## 2 Related Work  
The field of synthetic data generation has experienced significant growth, driven by advancements in algorithms and increasing demands across various domains. While early research focused heavily on natural language processing (NLP) with tools like ChatGPT and BARD [7], financial markets have emerged as a critical application area. Within this sector, machine learning models, particularly those dealing with sequential data, often require large, high-quality datasets to mitigate issues like imbalance and low signal-to-noise ratios. Synthetic data generation has proven essential in addressing these challenges, enabling improved predictive accuracy for tasks such as fraud detection and credit risk assessment [8]. This section explores state-of-the-art methods for synthetic data generation, focusing on financial applications and their capacity to manage the complexities of sequential data. A Quantum Wasserstein Generative Adversarial Network with Gradient Penalty (QWGAN-GP) [9] is used to enhance financial time series prediction by generating synthetic data that replicate the statistical characteristics of the S&P 500 index. Leveraging a quantum generator and a classical discriminator, the approach effectively addresses limitations in financial datasets, such as insufficient representation of rare market events. The synthetic data generated were evaluated using metrics like Wasserstein distance and Dynamic Time Warping, showcasing high fidelity to the original data. Integrating these synthetic datasets into long short-term memory (LSTM) prediction models significantly improved the forecasting of general trends and extreme market events. Monte Carlo methods have long been valued for their flexibility in simulating stochastic processes. Among recent advancements is the fintech-kMC model, which employs continuous-time Monte Carlo techniques to simulate customer behaviour on financial platforms [10]. This model excels in detecting potential bugs in machine learning processes while maintaining simplicity in implementation. Unlike traditional generative models that depend on high-quality data pipelines, kMC dynamically adjusts time steps based on event probabilities, ensuring realistic simulations. Both static and dynamic rates are incorporated in kMC, allowing for diverse agent behaviours. Additionally, the introduction of agent archetypes ensures the simulation reflects varied customer actions. Despite its strengths, fintech-kMC has limitations, including its reliance on user-defined probability distributions and its reduced scalability for large-scale systems. Nevertheless, the approach effectively bridges the gap between realism and control in synthetic data generation for financial applications. TimeGAN represents a significant advancement in the generation of sequential data by integrating temporal dynamics into Generative Adversarial Network (GAN) architectures. Unlike traditional GANs, which struggle with multivariate time series, TimeGAN combines autoregressive components, embedding networks, and adversarial training to learn both feature representations and temporal correlations [11]. This dual learning process enables the model to generate high-quality synthetic data that closely mimic the original sequences. Evaluations of TimeGAN using metrics like diversity, fidelity, and predictive performance have shown its superiority over earlier methods such as the Recurrent Conditional GAN (RCGAN) and Continuous Recurrent Neural Network GAN (C-RNN-GAN). For example, TimeGAN demonstrates nearly perfect alignment between real and synthetic distributions, as evidenced by t-Distributed Stochastic Neighbour Embedding (t-SNE) and Principal Component Analysis (PCA) visualisations. Additionally, its predictive scores, tested on datasets like stock prices and energy consumption, are comparable to those achieved using real data, solidifying its effectiveness for real-world applications.