Identifying Financial Risk Information Using RAG with a Contrastive Insight

2025/10/03 by A Abd Elahi, Elahi, Ali
Decision Sciences · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Stock Market Forecasting Methods

paper · pdf · doi:10.48550/arxiv.2510.03521

openalex publication_date 2025/10/03 · openalex created_date 2025/10/09 · openalex updated_date 2026/07/28

Abstract

In specialized domains, humans often compare new problems against similar examples, highlight nuances, and draw conclusions instead of analyzing information in isolation. When applying reasoning in specialized contexts with LLMs on top of a RAG, the pipeline can capture contextually relevant information, but it is not designed to retrieve comparable cases or related problems. While RAG is effective at extracting factual information, its outputs in specialized reasoning tasks often remain generic, reflecting broad facts rather than context-specific insights. In finance, it results in generic risks that are true for the majority of companies. To address this limitation, we propose a peer-aware comparative inference layer on top of RAG. Our contrastive approach outperforms baseline RAG in text generation metrics such as ROUGE and BERTScore in comparison with human-generated equity research and risk.

Citations

• AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions
• Modeling and Detecting Company Risks from News: A Case Study in Bloomberg News
• RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning
• Combining Financial Data and News Articles for Stock Price Movement Prediction Using Large Language Models
• Eliciting Critical Reasoning in Retrieval-Augmented Language Models via Contrastive Explanations
• RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs
• RAFT: Adapting Language Model to Domain Specific RAG
• A Framework for Institutional Risk Identification using Knowledge Graphs and Automated News Profiling
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• BERTScore: Evaluating Text Generation with BERT

Related

• RiskLabs: Predicting Financial Risk Using Large Language Model based on Multimodal and Multi-Sources Data
• Cross-Asset Risk Management: Integrating LLMs for Real-Time Monitoring of Equity, Fixed Income, and Currency Markets
• FinCARE: Financial Causal Analysis with Reasoning and Evidence
• Generative AI Enhanced Financial Risk Management Information Retrieval
• Financial Risk Relation Identification through Dual-view Adaptation