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In our hybrid architecture, the classical XGBoost model produces a deterministic probability of default from tabular features. The LLM layer then analyzes business plans, owner letters, and other unstructured documents to identify qualitative risk signals that classical models cannot see. Through RAG, the system retrieves relevant underwriting policies to ensure the LLM's adjustments align with institutional guidelines. The synthesis agent combines these outputs into a final decision with full explainability.
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