. They also optimize computational resources for scalability, ensuring efficient performance even with large-scale data. As financial fraud becomes increasingly sophisticated, the application of advanced ML and DL models helps institutions stay ahead of threats, manage risks effectively, comply with regulations, and protect against financial losses [10]. Class imbalance is a critical challenge in machine learning problems because it poses significant issues for most machine learning algorithms. By default, these algorithms optimize overall accuracy, which can cause models to ignore the minority class, prioritizing correct predictions for the majority while failing to detect rare but critical instances. For example, in fraud detection, fraudulent transactions may represent less than 1% of all data, allowing a model to achieve 99% accuracy by naively labeling every transaction as  
Figure 1: Fraud detection and prevention market data. legitimate [11]. However, accurately identifying rare fraudulent transactions is critical for financial institutions to prevent losses.