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Financial data extraction software: 8 tools, and how to extract data from financial statements and annual reports

For credit analysts, underwriters and finance teams who get borrower or portfolio financials as PDFs: what to capture, why statements are hard to read, and how to automate the work.

Updated · 6 min read
A balance sheet with total assets of 22,075 selected and labeled "Total Assets" in an extraction panel

Key takeaways

  • Financial data extraction software turns financial statements and annual reports from PDFs, scans or spreadsheets into structured, line-by-line data.
  • US public company numbers are already structured in the SEC's free EDGAR APIs. Private company statements, which arrive in every layout, are where extraction is needed.
  • Capture every line item, period column and unit, not just the totals, so lines can be mapped and ratios recalculated.
  • Check the statements' own math. Assets equal liabilities plus equity, each subtotal equals the sum of its lines, and net income matches across the statements.
  • Extraction feeds spreading, which maps each borrower's lines to a standard template for ratio and covenant analysis.
On this page
  1. Financial data extraction software compared
  2. The financial statements and what to capture
  3. How to extract data from an annual report (10-K)
  4. Why financial statement extraction is hard
  5. Checks that catch extraction errors
  6. How to automate financial statement extraction
  7. Before you spread the next borrower
  8. Frequently asked questions

Financial data extraction software pulls the numbers out of a company's financial reports and puts them in a table. It reads each balance sheet, income statement, cash flow statement and annual report line by line. It keeps every line item, period and total, ready for spreading and ratio analysis. US public company numbers are already tagged for software to read, and free, through the SEC's EDGAR APIs. Private company statements arrive as PDFs and scans in every layout. Those are the statements that need extraction.

Where do your statements come from?

For a public company's 10-K
Use EDGAR's free XBRL data, the numbers the company has already tagged for the SEC. Extract only the parts that aren't tagged, such as the MD&A (management's discussion and analysis).
For a private company's statements
PDFs, QuickBooks or Xero exports and scans in every layout. Extract them.
For a small business's tax returns
Form 1120, 1120-S, 1065 or Schedule C stand in for statements. See IRS tax form extraction.

Financial data extraction software compared

Some tools read and check statements for you. Others are APIs that your engineers use to build their own tool. Docsumo is our product. We took facts about the others from each vendor's own site in September and October 2026. The links are in the sources at the end.

ToolReadsOutputPricingWatch for
Document AI platforms1 tool
DocsumoOur productLenders and credit teamsFinancial statementsTax returnsBank statementsScansEach line with its statement, period and year; API, webhooks, ExcelFree trial; quoted plansCloud only; you build the spread and make the credit decision in your own tools
Loan platforms1 tool
nCino Automated SpreadingBanks already on nCinoTax returnsAuditsCompany-prepared statements10-Ks10-QsSpreads in nCino's Spreads toolNot publishedIt's part of the nCino banking platform
Statement converters1 tool
DocuClipperBookkeepersBank statementsCredit card statementsBrokerage statementsTax formsExcel, CSV, QuickBooks, Xero, APIFrom $29 a month, or $20.30 a month billed annuallyDocuClipper's site doesn't name balance sheets or P&Ls among its documents
Document APIs2 tools
ReductoEngineering teamsPDFsScansSpreadsheetsTablesChartsJSON, with each value's source and its position on the page$20 per 1,000 pages (Extract product)You run the statement math checks in your own code, and your schema sets the mapping
LlamaParseTeams building AI apps90+ file formatsTablesChartsMarkdown (formatted text) and structured extraction$1.25 per 1,000 credits, from 1 credit a pageCredits per page rise with the parsing mode
Cloud OCR APIs3 tools
Azure Document IntelligenceEngineering teams on AzureTextTablesJSON through the REST API and SDKs$10 per 1,000 pages (Layout model)No prebuilt model for financial statements
Amazon TextractEngineering teams on AWSTextFormsTablesJSON blocks with confidence scores$15 per 1,000 pages (tables)No financial statement model; A2I is closed to new customers
Google Document AIEngineering teams on Google CloudTextLayoutTablesJSON with tables and named values$10 per 1,000 pages (Layout Parser)Google's current list of processors has no financial statement parser

With the cloud APIs, your team builds the math checks, the mapping to your template and the review step itself.

The financial statements and what to capture

StatementCoversKey lines to capture
Balance sheetPosition on one dateCash, receivables, inventory, fixed assets, total assets, payables, debt, total liabilities, equity
Income statement (P&L)Performance over a periodRevenue, cost of goods sold, gross profit, operating expenses, interest, taxes, net income
Cash flow statementCash in and out over a periodNet income, non-cash adjustments, working capital, cash from operations, investing and financing
Statement of changes in equityMovements in equityShare capital, retained earnings, dividends, reserves
NotesDetail behind the numbersDebt maturities, leases, related parties, contingencies

Capture more than the totals.

  • Every line item as labeled, so it can be mapped later.
  • Every period column, prior years included.
  • The units, because "in thousands" in the header changes every number.
  • The quality level, whether audited, reviewed, compiled or company-prepared.

How to extract data from an annual report (10-K)

A Form 10-K is the annual report a US public company files with the SEC within 60 to 90 days of its fiscal year-end. Its financial statements are tagged in XBRL, so take the numbers from the EDGAR APIs rather than the document. When a 10-K arrives as a PDF in a credit package, split it by Item. Then extract each statement with its periods and units intact. MD&A commentary isn't tagged, so read it from the document either way.

