(untitled) (Journal of Artificial Intelligence & Research, 2022)
The increasing demand for large, high-quality datasets for financial risk modeling and fraud detection in the banking and insurance sectors presents significant challenges, particularly concerning data availability, privacy concerns, and the inherent biases in existing datasets. Generative Adversarial Networks (GANs), a class of deep learning models designed to generate realistic synthetic data, offer a promising solution to these challenges. This paper examines the application of GANs for synthetic financial data generation, emphasizing their potential to enhance risk modeling and fraud detection processes. The study begins by discussing the limitations of conventional financial datasets, which are often plagued by issues such as insufficient data volume, skewed distributions, and sensitive information that can lead to privacy breaches. By generating synthetic data that closely mirrors real financial datasets in both structure and variability, GANs provide a means to overcome these limitations, allowing for more robust machine learning models for risk assessment and anomaly detection. The paper then delves into the technical architecture of GANs, comprising two neural networks—the Generator and the Discriminator—operating in a competitive framework. This adversarial process allows the Generator to create increasingly realistic synthetic data, while the Discriminator continuously improves its ability to distinguish between real and synthetic data points. The iterative nature of GAN training enables the generation of high-quality, diversified synthetic data that maintains the statistical properties of original financial datasets, thus making them highly effective for use in downstream machine learning applications such as credit scoring, anti-money laundering (AML) initiatives, and market risk analysis. Further, the study provides a comprehensive review of various GAN architectures, including Deep Convolutional GANs (DCGANs), Conditional GANs (CGANs), and Wasserstein GANs (WGANs), which have been adapted to generate financial data that is not only realistic but also informative for risk modeling purposes. In particular, Conditional GANs allow for the incorporation of additional information, such as macroeconomic indicators or customer profiles, enhancing the generation of synthetic data that is contextually relevant for specific financial applications. The robustness of these GAN-based models is evaluated in terms of their ability to replicate key statistical features, detect rare events, and model extreme value scenarios that are critical for financial risk management. In addition to discussing the potential benefits of GANs in generating synthetic financial data, the paper addresses the critical issue of model evaluation. Traditional metrics used for assessing GAN performance, such as Inception Score (IS) and Fréchet Inception Distance (FID), may not be entirely suitable for financial data due to the need for domain-specific validation measures. Therefore, this study proposes a set of tailored evaluation metrics that consider distributional similarities, temporal dependencies, and the fidelity of generated data to capture the complexities of financial systems. These metrics are applied to case studies demonstrating how synthetic data generated by GANs can be used to train machine learning models for credit risk prediction and fraud detection, showing marked improvements in predictive performance compared to models trained on conventional datasets. The paper also explores the implications of using GANs for privacy preservation and data augmentation. By generating synthetic data that does not correspond to any real-world individuals or entities, GANs mitigate the risks associated with data privacy and regulatory compliance, providing a secure way to share data across financial institutions. This is particularly important in collaborative environments, such as consortia or federated learning frameworks, where data sharing is essential but restricted by privacy laws and competitive interests. Additionally, synthetic data generated by GANs can serve as an effective data augmentation technique, enriching sparse datasets, and thereby reducing the overfitting risks associated with machine learning models in financial contexts. However, the application of GANs for synthetic financial data generation is not without challenges. One of the primary concerns is the stability of GAN training, which can be affected by issues such as mode collapse, where the Generator produces limited diversity in the generated data. This study discusses several approaches to mitigate these challenges, including the use of alternative loss functions, architectural modifications, and ensemble techniques that enhance the robustness of GANs in generating diverse financial datasets. Moreover, the paper addresses the ethical considerations and potential misuse of GAN-generated data, such as the risk of creating realistic but fraudulent financial transactions that could be exploited by malicious actors.