Enhancing structured finance risk models (Leland-Toft and Box-Cox) using GenAI (VAEs GANs)

This work explores the integration of generative artificial intelligence (GenAI), specifically Variational Autoencoders (VAEs), into statistical and structural financial models, with a focus on the Leland-Toft and Box-Cox frameworks. We conduct a comprehensive review of these models, highlighting their use in financial risk analysis, bankruptcy prediction, and time-series forecasting. Through the integration of VAEs, we demonstrate their capability to enhance data generation, improve predictive accuracy, and enable robust validation of financial models, particularly in scenarios with scarce data. The application of VAEs to the Leland-Toft model facilitated the calculation of key financial metrics, including default spreads, credit spreads, and leverage ratios. Additionally, VAEs integrated with Box-Cox models generated latent features that correlated effectively with traditional financial factors, underscoring their utility in predictive modeling and survival analysis. This work provides a detailed overview of implementation pipelines, architecture diagrams, and model validation methods, offering a foundation for future research. Expanding on the use of VAEs, we propose incorporating advanced machine learning techniques and real-time data to further enhance model performance and revolutionize financial modeling.

We have discussed how implementation of synthetic data to enhance inputs for Leland-Toft and Box-Cox can aid is robust validation of the models.