Learn how to automatically detect and redact bank account numbers from text and documents. Protect financial identifiers including checking accounts, savings accounts, IBANs, and international banking numbers with high accuracy.
Bank account numbers are highly sensitive financial identifiers that provide direct access to individual or business bank accounts. Exposure of account numbers can lead to fraudulent transfers, unauthorized debits, identity theft, and significant financial losses.
Anonymization API uses pattern recognition combined with checksum validation to detect bank account numbers with 99.5% accuracy. Context analysis helps distinguish account numbers from similar numeric sequences.
Our system recognizes US checking and savings accounts, international IBANs (covering 80+ countries), SWIFT/BIC codes, and various regional banking number formats. These numbers must be protected under numerous financial regulations and industry standards.
Any pipeline, same result: Whether you're processing banking documents, payment records, customer correspondence, or financial reports, our API ensures that account numbers are consistently identified and redacted while maintaining data utility for audit and analysis purposes.
Support for IBAN, BBAN, and regional formats
Validate IBAN and routing number checksums
Meet financial privacy regulations
Bank account numbers present extreme financial risks when exposed. Unlike credit cards which have fraud liability protections, unauthorized access to bank account numbers can result in direct, immediate, and often irreversible financial losses through ACH fraud, wire transfers, or unauthorized debits.
Critical Risk: Bank account numbers combined with routing numbers provide everything needed for ACH debits. Unlike credit card fraud, bank account fraud often has limited protection and longer recovery timelines. Proper redaction is essential.
Financial account information is protected under numerous regulations:
Customer service logs, correspondence, and internal documents containing account references need redaction for analysis and sharing.
Employee direct deposit information must be protected in HR records and communications.
Vendor and customer banking details in invoices and payment records require protection.
Financial documents in legal proceedings may need account number redaction before production.
Financial reports for auditors often need account details masked while preserving transaction data.
Chat logs and email correspondence containing banking details should be anonymized before storage.
Best Practice: Implement account number redaction at the earliest possible point in your data pipeline. The less time unredacted account numbers exist in your systems, the lower your risk exposure.
Get started with bank account number redaction in just a few lines of code. This example demonstrates the simplest way to detect and redact account numbers from text using our API.
from anonymization import Client client = Client(api_key="your_api_key") result = client.anonymize( text="Wire funds to account 1234567890123", entity_types=["BANK_ACCOUNT"] ) print(result.anonymized_text) # Output: Wire funds to account [BANK_ACCOUNT]
const { AnonymizationClient } = require('@anonymization/api'); const client = new AnonymizationClient('your_api_key'); const result = await client.anonymize({ text: "Wire funds to account 1234567890123", entityTypes: ["BANK_ACCOUNT"] }); console.log(result.anonymizedText); // Output: Wire funds to account [BANK_ACCOUNT]
curl -X POST https://api.anonymizationapi.com/v2/anonymize \ -H "Authorization: Bearer your_api_key" \ -H "Content-Type: application/json" \ -d '{ "text": "Wire funds to account 1234567890123", "entity_types": ["BANK_ACCOUNT"] }'
The API response includes metadata about the detected account number:
{
"anonymized_text": "Wire funds to account [BANK_ACCOUNT]",
"entities": [
{
"type": "BANK_ACCOUNT",
"text": "1234567890123",
"start": 20,
"end": 33,
"confidence": 0.95,
"metadata": {
"account_type": "US_BANK_ACCOUNT",
"length": 13
}
}
]
}
Bank account numbers vary significantly by country and institution. Our API recognizes and validates multiple formats.
US bank account numbers typically range from 8-17 digits, varying by institution:
IBANs are standardized international account identifiers with built-in validation:
Bank identifier codes used for international transfers:
The API also detects country-specific account formats:
# Detect all bank account types result = client.anonymize(text, entity_types=[ "BANK_ACCOUNT", # Generic account numbers "IBAN", # International IBANs "SWIFT_BIC", # SWIFT/BIC codes "ROUTING_NUMBER" # US ABA routing numbers ]) # Enable IBAN checksum validation result = client.anonymize( text=text, entity_types=["IBAN"], options={"iban_validate": True} )
Pattern recognition, checksum validation, and context analysis work together on every request.
Choose the appropriate anonymization technique based on your security requirements and data utility needs.
Completely replaces the account number with a placeholder. Maximum security for general use.
Shows only the last 4 digits for verification purposes while hiding the rest.
Maintains the account number format while masking most digits. Useful for testing.
Replaces with a reversible token. Useful when you need to de-anonymize later with proper authorization.
Generates a fake but valid-looking account number. Same input produces same output within a session.
# Full redaction (default) result = client.anonymize(text, entity_types=["BANK_ACCOUNT"], mode="redact") # Show last 4 digits result = client.anonymize(text, entity_types=["BANK_ACCOUNT"], mode="mask", options={"account_show_last": 4}) # Format-preserving mask for IBANs result = client.anonymize(text, entity_types=["IBAN"], mode="mask", options={"preserve_format": True}) # Tokenization (reversible with key) result = client.anonymize(text, entity_types=["BANK_ACCOUNT"], mode="tokenize") # Pseudonymization result = client.anonymize(text, entity_types=["BANK_ACCOUNT"], mode="pseudonymize")
Four production-ready patterns for redacting financial identifiers in real workloads.
