Multimodal Insights into Credit Risk Modelling: Integrating Climate and Text Data for Default Prediction

Paper: arXiv 2601.00478

Authors: Zongxiao Wu, Ran Liu, Jiang Dai, Dan Luo

Abstract

Credit risk assessment increasingly relies on diverse sources of information beyond traditional structured financial data, particularly for micro and small enterprises (mSEs) with limited financial histories. This study proposes a multimodal framework that integrates structured credit variables, climate panel data, and unstructured textual narratives within a unified learning architecture. Specifically, we use long short-term memory (LSTM), the gated recurrent unit (GRU), and transformer models to analyse the interplay between these data modalities. The empirical results demonstrate that unimodal models based on climate or text data outperform those relying solely on structured data, while the integration of multiple data modalities yields significant improvements in credit default prediction. Using SHAP-based explainability methods, we find that physical climate risks play an important role in default prediction, with water-logging by rain emerging as the most influential factor. Overall, this study demonstrates the potential of multimodal approaches in AI-enabled decision-making, which provides robust tools for credit risk assessment while contributing to the broader integration of environmental and textual insights into predictive analytics.

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper employs established deep learning architectures (LSTM, GRU, Transformer) with SHAP for explainability, representing moderate mathematical complexity, but the focus is heavily on a specific empirical dataset and backtesting methodology.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Integrate climate & text data<br>for mSE default prediction"] --> B
  
  subgraph B ["Data Inputs"]
      B1["Structured Credit Data"]
      B2["Climate Panel Data<br>Physical Risks e.g., Water-logging"]
      B3["Unstructured Textual Narratives"]
  end

  B --> C["Computational Framework<br>Multimodal Learning Architecture"]
  
  C --> D{"Deep Learning Models"}
  D --> D1["LSTM"]
  D --> D2["GRU"]
  D --> D3["Transformer"]
  
  D --> E["Analysis & Explainability"]
  E --> F["Key Findings & Outcomes"]
  
  subgraph F ["Outcomes"]
      F1["Text & Climate models<br>outperform Structured alone"]
      F2["Multimodal integration<br>yields significant improvement"]
      F3["SHAP Analysis:<br>Physical climate risks crucial<br>Water-logging = Top factor"]
  end

Cited References (90)

1. A multi-view contrastive learning framework for spatial embeddings in risk modelling — → 2511.17954
2. State of China’s climate in 2024 (2025) — link
3. Do firms benefit from carbon risk management? Evidence from the credit default swaps market (2025) — link
4. A ‘divide and conquer’ reject inference approach leveraging graph-based semi-supervised learning (2025) — link
5. Climate risk and green innovation‐ESG disconnect: Firm‐level evidence from China (2025) — link
6. Unleashing the power of text for credit default prediction: Comparing human-written and generative AI-refined texts — → 2503.18029
7. Climate conditions, credit risk cycles, and technological progress: Evidence from China (2025) — link
8. A Good Sketch is Better than A Long Speech: Evaluate Delinquency Risk through Real-Time Video Analysis (2024) — link

5 of 90 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.