Mixpeek Logo
    Advanced
    Finance
    10 min read

    Automated Financial Table Extraction from 10-Ks and Earnings Reports

    For financial analysts processing thousands of 10-Ks and earnings reports. 94.2% accuracy on complex financial tables with automatic XBRL mapping.

    Who It's For

    Investment analysts, financial data providers, and regulatory teams processing thousands of 10-Ks, 10-Qs, and earnings reports annually

    Problem Solved

    Manual extraction of financial tables takes hours per document, copy-paste errors corrupt data integrity, and complex multi-level headers break standard extraction tools

    Why Mixpeek

    94.2% cell-level accuracy on complex financial tables, automatic XBRL tag mapping, and 80-90% time savings on document review compared to manual extraction

    Overview

    Financial analysts spend significant time extracting data from 10-Ks, quarterly reports, and earnings presentations. Complex tables with multi-level headers, merged cells, and footnotes break standard extraction tools. This use case shows how Mixpeek enables accurate, automated extraction of financial data.

    Challenges This Solves

    Complex Table Structures

    10-K financial statements have multi-level headers, merged cells, and footnotes

    Impact: Standard PDF extraction tools achieve 50-60% accuracy, requiring manual correction

    Manual Extraction Time

    Analysts spend 2-4 hours per document extracting and validating data

    Impact: Analysis delayed, opportunity costs from slow data availability

    Data Integrity Errors

    Copy-paste errors corrupt financial data, leading to incorrect analysis

    Impact: Investment decisions based on incorrect data, compliance violations

    XBRL Mapping Complexity

    Mapping extracted values to XBRL taxonomy requires expertise

    Impact: Inconsistent data models, difficult cross-company comparisons

    Implementation Steps

    Mixpeek uses specialized models trained on financial documents to detect table structures, extract cell values with proper relationships, and map to XBRL taxonomy for structured output

    1

    Upload Financial Documents

    Process 10-K, 10-Q, or earnings documents

    import { Mixpeek } from 'mixpeek';
    const client = new Mixpeek({ apiKey: process.env.MIXPEEK_API_KEY });
    // Process financial document
    const result = await client.extract({
    url: 'https://sec.gov/archives/edgar/data/company/10-K.pdf',
    extractors: [
    'financial-table-extraction',
    'xbrl-mapping',
    'numerical-extraction'
    ],
    settings: {
    document_type: '10-K',
    extract_footnotes: true,
    preserve_formatting: true
    }
    });
    2

    Extract Financial Tables

    Get structured table data with cell relationships

    // Access extracted tables
    const tables = result.tables;
    tables.forEach(table => {
    console.log(`Table: ${table.title}`);
    console.log(`Location: Page ${table.page}`);
    // Access structured data
    table.rows.forEach(row => {
    console.log(`${row.label}: ${row.values.join(', ')}`);
    });
    // Access XBRL mappings
    table.xbrl_mappings.forEach(mapping => {
    console.log(`${mapping.concept}: ${mapping.value}`);
    });
    });
    3

    Perform Financial Calculations

    Use extracted data for automated calculations

    // Calculate financial metrics
    async function calculateMetrics(documentId: string) {
    const data = await client.query({
    document_id: documentId,
    query: "Calculate YoY revenue growth, gross margin, and operating margin for the last 3 fiscal years"
    });
    return {
    revenue_growth: data.calculations.revenue_growth,
    gross_margin: data.calculations.gross_margin,
    operating_margin: data.calculations.operating_margin,
    source_tables: data.source_references
    };
    }
    4

    Export to Analysis Tools

    Send structured data to your analysis platform

    // Export to Excel/CSV
    const exported = await client.export({
    document_id: documentId,
    format: 'xlsx',
    include_xbrl: true,
    include_formulas: true
    });
    // Or push to database
    await client.push({
    document_id: documentId,
    destination: 'snowflake',
    table: 'financial_statements',
    mapping: {
    ticker: 'symbol',
    fiscal_year: 'year',
    revenue: 'us-gaap:Revenue'
    }
    });

    Feature Extractors Used

    Retriever Stages Used

    Expected Outcomes

    94.2% cell-level accuracy vs 55% with generic PDF extraction

    Table Extraction Accuracy

    80-90% reduction in document review time

    Processing Time

    99%+ accuracy on numerical values (validated against XBRL filings)

    Data Integrity

    Automatic mapping to 85%+ of common XBRL concepts

    XBRL Mapping

    Process 10x more documents per analyst

    Analyst Productivity

    Frequently Asked Questions

    Ready to Implement This Use Case?

    Our team can help you get started with Automated Financial Table Extraction from 10-Ks and Earnings Reports in your organization.