Financial Audit RAG System with Chain-of-Thought Prompting
A specialized Retrieval-Augmented Generation (RAG) system engineered to automate the analysis of multi-year corporate financial and annual reports across 15 enterprise sectors using open-source LLMs.
Business Challenge
Financial auditing requires manually reviewing, verifying, and extracting complex numerical reasoning from hundreds of pages of annual reports across multiple years and entities. This process is highly time-consuming, prone to human oversight, and vulnerable to information fragmentation.
Solution Delivered
Built an end-to-end local RAG pipeline using Python and a quantized Qwen-3 8B model architecture. Implemented a specialized "Audit-Chain-of-Thought" (Audit-CoT) prompting framework combined with Vector Search to rigorously analyze extensive document batches, significantly reducing model hallucinations and ensuring auditable, step-by-step reasoning for financial data extraction.
Outcomes Achieved
Implemented a structured prompting approach to optimize financial data extraction from multi-page corporate documents. The deployment successfully improved response consistency and verified data reliability across a comprehensive multi-year dataset spanning 15 distinct sectors, effectively lowering text fragmentation and mitigating common model hallucination tendencies.
Measurable Business Outcome
Significantly improved response reliability and accuracy in extracting multi-year financial trends compared to the baseline Qwen-3 8B model. Reduced document processing bottlenecks and information fragmentation across 4 years of comprehensive corporate reports.