RiskRAG: Real-Time Credit Risk Assessment

Abstract

This project presents RiskRAG, a novel real-time credit risk assessment framework that integrates Retrieval-Augmented Generation (RAG) with multimodal machine learning models. The system leverages structured financial metrics, unstructured financial documents, and macroeconomic indicators to predict borrower risk profiles more accurately than conventional credit scoring methods. RAG is used to dynamically retrieve relevant regulatory, market, and borrower-specific text data, which is then processed alongside structured numerical features in a multimodal neural network. The approach achieves state-of-the-art predictive accuracy while maintaining explainability through retrieved evidence.

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1. Introduction

Credit risk assessment is the cornerstone of modern banking operations. Traditional scoring models (e.g., logistic regression, gradient boosting) rely heavily on structured numerical datasets but fail to incorporate contextual information from unstructured data sources such as annual reports, news sentiment, regulatory filings, and borrower communications.

Recent advances in Large Language Models (LLMs) and RAG pipelines enable financial institutions to enrich predictive models with domain-specific knowledge retrieved at query time. This project aims to merge structured credit risk modeling with dynamic retrieval of textual and macroeconomic evidence.

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2. Project Goal

Primary Objective:
Develop a real-time, high-accuracy credit risk prediction system that fuses structured and unstructured data using RAG and multimodal deep learning.

Key Goals: - Enhance prediction accuracy by supplementing structured features with real-time textual evidence. - Ensure explainability via retrieved sources for each prediction. - Support dynamic updates from evolving market and borrower conditions.

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3. Methodology

3.1 Dataset Sources

• Structured Credit Data
  • Home Credit Default Risk Dataset (Kaggle) – loan applications, repayment history, demographics.
  • Fannie Mae Single-Family Loan Performance Data – U.S. mortgage loan data.
• Unstructured Financial Documents
  • EDGAR SEC Filings (10-K, 10-Q) – borrower and company disclosures.
  • Financial News Articles – e.g., Bloomberg, Reuters datasets.
• Macroeconomic Indicators
  • World Bank Data
  • FRED Economic Data

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3.2 Architecture Overview

        ┌─────────────────────┐
        │   Structured Data   │
        │  (Loan, Financials) │
        └─────────┬───────────┘
                  │
        ┌─────────▼───────────┐
        │  Numerical Encoder  │  (MLP/TabTransformer)
        └─────────┬───────────┘
                  │
┌─────────────────▼─────────────────┐
│     Multimodal Fusion Layer       │
└─────────────────┬─────────────────┘
                  │
        ┌─────────▼───────────┐
        │     Text Encoder    │  (FinBERT / LLaMA-3)
        └─────────┬───────────┘
                  │
┌─────────────────▼─────────────────┐
│ Retrieval-Augmented Generation    │
│ (LangChain + FAISS / Vespa DB)    │
└─────────────────┬─────────────────┘
                  │
            ┌─────▼───────┐
            │  Classifier │
            └─────┬───────┘
                  │
          Risk Score / Class

3.3 Technical Stack

• Retrieval Layer:
  • Vector Store: FAISS / Weaviate
    
  • Retriever: LangChain retriever with hybrid search (BM25 + dense embeddings)
    
  • Embeddings: sentence-transformers/all-MiniLM-L6-v2 for general text, FinBERT for finance domain.
• Text Encoder:
  • FinBERT for sentiment extraction.
    
  • LLaMA-3 (quantized) for contextual embeddings.
• Numerical Encoder:
  • TabTransformer or MLP with BatchNorm and dropout.
• Fusion Layer:
  • Concatenation + gated attention to weight modalities.
• Classifier:
  • XGBoost for explainability or a final fully connected neural network.

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3.4 Workflow

1. Data Preprocessing
   • Structured: Missing value imputation, feature engineering, normalization.
     
   • Unstructured: Text cleaning, stopword removal, entity extraction.
2. Vector Indexing
   • Store borrower/company filings, market news, and regulatory updates in FAISS index.
     
3. Query-Time Retrieval
   • Retrieve most relevant documents for the loan/customer being scored.
     
4. Embedding + Fusion
   • Encode retrieved documents and structured loan data.
     
5. Risk Prediction
   • Classify into risk tiers (Low, Medium, High) or predict probability of default.
     
6. Explainability
   • Output top retrieved documents with relevance scores.

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4. Results (Planned Evaluation)

• Evaluation Metrics: ROC-AUC, Precision-Recall AUC, Brier Score, SHAP feature attribution.
• Baseline Models: Logistic Regression, LightGBM without textual features.
• Expected Improvement: 5–12% AUC gain over baseline by integrating RAG-based textual context.

5. Conclusion

This work proposes RiskRAG, a framework that combines structured financial modeling and dynamic retrieval of unstructured financial knowledge for real-time credit risk assessment. The architecture is extensible to other domains such as fraud detection, regulatory compliance monitoring, and supply chain risk management.

By unifying RAG with multimodal fusion, RiskRAG enables more accurate, context-aware, and explainable credit risk predictions, setting the stage for deployment in high-stakes financial decision-making.

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6. References

1. Devlin, J., et al. “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.” NAACL-HLT (2019).
2. Araci, D. “FinBERT: Financial Sentiment Analysis with Pre-trained Language Models.” arXiv preprint (2019).
3. Huang, Z., et al. “TabTransformer: Tabular Data Modeling Using Contextual Embeddings.” arXiv preprint (2020).
4. Lewis, P., et al. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” NeurIPS (2020).