GenAI Financial Reporter: Enhancing Financial Reporting for Accuracy and Efficiency Using Generative AI

This paper presents GenAI Financial Reporter, a multimodal artificial intelligence system designed to automate the generationof comprehensive financial analysis reports. The system leverages large language models (GPT-4o), retrieval-augmentedgeneration (RAG) with ChromaDB vector database, and multi-agent architectures to transform raw financial data intoprofessional reports enriched with text summaries, interactive visualizations, and audio narration. By integrating real-timemarket data from Yahoo Finance and SEC EDGAR filings, the system computes 27 key performance indicators (KPIs) fromstructured financial data stored in PostgreSQL and generates contextually-grounded analysis using RAG over SEC filing text.Our evaluation demonstrates vector similarity search completing in 1.3ms and full RAG queries averaging 15 seconds. Anablation study shows that RAG-enabled queries cite an average of 4 SEC filing sources per response compared to zero forbaseline approaches, improving answer provenance. The system supports multi-company comparisons, historical trendanalysis, and exports to multiple formats including PDF, DOCX, HTML, and MP3 audio. Deployed on AWS EC2 with Dockercontainerization, the system achieves production-ready reliability. We note that this is a systems paper emphasizing practical deployment;rigorous evaluation against financial benchmarks remains future work.