Beyond Semantic Similarity: A Two-Phase Non-Parametric Retrieval Workflow for Corporate Credit Underwriting
arXiv:2605.20684v1 Announce Type: new Abstract: Corporate credit underwriting requires analysts to extract actionable evidence from long, heterogeneous financial documents spanning hundreds of pages and multiple languages. Standard Retrieval-Augmented Generation (RAG) pipelines optimize for semantic similarity, which frequently surfaces passages that are topically related but lack decision utility, a problem we term the similarity-utility gap. We propose a two-phase non-parametric retrieval architecture that separates high-recall candidate retrieval from high-precision utility ranking. The fir
The proliferation of Large Language Models (LLMs) has highlighted the limitations of semantic similarity in complex information retrieval tasks, necessitating more sophisticated approaches for real-world business applications.
This development addresses a critical weakness in current AI-driven information retrieval for high-stakes domains like financial analysis, potentially enabling more accurate and actionable insights from unstructured data.
The focus shifts from purely semantic matching to a two-phase retrieval process emphasizing both recall and precision, mitigating the 'similarity-utility gap' in RAG pipelines for specialized tasks.
- · Financial Institutions (AI-enabled)
- · AI/ML Research & Development
- · Credit Underwriting Analysts
- · Enterprise AI Solution Providers
- · Vanilla RAG Implementations
- · Companies reliant on basic semantic search for critical decisions
- · Inefficient manual data extraction processes
Improved accuracy and efficiency in corporate credit risk assessment using AI.
Faster and more reliable loan decisions, potentially increasing the volume of credit extended and reducing risk for lenders.
Enhanced financial stability through better risk management, and the possibility of new credit products tailored by AI-driven insights.
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Read at arXiv cs.CL