| Accurately predicting credit risk is essential in the financial industry to ensure stability and effective risk management. Many existing credit risk models still rely on structured data streams such as financial ratios, credit scores, and repayment histories, which are effective for historical analysis but often inadequate for identifying early warning signals of potential default. Even though unstructured data sources, which include financial news, market analyst reports, and earnings call transcripts, provide forward-looking and contextual insights, their integration with structured data remains limited due to heterogeneity, noise, redundancy, and uncertainty in information quality. This paper addresses these challenges by proposing a novel FinFusion framework for early credit risk insight. The proposed framework models structured financial attributes using a Multilayer Perceptron (MLP), while unstructured textual data are converted into dense semantic representations. To ensure reliable interaction between these modalities, a Quality-Gated Cross-Attention (QGCA) mechanism is introduced, which dynamically evaluates the relevance and reliability of unstructured signals and selectively assigns higher attention weights to risk-informative content. This gating strategy reduces the influence of noisy and irrelevant textual information and enhances alignment between structured indicators and contextual signals. Experimental results demonstrate that the proposed approach consistently outperforms conventional credit risk models and standalone deep learning methods across multiple evaluation metrics, particularly in early-stage default identification. |
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