Effective financial risk management is essential for safeguarding individual institutions and maintaining systemic stability, as risks such as accumulation, contagion, and external shocks can rapidly propagate through the financial ecosystem. Predicting these risks requires models capable of integrating heterogeneous, multi-source data that characterize modern financial environments. This study presents a deep learning framework for financial risk prediction that leverages diverse features from the FinBench dataset, a comprehensive benchmark consolidating hundreds of financial datasets. The proposed framework adopts a multi-source pretraining and fine-tuning strategy to learn transferable, high-quality representations across varied financial tabular data. By integrating tabular, temporal, and categorical information through transformer-based architectures, the model captures complex cross-feature dependencies that traditional approaches often overlook. Extensive experiments show that our framework consistently outperforms classical machine learning methods and state-of-the-art tabular deep learning models across credit default, fraud detection, and customer churn prediction tasks. Ablation studies further demonstrate the critical roles of backbone architecture and pretraining design in achieving superior predictive performance. These findings highlight the potential of deep representation learning on multi-source financial data as a modern and robust solution for accurate, generalizable financial risk assessment.
Wang et al. (2026) studied this question.