80% Faster Financial Statement Processing for a Fortune 500 Company

THE PROBLEM

Manual Data Extraction From 10-K and S-1 Filings Was Too Slow to Scale

Manual data extraction from financial reports was a slow, error-prone, and costly process for this global financial intelligence company. Their credit analysts spent countless hours reviewing SEC filings like 10-Ks, 10-Qs, and S-1s. These filings have data locked inside dense, unstructured layouts.

The complexity of these documents made automation nearly impossible without Agentic AI. OCR tools couldn’t parse graphs, charts, figures, or tables reliably. Human review slowed turnaround times, introduced risk, and made it challenging to meet the demands of a fast-moving market.

Strict regulatory standards added another layer of complexity. Every data point had to be precise and traceable. Off-the-shelf AI tools lacked the precision and adaptability needed to align with internal guidelines and compliance requirements, making them unsuitable for this workflow.

To solve the problem, they needed more than rules-based automation. They needed an intelligent, adaptable system that could accurately and quickly process high volumes of unstructured data.

THE SOLUTION

Unstructured AI Built to Handle Complex Financial Filings

This enterprise was one of the first to surface a problem we later saw across multiple customers: the inability to extract structured insights from messy, inconsistent documents. Their backlog of filings helped confirm the need for a new kind of AI Agent—purpose-built for parsing unstructured, multi-format content at scale.

We deployed our Unstructured AI Agent to process financial filings like 10-Ks, 10-Qs, and S-1s. The system extracts charts, tables, and freeform text from PDFs, DOCX, and HTML files, and transforms them into structured, query-ready JSON outputs.

To support speed and accuracy, we implemented parallel processing—splitting long documents into overlapping page segments and reassembling them with GPT-based analysis. This preserved continuity in headers and section mapping, avoiding the data loss typical with standard extraction tools when context spans multiple pages.

From there, the extracted data is sent to internal databases and CSV pipelines, where credit analysts can easily search and act on the information. This data then powers downstream tasks like credit memo preparation, enabling faster, more consistent decisions.

The end result was a seamless pipeline: documents went in, structured insights came out.

THE RESULTS

Faster Processing, Higher Accuracy, and Scalable Reporting

Unstructured AI reduced review time and improved document analysis accuracy across this customer’s most critical financial reports:

• 100% table and figure detection for 10-K & 10-Q filings
• 95% accuracy for table data extraction (non-continuing tables)
• Average processing time for 500+ page documents: 6 minutes
• Parallel processing with GPT-powered overlap resolution ensures the hierarchical structure is retained
• Enabled analysts to spend more time on high-value work like market research and decision-making

Our AI Agent now processes 10-K, 10-Q, and S-1 filings with significantly fewer errors. The organization has automated a key part of its credit intelligence pipeline, saving time and reducing the operational burden on analysts. They now have a scalable system ready to support continued growth.

About the customer

This global financial services provider is a Fortune 500 Enterprise that delivers market intelligence and credit ratings across industries. They partnered with us in 2024 to streamline financial report analysis and internal workflows.

Our solution has already demonstrated a strong impact across early-stage use cases. We are continuously identifying more use cases to collaborate on.