Document AI for financial services.
Automate underwriting, credit agreement, and research extraction — with every figure traced back to its source.
Deal documents don’t come in one format.
Underwriting means manually spreading financials
Teams manually spread financial statements and multi-tab spreadsheet models to extract ratios and cash flow data.
Credit terms are extracted by hand
Credit agreements, term sheets, and loan documents require manual extraction of covenants, rates, and collateral terms before they can feed decisioning systems.
Research data hides in tables and footnotes
Analyst teams spend hours scanning filings, earnings reports, and research decks for data buried in tables, charts, and footnotes.
Built for lending, markets, and advisory documents.
Structured covenant extraction
Covenants, rates, and collateral terms are extracted from credit agreements and loan documents, structured for direct use in decisioning.
Faster underwriting
Automated financial statement and multi-tab spreadsheet extraction speeds up underwriting.
Traceable research extraction
Chart-, table-, and footnote-level extraction from filings and reports, with every figure traced back to its exact source.
Automated holdings ingestion
Holdings and account data from brokerage/policy statements flow directly into risk and advisory systems.
Audit-ready output
Every extracted figure is linked to its source document, page, and location.
Regional pain points, handled.
European Union
GDPR constrains automated decision-making built on extracted data in credit workflows. DORA requires operational resilience assurances from any AI vendor in the pipeline.
United Arab Emirates(UAE)
In-country processing requirements under CBUAE apply to lending and investment documentation. Islamic finance contract structures need handling most generic tools aren't built for.
India
NBFC and MSME lending volume means financial statements and GST documents arrive in highly inconsistent formats, straining manual underwriting at scale.
Singapore
Cross-border wealth and asset management documents span multiple jurisdictions and currencies, while MAS oversight raises the governance bar.
Common questions
What does Proof Perimeter do for financial services document processing?+
It automates extraction from the documents lending, markets, and advisory teams handle — credit agreements, financial statements, research filings, and brokerage/policy statements — with every figure traced back to its source document, page, and location.
Can Document AI extract covenants and collateral terms from credit agreements?+
Yes. Covenants, rates, and collateral terms are extracted from credit agreements, term sheets, and loan documents and structured for direct use in decisioning systems, instead of manual extraction.
How much more accurate is Document AI than a general-purpose LLM for financial documents?+
Per internal benchmarks, Proof Perimeter's fine-tuned Document AI delivers 20% higher accuracy and 50% lower token consumption than general frontier models on document-extraction tasks like spreading financials and extracting research data.
Can financial services documents be processed with zero data egress?+
Yes. Enterprise deployments support zero-egress options — cloud-hosted, within your infrastructure, or fully on-premises — so credit, investment, and advisory documents never leave your chosen boundary.
Is Bring Your Own Key free for financial services teams?+
Yes. The full workspace — templates, workflows, batch processing, review, and export — is free with your own model key. You pay your model provider directly, with no platform fee or document limit.
How does Proof Perimeter handle regional financial services compliance?+
Deployment adapts to where you operate — GDPR and DORA controls in the EU, in-country processing under CBUAE in the UAE, and governance aligned with MAS oversight in Singapore.
How does this integrate with our existing underwriting or portfolio systems?+
Enterprise ships with system-of-record integration and custom workflow support, so extracted covenants, holdings, and research data land directly in the risk, advisory, or decisioning systems your team already runs.