Bank Statement Metadata Extraction: Extract Bank Name, Account Number & Dates

The Metadata Extraction Challenge

Every bank statement PDF contains valuable metadata beyond the transaction data: bank name for categorization, account number for identification, statement date for organization, balances for reconciliation. Yet this metadata is locked in unstructured PDF text, requiring manual copying or sophisticated extraction.

For individuals converting 1-2 statements monthly, manual copying is tolerable. But for accounting firms processing 50+ client statements, bookkeepers managing 10+ business accounts, or financial analysts aggregating data from multiple sources, manual metadata extraction becomes a bottleneck consuming hours weekly.

This guide covers the complete metadata extraction workflow: which fields to extract, three extraction methods (regex, OCR zones, AI), accuracy expectations, regex patterns for common fields, and strategies for generating intelligent filenames for organized statement libraries.

15 Metadata Fields to Extract

Not all metadata is equally valuable. Here's a prioritized list of 15 fields with their use cases and extraction difficulty:

3 Extraction Methods: Regex vs OCR Zones vs AI

Three approaches exist for metadata extraction, each with different accuracy, speed, and complexity tradeoffs:

Which Method Should You Use?

Regex Patterns for Common Metadata Fields

If you're building your own extraction system or need to understand how regex-based extractors work, here are proven patterns for 8 common metadata fields:

Improving Regex Accuracy

Generating Intelligent Filenames from Metadata

Once you've extracted metadata, the killer application is automatic filename generation. Transform "statement_downloaded_20250104_v2_final.pdf" into "Chase_1234_2024-12.csv" for instant organization.

Filename Template Best Practices

Filename Sanitization Rules

Code Example: Filename Generation

function generateFilename(metadata) {
  // Sanitize bank name
  const bankShort = {
    "JPMorgan Chase": "Chase",
    "Bank of America": "BofA",
    "Wells Fargo": "WellsFargo",
    "Citibank": "Citi"
  }[metadata.bankName] || metadata.bankName.replace(/[^a-zA-Z0-9]/g, "");

  // Extract last 4 digits of account number
  const accountLast4 = metadata.accountNumber.slice(-4);

  // Format date as YYYY-MM
  const date = new Date(metadata.statementDate);
  const yearMonth = `${date.getFullYear()}-${String(date.getMonth() + 1).padStart(2, '0')}`;

  // Combine into filename
  let filename = `${bankShort}_${accountLast4}_${yearMonth}.csv`;

  // Handle duplicates
  let counter = 1;
  while (fs.existsSync(filename)) {
    filename = `${bankShort}_${accountLast4}_${yearMonth}_v${++counter}.csv`;
  }

  return filename;
}

// Example usage:
const metadata = {
  bankName: "JPMorgan Chase",
  accountNumber: "1234-5678-9012",
  statementDate: "2024-12-31"
};

console.log(generateFilename(metadata));
// Output: Chase_9012_2024-12.csv

Batch Metadata Extraction Workflow

For accounting firms, bookkeepers, and financial analysts processing dozens or hundreds of statements monthly, batch metadata extraction is essential:

Metadata Spreadsheet Example

The spreadsheet shows successful extractions (✓ OK) and partial failures (⚠ Review - account number missing). Review flagged records and manually fill gaps before renaming files.

Frequently Asked Questions