Bank Statement Parser API

The Bank Statement Parser API supports PDF statements from over 500 financial institutions including Chase, Bank of America, Wells Fargo, Citibank, HSBC, Barclays, and regional credit unions worldwide. It processes both digitally-generated PDFs (where text is embedded) and scanned paper statements using integrated OCR with automatic image preprocessing for skew correction and contrast enhancement. The API also handles password-protected PDFs when the password is provided as a parameter. For scanned documents, the engine supports resolutions from 150 to 600 DPI and automatically detects multi-page layouts, column formats, and table structures specific to each bank's statement template.

Security is fundamental to our architecture. All document uploads and API responses are transmitted over TLS 1.3 encrypted connections. Uploaded documents are processed in isolated, ephemeral containers and permanently deleted within 60 seconds of processing completion — no financial data is persisted on our servers. We comply with PCI DSS Level 1 requirements, SOC 2 Type II standards, and GDPR data protection regulations. Sensitive fields like full account numbers and routing numbers are automatically masked in API responses (e.g., ****1234), and you can configure additional masking rules via request parameters. Our infrastructure undergoes quarterly penetration testing and annual security audits by independent third-party firms.

The API extracts a comprehensive set of structured fields organized into logical sections. Bank information includes the institution name, address, phone number, routing number, and SWIFT/BIC code. Account details cover the account holder name, masked account number, account type (checking, savings, money market), and currency. The statement period section provides start date, end date, and statement generation date. Balance information includes opening balance, closing balance, available balance, and pending amounts. Each transaction record contains the date, description, debit or credit amount, running balance, reference number, merchant name, location, and auto-assigned category. The financial summary aggregates total credits, total debits, transaction count, fees charged, and interest earned.

Yes, the API uses a multi-layered classification system combining rule-based matching and machine learning to categorize transactions with over 95% accuracy. Standard categories include direct deposits, payroll, ACH transfers, wire transfers, ATM withdrawals, point-of-sale purchases, online payments, recurring subscriptions, loan payments, utility bills, and bank fees. The ML model is trained on millions of real transaction descriptions and continuously updated to recognize new merchants and payment patterns. Enterprise plans support custom categorization taxonomies, allowing you to define business-specific categories like department codes or cost centers that map to your internal accounting chart of accounts.

Our API achieves over 98% field-level accuracy in data extraction, validated against a benchmark dataset of 50,000+ bank statements from diverse institutions and formats. Accuracy is measured across individual fields: numerical values like amounts and balances achieve 99.2% accuracy, dates achieve 98.7%, and free-text fields like merchant names achieve 96.5%. The system uses a two-pass extraction approach — the first pass applies template-matched extraction for known bank formats, while the second pass uses adaptive ML models for unfamiliar layouts. A confidence score is returned for each extracted field, allowing your application to flag low-confidence results for manual review rather than accepting potentially incorrect data.

Yes, the Bank Statement Parser API supports statements from international banks across multiple countries and currencies. The API recognizes various date formats (DD/MM/YYYY, MM/DD/YYYY, YYYY-MM-DD), currency symbols and ISO codes (USD, EUR, GBP, JPY, CAD, AUD, and 50+ others), and multi-language bank statements in English, Spanish, French, German, Portuguese, Italian, Dutch, and other major languages. The OCR engine handles non-Latin scripts including Chinese, Japanese, Korean, Arabic, and Cyrillic characters. The extracted data is normalized into a consistent JSON format regardless of the source bank or country, with amounts converted to standard decimal notation and dates standardized to ISO 8601 format, making it straightforward to work with international financial data in a unified pipeline.