International Journal of Scientific Research and Engineering Development

The financial sector produces vast amounts of diverse information, including structured tables such as transaction records, sequential time-series like market movements, and unstructured text from disclosures and reports. These different data types are essential for key applications such as fraud detection, credit risk analysis, and ensuring compliance with regulations. However, their heterogeneous nature makes generative modelling highly challenging. Diffusion models have shown strong results in areas such as computer vision and speech. However, most of these models are built for one type of data and face challenges when creating realistic financial datasets that combine different data formats and meet specific financial rules. To overcome this issue, FinDiff++ is introduced—a diffusion-based framework for finance that brings together tabular data, time-series data, and text in a single system. Built on the denoising diffusion probabilistic model (DDPM), FinDiff++ introduces three core improvements: schema-driven conditioning for tabular data, temporal encoders for capturing sequential patterns, and fusion layers to connect multiple data types. It also incorporates financial ontologies and compliance rules, ensuring that generated data is reliable, realistic, and regulation-aware. Experiments on anonymized banking transactions, stock market time-series (S&P 500, NASDAQ), and SEC filing texts show that FinDiff++ outperforms GANs and VAEs in fidelity, task utility, and compliance. Fraud detection models achieved higher accuracy (AUC 0.91 vs. 0.84) and risk assessment performance was within 3% of real data. In summary, FinDiff++ bridges the gap between state-of-the-art generative AI and the unique requirements of financial analytics. It lays the foundation for scalable, regulation-compliant, and high-utility synthetic data generation, with potential extensions to market simulation and multi-agent financial systems.