Structured Extraction from Financial Documents with LLMs

Financial documents like T12s (trailing twelve-month operating statements) and rent rolls contain critical data buried in inconsistent formats. Traditional OCR and rule-based extraction systems struggle with the variance in layouts, handwritten notes, and multi-table structures.

The Challenge

Real-estate underwriting requires extracting dozens of fields from each document:

Tenant information (name, suite, square footage)
Rent details (base rent, escalations, CAM charges)
Lease terms (start date, end date, options)
Historical financials (monthly breakdowns, year-over-year comparisons)

The challenge isn't just reading text—it's understanding context, reconciling conflicts, and structuring the output for downstream analysis.

Our Approach

We use a multi-stage pipeline:

Document Preprocessing: Layout detection and table segmentation
Schema-Guided Extraction: LLMs prompted with strict output schemas
Cross-Validation: Multiple extraction passes with conflict resolution
Human-in-the-Loop: Confidence scoring to flag low-certainty fields

Results

Early testing shows 94% field-level accuracy on T12s and 97% on rent rolls, with a 10x speed improvement over manual data entry. Critically, the system flags ambiguous extractions for human review, maintaining auditability.

Next Steps

We're exploring fine-tuning on domain-specific datasets and multi-modal approaches that combine OCR, layout analysis, and language understanding in a single model.

Published January 2025