Financial Risk Analysis Using Integrated Data and Transformer-Based Deep Learning

Abstract

This paper explores the integration of multimodal data using Transformer models to enhance the accuracy of financial risk prediction. By combining diverse data sources such as time series, financial reports, and unstructured text (e.g., news and social media), this research offers a more holistic approach to identifying potential high-risk events in the financial market. We compare different data fusion strategies and demonstrate that the combination of textual and quantitative financial data significantly improves model performance, particularly in terms of AUC and Recall. Unlike traditional single-modality approaches, multimodal fusion enables the model to capture a wider range of risk signals, which are often latent or intertwined across data types. Our experimental results highlight the superiority of Transformer models in processing complex multimodal information, making them a powerful tool for financial risk assessment. The findings of this research are particularly relevant for regulatory bodies such as central banks, financial supervisory authorities, and risk management departments within financial institutions, as these sectors require more precise and adaptive tools to monitor market dynamics, identify systemic risks, and respond to financial crises. By leveraging the self-attention mechanisms of Transformers, this study offers an effective methodology for improving predictive accuracy in financial risk management, with practical implications for regulatory compliance and policy-making.