Financial Report Intelligence System

A 10-agent AI platform that turns unstructured financial reports into structured metrics, risk flags, and executive commentary

Overview

Rather than functioning as a chatbot, FRIS transforms unstructured financial documents — income statements, balance sheets, cash flow statements — into structured metrics, insights, and business intelligence through a specialised multi-agent architecture: document processing, metrics extraction, financial calculations, data quality, financial analysis, insight generation, risk assessment, visual blueprints, narrative synthesis, and export.

The guiding principle: every insight should be traceable to source evidence. Python performs the calculations; AI provides the interpretation — evidence-linked risk assessment and anomaly detection, trend and variance analysis across reporting periods, and AI-generated management commentary, with model-agnostic support for local models (Ollama, Phi-3 Mini, Llama 3.1) or hosted providers (OpenAI, Anthropic, Google Gemini).

Key Results

10
Specialised agents, one per task in the pipeline
21+
Financial metrics & ratios calculated automatically
37 tests
Passing, validated against Apple, Amazon & Colgate annual reports
~1s–48s
Processing time — text-native reports to OCR-required documents

Tech Stack & Skills Demonstrated

Multi-Agent AI Architecture

  • Model-agnostic: Ollama/Phi-3/Llama 3.1, OpenAI, Anthropic, Gemini
  • 10 specialised agents, evidence-linked outputs

Financial Statement Analysis

  • Multi-period extraction (income, balance sheet, cash flow)
  • Ratio calculation, trend & variance analysis

Data Engineering

  • PyMuPDF / OpenPyXL document parsing
  • SQLite, PostgreSQL, ChromaDB storage

Reporting

  • AI-generated executive commentary
  • Power BI dashboard specification generation

Explore the Code

Full architecture, agent design, and test suite are on GitHub.

GitHub ↗ Visual Write-up on Notion ↗ ← Back to Portfolio