Agentic Retrieval-Augmented Generation for Financial Document Question Answering

Researchers introduce FinAgent-RAG, an advanced AI framework designed to answer complex financial questions by combining iterative retrieval, reasoning, and self-verification. The system achieves 76-78% accuracy on financial benchmarks while reducing computational costs by 41%, demonstrating practical viability for institutional financial analysis.

FinAgent-RAG represents a meaningful advancement in applying large language models to financial document analysis, a domain where accuracy and computational efficiency directly impact institutional operations. The framework addresses a genuine limitation of conventional RAG systems—their inability to handle the multi-step reasoning required when analyzing corporate filings that interweave numerical data, narratives, and footnotes. By incorporating program-of-thought reasoning that generates executable Python code rather than relying on LLM mental arithmetic, the system mitigates hallucination risks that plague pure language-model approaches to financial computation.

The innovation builds on growing recognition that generic AI tools require domain-specific tuning for high-stakes applications. The contrastive retriever trained with hard negative mining directly tackles the problem of semantically similar passages containing different numerical values—a critical distinction in financial contexts where small differences compound across analysis. The adaptive strategy router demonstrates practical engineering maturity by dynamically allocating computational resources based on question complexity, achieving the 41.3% cost reduction while maintaining accuracy benchmarks.

For financial institutions and fintech platforms, this work signals that production-grade financial AI systems are becoming feasible. The multi-benchmark validation across FinQA, ConvFinQA, and TAT-QA datasets provides credible evidence of robustness. However, the 76-78% execution accuracy, while superior to baselines, still implies meaningful error rates in real deployment scenarios. Institutions would require additional safeguards and human validation for high-stakes decisions. The framework's open emphasis on cost reduction and cross-LLM compatibility suggests potential integration into existing financial workflows within coming years.

• →FinAgent-RAG achieves 76-78% accuracy on financial question-answering tasks, outperforming baselines by 5-9 percentage points
• →Program-of-thought code generation reduces arithmetic errors compared to LLM mental computation in financial calculations
• →Adaptive strategy routing cuts API costs by 41.3% while preserving accuracy, improving practical deployment economics
• →Contrastive retriever trained on hard negatives specifically addresses distinguishing numerically distinct but semantically similar financial passages
• →Framework demonstrates robustness across multiple LLM backbones and financial document types, supporting institutional adoption potential