Redact account numbers from transaction records:
payment_records = [
"ACH Credit to 123456789012, routing 021000021, $1,500.00",
"Wire transfer to IBAN DE89370400440532013000, EUR 2,500",
"Debit from account ending 7890, sort code 12-34-56"
]
results = client.batch_anonymize(
items=[{"text": record} for record in payment_records],
entity_types=["BANK_ACCOUNT", "IBAN", "ROUTING_NUMBER"]
)
for r in results:
print(r.anonymized_text)
# ACH Credit to [BANK_ACCOUNT], routing [ROUTING_NUMBER], $1,500.00
# Wire transfer to [IBAN], EUR 2,500
# Debit from account ending [BANK_ACCOUNT], sort code [SORT_CODE]
Combine account number redaction with other financial identifiers:
financial_doc = """ Payment Instructions: Beneficiary: John Smith Bank: Chase Bank NA Account: 123456789012 Routing: 021000021 SWIFT: CHASUS33 Amount: $10,000.00 """ result = client.anonymize( text=financial_doc, entity_types=[ "PERSON", "BANK_ACCOUNT", "ROUTING_NUMBER", "SWIFT_BIC" ] ) print(result.anonymized_text)
Validate IBANs before redaction to ensure accuracy:
# Validate IBAN checksums result = client.anonymize( text="Transfer to IBAN DE89370400440532013000", entity_types=["IBAN"], options={"iban_validate": True} ) # Check validation status in metadata for entity in result.entities: if entity.metadata.get('iban_valid'): print(f"Valid IBAN from {entity.metadata['country']}") else: print(f"Invalid IBAN checksum detected")
Scan application logs for accidentally logged account numbers:
def scan_logs_for_accounts(log_file): with open(log_file, 'r') as f: content = f.read() result = client.detect( text=content, entity_types=["BANK_ACCOUNT", "IBAN", "ROUTING_NUMBER"] ) if result.entities: print(f"ALERT: Found {len(result.entities)} account numbers in logs!") for entity in result.entities: print(f" - {entity.type} at position {entity.start}") return True return False
Five rules that keep account numbers out of the wrong hands at every layer of your stack.
An account number alone may be less useful to attackers, but combined with routing numbers, they enable ACH fraud:
# Always redact both account and routing numbers result = client.anonymize( text=text, entity_types=["BANK_ACCOUNT", "ROUTING_NUMBER"] ) # Also consider SWIFT codes for international transfers result = client.anonymize( text=text, entity_types=["BANK_ACCOUNT", "IBAN", "ROUTING_NUMBER", "SWIFT_BIC"] )
Enable context analysis to distinguish account numbers from other similar numeric sequences:
# Context helps distinguish: # "Account: 123456789012" -> BANK_ACCOUNT # "Order #123456789012" -> Not a bank account result = client.anonymize( text=text, entity_types=["BANK_ACCOUNT"], options={"use_context": True} )
IBAN validation significantly reduces false positives:
# Enable IBAN checksum validation result = client.anonymize( text=text, entity_types=["IBAN"], options={ "iban_validate": True, "iban_countries": ["DE", "GB", "FR", "ES"] # Expected countries } )
For any display or logging purposes, limit visible digits:
Security Rule: Never display or log more than the last 4 digits of a bank account number. Even showing the first few digits can narrow down the institution and make fraud easier.
Defense in depth: apply account number redaction at multiple points:
Our detection achieves 99.5% accuracy with context analysis enabled. For IBANs with checksum validation, accuracy exceeds 99.9%. False positives from random numeric sequences are minimized through length validation and contextual clues.
Yes, the API can detect partial account numbers (like "ending in 7890") when context indicates a bank account reference. Use: options={"detect_partial": True}. Note this may increase false positives.
The API normalizes various formats automatically. Account numbers with spaces, dashes, or other separators are detected: "1234 5678 9012", "1234-5678-9012", and "123456789012" are all recognized as the same account.
Cryptocurrency addresses (Bitcoin, Ethereum, etc.) can be detected with: entity_types=["CRYPTO_ADDRESS"]. These use different patterns than traditional bank accounts and are handled separately.
Yes, US ABA routing numbers have a built-in checksum. Enable validation with: options={"routing_validate": True}. Invalid routing numbers will be flagged with lower confidence.
For check images or scanned documents, use OCR first to extract text, then pass to our API. We can recommend OCR partners if needed. The MICR line at the bottom of checks contains routing and account numbers in a specific format that our API recognizes.
The API detects account numbers regardless of account type. The format and detection is the same for personal, joint, business, and other account types.
Protect financial data with 99.5% accurate detection. Support for US accounts, IBANs, and 80+ countries.