10-K itemWhat it holdsWhy an analyst reads it
Item 1: BusinessSegments and customersContext for the numbers
Item 1A: Risk factorsMaterial risks the company disclosesCredit and concentration risk
Item 7: MD&AManagement's view of results and liquidityWhy revenue, margins or cash moved
Item 7A: Market riskRate, currency and commodity exposureHow debt costs would move
Item 8: Financial statementsStatements, notes and auditor's reportThe figures, plus debt maturities and covenants in the notes

Why financial statement extraction is hard

  • No standard layout

    Every accountant's format differs, and so do line names, such as "Sales", "Revenue" and "Net revenues".

  • Several periods side by side

    Prior years, budget and variance columns share one table.

  • Hierarchy

    Sub-lines, subtotals and totals have to keep their structure.

  • Negative numbers

    Parentheses, minus signs or red ink.

  • Multi-page statements

    Headers repeat on some pages and vanish on others.

  • Scans and faxes

    Common from smaller borrowers.

  • Mixed packages

    A cover letter, three statements, the notes and a tax return in one PDF.

Checks that catch extraction errors

The numbers in a financial statement must agree with each other, so you can use them to check the extraction. A failed check means a misread value or a missing line, so the statement goes to a reviewer.

  • The balance sheet balancesTotal assets equal total liabilities plus equity.
  • Subtotals footEach subtotal equals the sum of its lines.
  • The income statement flowsRevenue minus cost of goods sold equals gross profit, and so on down to net income.
  • The statements tieNet income matches the cash flow statement, and ending cash matches the balance sheet.
  • Periods line upThe balance sheet date is the end of the P&L period.
  • Units are consistentThousands and dollars aren't mixed.

How to automate financial statement extraction

Automated financial statement processing runs one pipeline on every package, from a single P&L to a 10-K PDF.

  • Financial statements
  • 10-K PDFs
  • Tax returns
Extraction
  1. 01Classify and split
  2. 02Extract lines and periods
  3. 03Validate the math
  4. 04Map to your template
Spreading template or credit system
From a borrower's PDF to a spread

Reuse analysts' past mappings, so "Cost of sales" lands in the same row for every borrower. Analysts see only failed checks and low-confidence values, each on its source page. The data reaches your spreading tool or credit system by API or webhook. Borrower financials are confidential, so ask for SOC 2 Type 2 compliance.

Docsumo does the reading for financial spreading. Every line comes out with its statement, period and year, and fields it's unsure about go to your reviewer. The spread itself and the credit decision stay in your tools and with your analysts.

  • 99%field-level accuracy on 250+ document types
  • <5 minper document, down from 2+ hours
  • 95%+of documents processed straight through, without manual review

Before you spread the next borrower

What we'd do. Test each tool on your messiest borrower packages first, such as a faxed P&L or a scanned tax return. Neat sample statements tell you little about how a tool will do.

Pull public company data from EDGAR, and extract private company statements. Capture every line, period and unit, check them against the statements' own math, and map them to your template. Then analysts review exceptions instead of retyping PDFs. For property loans, the documents that play this role are the T12 (trailing 12 months) and rent roll.

Book a demo with a few of your own borrower statements, or start a free trial.

Frequently asked questions

What is the best financial data extraction software?

It depends on where the data goes. Lenders that spread borrower statements want a tool that reads every line and period and sends doubtful values to an analyst, as Docsumo does. Banks already on nCino can use its Automated Spreading. Engineering teams can build on Reducto, LlamaParse or a cloud API, and add the checks themselves.

How do you extract data from financial statements?

A document AI tool classifies each statement, reads every line item and period column, and returns structured data. You then check the math, map the lines to your spreading template and export to Excel or your credit system.

How do you extract data from an annual report?

For a US public company, skip the PDF. The statements in its 10-K are tagged in XBRL and free as JSON through the SEC's EDGAR APIs. If you only have the PDF, split it into its numbered sections, called Items. Extract the Item 8 financial statements with their periods and units. Then read the notes and the MD&A (management's discussion and analysis) for everything else.

Can I extract financial statement data to Excel?

Yes. Most tools export to Excel or CSV, one row per line item and one column per period. For ongoing volume, send the data to your spreading or loan system by API.

What is the difference between extraction and financial spreading?

Extraction reads the numbers off the statement as they're labeled. Spreading maps those lines into a standard template. For example, "Cost of sales" and "COGS" go in the same row, so you can calculate ratios and compare borrowers. See spreading financial statements.

How accurate is automated financial statement extraction?

It depends on the tool and the documents, so measure it on your own borrower statements, scans included. Docsumo reports 99% field-level accuracy on 250+ document types.

Sources

  1. SEC: EDGAR application programming interfaces
  2. SEC: Form 10-K general instructions and items
  3. Docsumo: pricing
  4. nCino: Automated Spreading
  5. nCino: platform
  6. DocuClipper: home page (documents it reads)
  7. DocuClipper: pricing
  8. Reducto: home page
  9. Reducto: pricing
  10. Reducto: financial statement data extraction guide (checks and schema mapping)
  11. LlamaIndex: LlamaParse
  12. LlamaIndex: LlamaParse pricing
  13. Microsoft: Document Intelligence overview (prebuilt models)
  14. Microsoft: Document Intelligence pricing
  15. AWS: Amazon Textract pricing
  16. AWS: Amazon Textract features
  17. AWS: Amazon A2I human review loops (no longer open to new customers)
  18. Google Cloud: Document AI processor list
  19. Google Cloud: Document AI pricing

First published . Last updated .

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