FinTech & Surety · Document Intelligence
Document intelligence for financial services.
The financial services industry runs on documents — applications, statements, tax returns, balance sheets, certificates, endorsements. All unstructured, all critical. We build AI that extracts and interprets them, not just copies numbers.
Why it matters
AI must extract and interpret — not just transcribe.
Financial packages contain PDFs, scanned images, Excel exports, bank statements, and handwritten notes — and AI has to handle all of them. The surety and insurance industries need systems that extract structured financial data and interpret it, computing ratios rather than simply copying numbers off a page.
Our pipeline classifies each document, extracts its target fields with a confidence score, and normalizes currency, dates, and entities so the same company resolves correctly across multiple document types. Below-threshold fields are automatically flagged for human review.
Extraction pipeline
From mixed document packages to normalized, structured data.
Documents arrive in any format and move through a consistent ingestion, extraction, and normalization flow.
Ingestion: Multi-format support across PDF, DOCX, XLSX, JPEG, PNG, and scanned TIFF, with an OCR layer combining Azure Form Recognizer, AWS Textract, and a PaddleOCR fallback for difficult scans.
Classification: Each document is classified by type — balance sheet, income statement, bank statement, work-on-hand schedule, references — so the right extraction targets are applied.
Extraction with confidence: Fields are extracted with a confidence score per field. Every field carries that score, and anything below threshold is automatically routed to a human reviewer.
Normalization: Currency normalization (thousands/millions/actuals), date normalization and period identification, and entity resolution so the same company is recognized across its different documents.
Output: Structured, normalized data ready for financial spreading and downstream risk scoring, with missing fields explicitly detected and flagged.
Document types
What we process — and what we pull from each.
A representative set of the financial document types our deployments handle, with the primary fields extracted from each.
Financial spreading
Extraction feeds automated financial spreading.
Once fields are extracted and normalized, the data feeds an automated spreading layer that prepares it for underwriting and risk analysis.
Balance-sheet normalization
Extracted assets, liabilities, and equity are normalized into a consistent structure so multi-period comparisons hold up across statement formats.
Working capital & current ratio
Working capital and the current ratio are computed directly from spread statements — the inputs surety underwriting depends on, including the 10x working-capital bonding-capacity rule.
Revenue trend analysis
Multi-period revenue trend and growth-rate analysis surfaces the trajectory behind the numbers, not just a single point-in-time snapshot.
Confidence-gated review
Every spread value traces back to a confidence-scored field. Low-confidence extractions are flagged so a human reviews the source before the number is trusted.
94%+
Field-level extraction accuracy on clean PDFs
Production deployments achieve over 94% field-level accuracy on clean PDFs and over 87% on scanned documents. Every field includes a confidence score, and below-threshold fields are automatically flagged for human review.