Strip financial PII before it leaves your machine.
Account numbers, transaction details, and client identities are detected and anonymized locally across statements, reports, and audit logs. No data ever reaches the cloud.
What Piixie detects in financial documents.
Piixie identifies and anonymizes account numbers, payment card data, tax identifiers, transaction details, and credit information across every financial document type.
Account identifiers
Bank account numbers, routing numbers, IBAN, SWIFT codes, check numbers, and ACH references.
Payment card data
Primary account numbers (PANs), CVVs, expiration dates, cardholder names, and track data.
Tax identifiers
SSNs, EINs, ITINs, taxpayer names, W-2 and 1099 payer information, and filing status.
Transaction details
Transaction amounts, merchant names, payment dates, running balances, and wire transfer details.
Credit information
Credit scores, credit bureau data, delinquency records, loan numbers, and creditworthiness indicators.
Investment data
Brokerage account numbers, portfolio holdings, dividend records, cost basis, and beneficiary designations.
Credit report, before and after.
Piixie detects PII across consumer records, account details, and financial identifiers in credit and banking documents.
How Piixie processes financial documents.
From ingestion to anonymized output, every step happens on your machine. No data ever crosses a network boundary.
1. Load financial document
Drop a bank statement, tax return, credit report, or loan document into Piixie. PDF, DOCX, spreadsheets, and scanned images are all supported.
2. Detect financial PII
The local LLM scans extracted text for account numbers, PANs, SSNs, EINs, transaction amounts, merchant names, and credit information.
3. Anonymize
Choose your mode: redact with black bars, replace with stable tokens like [ACCOUNT_1], or synthesize realistic fake data using the local Faker engine.
4. Export with audit log
The anonymized document is saved locally. The original is never modified. A full audit log records every detected entity and the action taken.
Regulatory frameworks Piixie addresses.
Local processing eliminates entire categories of compliance risk across every framework that governs financial data.
- PAN masking meets PCI DSS requirements: primary account numbers, CVVs, and expiration dates are fully redacted before documents leave your machine.
- Cardholder data environment (CDE) scope is reduced because Piixie anonymizes payment card data locally, preventing it from entering shared systems or third-party processors.
- GLBA non-public personal information (NPI) is protected: customer names, account numbers, income data, and Social Security numbers are detected and anonymized automatically.
- Credit report data is handled in compliance with FCRA requirements. Consumer identifiers, account details, and credit scores are stripped before sharing with non-authorized parties.
- Tax return anonymization removes SSNs, EINs, taxpayer names, income figures, and filing details, enabling safe sharing of tax data for analytics or audit purposes.
- Audit trails document exactly which financial PII elements were detected and redacted, providing evidence for regulatory examinations and compliance reviews.
How financial teams use Piixie.
From compliance departments to data science teams, Piixie fits into existing financial workflows.
Analytics-ready datasets
Strip client identifiers from transaction logs, statements, and reports to create clean datasets for business intelligence, trend analysis, and forecasting without exposing customer PII.
Vendor and auditor sharing
Anonymize financial documents before sharing with external auditors, consultants, or technology vendors. Reduce the risk of data breaches during third-party engagements.
Regulatory compliance reports
Prepare anonymized versions of financial records for regulatory submissions where raw customer data is not required. Meet SOX, GLBA, and PCI DSS documentation requirements safely.
Fraud detection model training
Feed anonymized transaction data into machine learning pipelines to train fraud detection models without exposing real customer account numbers, names, or financial details.
Protect financial data at the source.
Start anonymizing financial reports locally. No cloud account, no data agreements.