n8n-nodes-sec-filings-intelligence

n8n community node for SEC Filings Intelligence - the SEC decoder AI agents trust.

Extract structured financials, risk factors, executive compensation, and MD&A from SEC 10-K, 10-Q, 8-K, and proxy statements. Built for Colorado SB 25B-004 compliance.

Features

• 8 Task Modes - Annual reports, quarterly earnings, material events, proxy statements, and more
• Financial Extraction - Income statement, balance sheet, cash flow with YoY comparisons
• Risk Factor Analysis - Item 1A parsed with categorization and materiality scoring
• Executive Compensation - Summary comp tables, equity awards, CEO pay ratios
• MD&A Insights - Key themes, outlook, and management highlights
• Bluebook Citations - Legal-grade citations in multiple formats
• RAG-Ready Chunks - 800-1200 token segments for vector databases
• Quality Scoring - Confidence metrics for every extraction

Installation

npm install n8n-nodes-sec-filings-intelligence

Or install via n8n Community Nodes in the UI.

Configuration

1. Get your Apify API token
2. Create Apify API credentials in n8n
3. Add the SEC Filings Intelligence node to your workflow

Usage

Basic Example

Schedule → SEC Filings Intelligence → Process Data → Notify

Parameters

| Parameter | Description | |-----------|-------------| | Task Mode | Extraction focus (annual_report, quarterly_report, etc.) | | Stock Tickers | Company tickers (AAPL, MSFT, GOOGL) | | Date Range | How far back to search (30d, 90d, 1y, 3y, 5y, all) | | Max Filings | Maximum filings per company | | Output Format | standard, full, compact, or rag_only |

Additional Options

• CIK Numbers (for when ticker lookup fails)
• Filing Types (10-K, 10-Q, 8-K, DEF 14A, S-1, 20-F)
• Extract Financials
• Extract Risk Factors
• Extract MD&A
• Extract Compensation
• Compare Prior Year
• RAG Chunk Size
• Baseline Run ID (for change detection)

Output

The node returns structured SEC filing data including:

• Filing information (ticker, company, date, accession number)
• Financial metrics (revenue, net income, EPS, margins)
• Risk factors (count, categories, top risks)
• Executive compensation (named officers, totals, pay ratios)
• MD&A (summary, key themes, outlook)
• Citations (Bluebook, APA, MLA, inline)
• RAG chunks (for vector databases)
• Quality scores (0-100)

Examples

Portfolio Analysis

{
  "taskMode": "investor_research",
  "tickers": "AAPL, MSFT, GOOGL, META, NVDA",
  "dateRange": "1y",
  "maxFilings": 3
}

Risk Monitoring

{
  "taskMode": "risk_analysis",
  "tickers": "TSLA",
  "extractRiskFactors": true,
  "comparePriorYear": true,
  "dateRange": "3y"
}

Executive Compensation

{
  "taskMode": "compensation_intel",
  "tickers": "CRM, ORCL, SAP",
  "filingTypes": ["DEF 14A"],
  "extractCompensation": true
}

Resources

• Apify Actor Documentation
• n8n Community Nodes

License

MIT