5 Reasons Why Using OCR Invoice Capture In Account Payable Process Is Not Enough?
If you are looking to optimize your workflow, specifically optimize your process and OCR invoice capture in Accounts Payable (AP), relying solely on optical character recognition (OCR) technology is not a long-term strategic decision. While automating the extraction of data from invoices reduces manual data entry, does it improve the invoice processing process to a significant extent?
The answer is often: Not enough. Although OCR invoice capture can recognize and extract text data from invoices, it still has many limitations that prevent your application of this technology from really seeing positive results. Read on to find out why OCR alone is not enough to extract invoices?
What is OCR Invoice Capture?
Optical Character Recognition (OCR) in invoices is a technology that allows for the automatic extraction of data from scanned or electronic invoices. OCR software uses algorithms to recognize and interpret characters, such as numbers and letters. OCR converts images containing text into editable text on a computer, transforming documents from image formats (such as JPG, PDF) into editable text formats (such as DOCX, TXT).
5 Reasons Why Using Only OCR Invoice Capture in Accounts Payable (AP) Processes Is Not Enough
1. OCR Invoice Capture Cannot Process Semi-Structured and Unstructured Data
OCR works well and provides high accuracy only for documents and images with clear structures, such as pre-printed texts in fixed templates. However, in modern AP processes, documents and invoices from various sources come in many different formats. These invoices contain small details or even images. This is a limitation that OCR cannot handle, or if it does, it produces many errors.
For semi-structured data (containing both fixed and flexible elements) or unstructured data (free-form data like emails and notes), OCR lacks the capability to process and extract data. Since many documents in the AP process contain such data, businesses must still rely heavily on manual operations alongside OCR.
2. Accuracy Limitations
The accuracy of OCR in processing documents is typically between 85% and 90%. This means that data extraction from invoices is not fully automated and is not 100% accurate. OCR can recognize text, but when dealing with low-quality image documents or complex formats, such as handwritten, multilingual texts, the error rate can increase significantly. Especially in Accounts Payable (AP) processes, information like account numbers, amounts, or dates is critical, and errors are unacceptable.
The limitations in accuracy after data extraction can lead to serious consequences. Incorrect data can result in issues such as incorrect payments, financial losses, or even damage to reputation and relationships with customers and suppliers.
Specifically, data errors in AP processes can severely impact other systems and processes in the business:
- Financial Accounting Systems: Incorrect journal entries and financial reports can lead to discrepancies between books and reality, reducing the reliability of reports, affecting management decisions, and even leading to legal risks when filing tax reports or audits.
- Budget Management Processes: If AP data is inaccurate, budget tracking and management become difficult, leading to overspending or a loss of financial control, which negatively impacts projects or business strategies.
- Supplier Relationship Management (SRM) Systems: Errors in AP, such as incorrect or late payments, can harm the relationship between the business and its suppliers. Payment discrepancies can result in overpayment or underpayment, leading to misunderstandings and conflicts.
- Procurement and Inventory Control Processes: AP is closely linked to the Procure-to-Pay (P2P) process. If AP data is inaccurate, reconciling invoices with purchase orders (PO) or goods received notes becomes difficult. This can lead to incorrect order payments, affecting inventory management and supply chain planning.
- Risk Management and Audit Systems: Inaccurate AP data makes it harder to detect and manage risks, impacting the ability to identify fraud or internal irregularities within the company.
3. Manual Work Is Still Necessary
Due to accuracy limitations and the types of data OCR can process, businesses using OCR still require human involvement for manual tasks. After processing documents and invoices with OCR, humans must review all the data to identify errors. Then, staff must correct these mistakes to ensure the accuracy of other processes. At the end of the month, when there are many invoices to process, this verification step is time-consuming and labor-intensive. While OCR helps save time in data extraction, many steps still require manual handling.
4. Inability to Extract All Relevant Data Fields from New Invoice Types
OCR technology is quite old, dating back to the 1970s, but it has only been widely used in recent years. This means OCR is not a suitable technology solution for all types of invoices.
Today, many invoices are presented in different formats and styles. In the AP process, instead of fixed and simple invoice templates like before, many invoices now contain complex tables with multiple rows and columns, notes, or non-standard payment terms, logos, images, and other graphic elements, as well as special characters and multilingual text. This further reduces OCR’s accuracy.
5. Lack of Workflow Automation Capabilities
In the AP process, OCR invoice capture only handles a small part—recognizing and converting text from images into digital data. It cannot automate the entire end-to-end process. Moreover, it cannot integrate or automate subsequent steps like approvals or invoice matching. This means human intervention is still required for the remaining steps in the process, reducing efficiency, increasing the risk of human errors, slowing down the process, and increasing labor costs.
Although OCR is a useful tool for extracting and converting text, using only OCR invoice capture in the Accounts Payable (AP) process is insufficient. To achieve full automation and save time, businesses should consider using Intelligent Document Processing (IDP) technology, a solution that integrates OCR with artificial intelligence (AI) and machine learning and some other technologies. IDP helps businesses automate the entire AP process from start to finish, ensuring greater accuracy and efficiency.
AFusion – a company providing outsourcing solutions, especially automation solutions for the AP process – is ready to assist you!
Email: sales@afusion.ai
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