Skip to main contentData extraction is the core process that transforms raw bank statements into structured, analyzable transaction data. LedgerBeam’s advanced extraction technology handles various document formats and layouts to accurately identify and extract all relevant financial information.
Optical Character Recognition (OCR)
Our OCR engine uses state-of-the-art machine learning models to convert images and scanned documents into machine-readable text. Advanced OCR features include multi-language support that recognizes text in multiple languages and character sets, handwriting recognition that processes handwritten notes and annotations, layout preservation that maintains spatial relationships between text elements, and quality enhancement that automatically improves image quality for better recognition.
OCR accuracy shows 99%+ accuracy for standard printed bank statements, 85%+ accuracy for clear handwritten annotations, 95%+ accuracy for multi-column statements, and 90%+ accuracy even with poor image quality scans.
Text Processing Pipeline
Once text is extracted, our processing pipeline normalizes and structures the data through text normalization that fixes common OCR errors and character recognition mistakes, converts dates, amounts, and other data to standard formats, processes various text encodings and special characters, and cleans up spacing and formatting issues.
Layout analysis identifies and processes tabular data structures through table detection, distinguishes between different data columns through column recognition, recognizes statement headers and metadata through header identification, and extracts summary information and totals through footer processing.
Transaction Identification
Pattern Recognition
Our AI models identify transactions using sophisticated pattern recognition that recognizes various date formats like MM/DD/YYYY and DD-MM-YYYY, identifies monetary amounts with proper decimal handling, extracts transaction descriptions and merchant names, and tracks running balances and account totals.
Multi-format support handles common bank statement layouts through standard bank formats, processes credit card transaction formats, extracts investment transaction data, and supports various international banking formats.
Data Validation
Each extracted transaction undergoes rigorous validation through data completeness checks that ensure all required fields are present, format validation that verifies dates, amounts, and other data formats, logical consistency checks for logical inconsistencies in the data, and cross-reference validation that compares against known patterns and databases.
Error detection identifies incomplete or unclear transactions through missing data detection, finds and handles duplicate transactions through duplicate detection, verifies running balances match extracted transactions through balance reconciliation, and flags unusual or suspicious transactions through anomaly detection.
Core Transaction Fields
Our system extracts standard transaction fields including essential fields like transaction date showing when the transaction occurred, amount with transaction value and proper currency detection, description with raw transaction description from the bank, transaction type indicating debit, credit, transfer, or other type, and running balance showing account balance after the transaction.
Additional fields include reference numbers with bank reference numbers and transaction IDs, check numbers for check-based transactions, posting date showing when the transaction was posted to the account, effective date indicating when the transaction becomes effective, and memo fields containing additional notes or memo information.
Beyond transaction data, we extract valuable metadata including account information with account identifiers that are masked for security, account types like checking, savings, and credit card, statement period showing the date range covered by the statement, and opening/closing balances indicating account balances at period start and end.
Bank information includes the financial institution name, branch details when available, bank contact details, and required regulatory disclosures.
Multi-Currency Support
Our system handles transactions in various currencies through currency detection that identifies currency symbols like $, €, £, and ¥, recognizes ISO currency codes such as USD, EUR, and GBP, handles different decimal and thousands separators through amount formatting, and can integrate with exchange rate data.
We support various international banking formats including US banking with standard US bank statement formats, European banking with SEPA and European banking formats, Asian banking with various Asian banking system formats, and Latin American with regional Latin American banking formats.
Complex Document Handling
Our system can process complex financial documents including multi-page statements that handle statements spanning multiple pages, combined statements that process statements with multiple account types, investment statements that extract investment transaction data, and loan statements that process loan payment and interest information.
Quality Metrics
Our data extraction achieves high accuracy across various metrics with field-level accuracy showing 99%+ accuracy for transaction dates, 99%+ accuracy for amounts, 95%+ accuracy for descriptions, and 98%+ accuracy for balances.
Overall processing shows 95%+ of transactions are fully extracted, 4% require minor manual review, and less than 1% fail extraction entirely.
Speed metrics show small documents with less than 50 transactions are processed in under 30 seconds, medium documents with 50-500 transactions take 1-3 minutes, and large documents with more than 500 transactions take 3-10 minutes.
Throughput includes concurrent processing that handles multiple documents simultaneously, efficient processing queue with priority handling, and resource optimization that balances speed and accuracy.
Error Handling
OCR-related issues are handled through image enhancement for poor image quality, specialized handwriting recognition for handwritten text, advanced layout analysis for complex layouts, and multi-language OCR for multiple languages.
Data quality issues include missing information that is flagged for manual review, inconsistent formats that are normalized through format standardization, duplicate transactions that are detected and handled appropriately, and balance discrepancies that are identified and reported.
Recovery Mechanisms
When extraction issues occur, our system retries failed extraction steps through automatic retry, uses backup extraction methods through alternative methods, flags transactions requiring human review through manual review, and continues processing successful transactions through partial success.
Getting Started
Ready to extract data from your bank statements? Check out our API Reference for detailed endpoint documentation, or visit our Quick Start Guide to begin extracting transaction data in minutes. 
