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.
Investment analysts, financial data providers, and regulatory teams processing thousands of 10-Ks, 10-Qs, and earnings reports annually
Manual extraction of financial tables takes hours per document, copy-paste errors corrupt data integrity, and complex multi-level headers break standard extraction tools
Ready to implement?
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
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 documentconst 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}});
Extract Financial Tables
Get structured table data with cell relationships
// Access extracted tablesconst tables = result.tables;tables.forEach(table => {console.log(`Table: ${table.title}`);console.log(`Location: Page ${table.page}`);// Access structured datatable.rows.forEach(row => {console.log(`${row.label}: ${row.values.join(', ')}`);});// Access XBRL mappingstable.xbrl_mappings.forEach(mapping => {console.log(`${mapping.concept}: ${mapping.value}`);});});
Perform Financial Calculations
Use extracted data for automated calculations
// Calculate financial metricsasync 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};}
Export to Analysis Tools
Send structured data to your analysis platform
// Export to Excel/CSVconst exported = await client.export({document_id: documentId,format: 'xlsx',include_xbrl: true,include_formulas: true});// Or push to databaseawait 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
Related Resources
Related Comparisons
More Finance Use Cases
Earnings Call Intelligence for Investment Research
For investment firms analyzing 1000+ earnings calls quarterly. Extract sentiment, key metrics, and management signals. 85% faster research coverage.
Visual Document Fraud Detection for Financial Services
For financial institutions processing 10K+ document submissions daily. Detect fraudulent documents in real-time. 98% detection rate, 0.1% false positive rate.
Automated KYC Document Verification
For financial institutions onboarding 1000+ customers daily. Automate identity verification. 99% accuracy, 80% faster onboarding.
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.
