Fin Report Analyzer

Investment analysts often spend over 30 minutes per report extracting financial metrics from quarterly and annual reports. These documents vary widely in structure, especially between private and public companies. Manual extraction not only slows down the analysis process but also increases operational costs and error rates.

To streamline this, we developed Fin Report Analyzer—an AI-powered engine that automates extraction of critical financial data with high precision and speed.

We started by training classifiers to differentiate between public and private company reports, allowing the engine to tailor its extraction logic accordingly. To improve accuracy, we built a context-aware narrowing system that identifies relevant sections (e.g., financial highlights, ESG, or operational metrics) using structural and linguistic cues.

The core of the engine is a fine-tuned LLM-based extractor trained to identify and pull over 30 key data points such as:

• Revenue, Profit, and EBITDA

• ESG disclosures

• Employee count

• CAPEX, OPEX

• Segment-wise performance

All extracted data is returned in a clean, structured format (e.g., CSV/JSON/table) ready for downstream analysis, reporting, or integration with BI tools.

Fin Report Analyzer can:

• Extract 30+ financial KPIs with 95%+ accuracy

• Adapt to varied report formats and layouts

• Output a single structured dataset ready for use within minutes

By turning hours of manual reading into a few clicks, Fin Report Analyzer drastically boosts productivity for financial analysts, research firms, and investment teams.