Extract Accurate Data Fast with Brokerage Statement OCR

It’s layout-aware, so it preserves reading order across columns, headers/footers, and section breaks. That means holdings, transactions, and fees tables come through cleanly—without the scrambled lines and shifted cells common in traditional OCR.

Yes—outputs can be returned as statement-to-JSON with key fields like account number, statement period, balances, positions, and transaction rows. This makes it easy to map data into your portfolio, reporting, or reconciliation pipeline with minimal post-processing.

Each extracted element can include traceable metadata such as page references and bounding boxes. This gives you a clear audit trail and speeds up human review because reviewers can jump directly to the exact source location on the statement.

Auto validation and correction loops help catch common issues like broken rows, missing totals, and inconsistent currency formatting. The result is higher straight-through processing rates and less manual cleanup before downstream calculations.

It’s designed to generalize across varying statement layouts by using structure-aware parsing rather than rigid, broker-specific templates. You can onboard new statement formats faster and avoid constant maintenance when brokers update their designs.

You get structured outputs plus validation to reduce downstream errors, and trace metadata to support quick, confident approvals on exceptions. That combination typically cuts rework time and helps teams move from manual review to scalable, repeatable automation.