Invoice processing is a critical yet time-consuming task for any business dealing with suppliers and invoices. But automating this crucial process is now possible with invoice parsing.
What is Invoice Parsing?
Invoice parsing uses optical character recognition (OCR), natural language processing (NLP), and other automation techniques to extract key information from paper or digital invoices and input it into an accounting system or database.
The key data points extracted include:
- Supplier/Vendor name
- Invoice date
- Invoice number
- Line item details
- Taxes
- Total amounts
Once extracted, this data is validated to catch any inaccuracies from the scanning process. It is then structured into formats like CSV files or database records that integrate with popular accounting software.📥
Invoice parsing process
Implementing invoice parsers to automatically extract and process this data provides immense time and cost efficiencies.
Benefits of Automated Invoice Data Extraction
Extracting invoice data automatically creates significant benefits across accounting and AP processes:
80% Faster Processing
Automating data extraction with parsers cuts invoice handling time dramatically, freeing up staff for more strategic initiatives.📈
Improved Accuracy
Algorithmic data extraction minimizes human error that leads to penalties, write-offs, and revenue impacts down the line.🔎
Real-Time Insights
With automated approaches getting data in real-time vs batch processing, businesses can access more timely analytics.
Cost Savings
Invoice automation solutions with parsing can lower processing costs by 50-80% when factoring in labor, errors, and fraud.💰
The Progression of Invoice Processing Technology
Before exploring how today‘s AI-driven invoice parsers work, it helps to understand the evolution of processing methods over time.
Excel and Manual Data Entry
Initially, AP clerks would receive paper or PDF invoices and manually type all relevant supplier, item, date, and amount details into Excel or accounting software. This was an inefficient, error-prone process that struggled to scale.
Template-Based Scanning
Next came scanning and OCR to convert paper invoices into digital files. Staff then keyed the data into preset forms or templates to pull it into the correct systems. Still manual but helped move away from paper storage.
Rules-Based Parsing
The first generation of automated parsers relied on rigid rules and templates to interpret invoices, identifying relevant fields to extract based on their position on a page rather than meaning. These didn‘t easily handle irregularities.
AI-Driven Machine Learning Parsers
Today‘s cutting edge solutions instead apply NLP and advanced ML techniques to handle variety and complexity in supplier invoice formats while continuously improving accuracy through feedback loops.👩💻
This evolution has graduated from strictly manual > basic automation > rules-based systems > to today‘s intelligent invoice processing capable of understanding documents like humans.
Challenges With Manual Invoice Processing
Manual invoice processing creates major efficiency and risk challenges:
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Time-Consuming: Processing invoices manually takes ~17 days on average. As volumes increase, existing staff can‘t keep up. 🕛
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Prone to Errors: Humans make mistakes which lead to write-offs, penalties, and revenue impacts over time if not caught. Fat finger errors, duplicate invoice risks, missed cash discount opportunities, and fraud easily occur. 📝
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Format Challenges: Suppliers large and small create invoices in different formats, making it hard for AP teams to accurately capture data. ❓
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Data Loss Risk: Misinplacing or losing paper invoices lead to missing supplier and accounting data with no backup records to address disputes or audits. 🗃️
Deloitte found that 61% of CEOs have already automated invoice processing to address these document handling pain points – and for good reason.
How Intelligent Invoice Parsers Work
Sophisticated invoice parsing solutions use advanced automation techniques across five key steps:
1. Document Input
Incoming invoices are ingested by the platform in any format – scanned images, PDFs, e-invoices, EDI documents etc.
To handle variety, today‘s AI parsers don‘t require templates or strict rules upfront.
2. Optical Character Recognition
OCR software converts scanned images or PDF invoices into machine-readable data by identifying characters. This preserves original documents while pulling text.
Some tools like Rossum use a technique called Cognitive OCR reinforcing character recognition with context from NLP algorithms.
3. Information Extraction
Here‘s where automated parsers analyze words, phrases, and relationships within invoices to extract required datapoints.
Using contextual understanding, they identify relevant values for supplier name, addresses, VAT numbers, invoice dates, line item descriptions, units, quantity, unit prices, and totals.
Information extraction from an invoice (Credit: Rossum)
In this example data is assigned to highlighted fields automatically even without templates, learning what data belongs in which column.
4. Data Validation
Next parsers run logic checks detecting outliers against past invoices, duplicates, or known vendors to validate extracted fields, reducing errors before export.
For example, dates that don‘t make chronological sense or prices far outside expected ranges get flagged for verification.
5. Export Parsed Data
Finally, structured parsed invoice data gets exported into shareable file formats like CSV/XLS or to accounting software via integrations.
This hands off validated, accurate information to business systems and finance teams.
