From Complex Docs to Decision-Ready Context for Financial Services Agents

Agents are only as good as the context they're given, and in finance that context is locked inside the messiest documents you own. Credit memos, audited financials, and credit agreements are where the real risk hides: revenue-breakdown tables, charts and figures embedded in the statements, and numbers that only resolve when you follow references across documents. Most document tooling flattens exactly the structure that matters, so what reaches your systems and your agents is already degraded.

In this session we run three financial workflows through parsing and extraction: private equity investment analysis, corporate lending, and mortgage. You'll see a mixed set of documents come back as clean structure, with the terms that drive the decision pulled into a defined schema, each with a page-level citation you can click back to the source table. Anything the system isn't confident about lands in a review queue before it goes downstream. Same pipeline all three times, ending in a consolidated financial model, a credit decision, and a mortgage claim decision.

What you'll see

• Dense financial tables and footnotes parsed without losing structure
• Schema-driven extraction with page-level citations and per-field confidence
• Extracted values checked against policy, with exceptions flagged and sourced
• Works across different document formats with no per-template setup