In the world of business, invoices are an essential part of daily operations. They represent a request for payment and provide a detailed breakdown of goods or services provided. Manual invoice data extraction is a redundant and slow process that involves collecting the invoices from the suppliers and feeding the details in the ERP for approval and further processing.
Since the last few decades, there has not been much of a technological shift in invoice processing, and because of that, the accounts payable department in an enterprise is loaded with a plethora of document-based invoices. Most of large-scale organizations have more than 500 suppliers and manually processing an influx of invoices is a slow and cumbersome process. Due to less visibility and error-prone processing, a lot of time and cost are spent which leads to delayed payments and reworking on the erroneous invoices. To combat the issues involved in manual processing, Artificial intelligence is being used for invoice data extraction to automate business processes and decrease the turnaround time.
What is Invoice Data Extraction?
Invoice data extraction refers to the process of extracting relevant data from an invoice, such as the invoice number, supplier name, date, and line item details. This data is then used for various purposes, such as accounting and financial reporting.
Traditionally, invoice data extraction has been a manual process, with employees manually entering data into a computer system. However, this approach is time-consuming and error-prone, particularly when dealing with a large volume of invoices.
Optical Character Recognition coupled with Artificial Intelligence can simplify invoice data extraction by reading the information in the documents and presenting them in a digital format. It can also pick up on human errors and resolve them to produce a meaningful text which can be analyzed later.
Using AI for Invoice Data Extraction
AI can be used to automate many aspects of invoice data extraction, making the process faster and more accurate. There are several AI-based techniques that can be used for invoice data extraction, including:
1.Optical Character Recognition (OCR)
Optical character recognition is a technology that enables computers to read and interpret text from an image or scanned document. OCR can be used to extract data from invoices, such as the invoice number, supplier name, and date.
2.Natural Language Processing (NLP)
NLP is a field of AI that focuses on enabling computers to understand and interpret human language. NLP can be used to extract line item details from invoices, such as the product or service provided, quantity, and price.
3.Machine Learning (ML)
ML is a technique that enables computers to learn from data and improve their performance over time. ML can be used for invoice data extraction by training a machine learning model to recognize and extract relevant data from invoices.
Benefits of Invoice Data Extraction Using AI
Intelligent Document Processing (IDP) is the process of intelligently capturing the domain-specific data across documents and streamline document routing activities using AI-based methods.
Regardless of what kind of document needs to be processed, scanned or native PDFs, structured or unstructured, IDP serves a single purpose: to extract structured information without the need to define rules or templates.
Some of the industrial benefits of using invoice data extraction using AI are discussed below:
Decreased Manual Intervention
In an automated data extraction system, manual intervention is reduced by almost 70 to 80 percent. An Intelligent Document Extraction tech that leverages OCR document scanner and artificial intelligence (AI) can identify the various data types without any need for a specific template and can intelligently read the data and convert it into a digital text. At the end of the data extraction, one can cross-check the details for any errors.
Higher Accuracy of Extraction
Invoice data extraction using AI can reduce errors and increase the efficiency of the system and can deliver faster results in comparison to manual processing. The advanced, deep learning method of data capturing keeps on correcting the errors and becomes highly efficient with time. With the help of AI, more invoices can be processed accurately in less time, increasing system efficiency, and productivity.
Time Saving in Invoice data extraction and Processing
A technology-driven processing system will help in reducing the time required to process a single invoice and the cognitive data capture through Intelligent Data Extraction will shorten the processing cycle resulting in processing more invoices in less time. A well-established system using Artificial intelligence for data extraction can reduce the turnaround time by five times and increases the overall productivity of the system by 200%.
Cost Reduction
Artificial Intelligence helps in template-less invoice extraction of data from the invoices and there is no need for customization and configuration of rules for the extraction process. This helps in saving the implementation costs, efforts of the accounts departments, and operational expenses on processing the invoices. The increased efficiency of the system and reduction of errors helps in achieving significant returns in a short time.
Efficient Process Management
Invoice data extraction using AI will help reduce the rework required for erroneously processed invoices, avoid late payment fees and penalties by the suppliers for the left-out invoices, and reduce human interference with less approval time for invoicing. An efficiently managed system will improve the relationship with the vendors and help in becoming a result-driven and optimally functioning organization.
How Invoice Data Extraction Using AI Works?
Invoice data extraction using artificial intelligence works in a self-sustainable manner to understand the format of the documents being scanned and learn and adapt with time. As there is no particular format followed for the invoices, an AI-powered optical character recognition software is made with the capability to comprehend the data using deep learning, natural language representation, and optical character recognition.
The unstructured data is converted into a structured format and can be used either to directly fill the customer forms and bills or on the enterprise resource planning systems for reconciliation with the invoices. The automated processing results in an error-free and optimized data ready to be used for analysis.
KlearStack: Invoice data extraction using AI and Deep learning
KlearStack is a software solution that uses AI, ML, and deep learning technology to automate the process of invoice data extraction.
Here’s how it works:
- Document Capture: KlearStack first captures the invoice document in any format, whether it’s a scanned image, PDF, or digital file.
- Data Extraction: Once the invoice document is captured, KlearStack’s AI algorithms extract relevant data from the invoice, such as the invoice number, supplier name, date, and line item details. This is done using a combination of OCR, NLP, and ML techniques.
- Data Validation: KlearStack then validates the extracted data to ensure accuracy and completeness. For example, KlearStack can check that the invoice number matches the supplier name and that the line item details add up correctly.
- Data Export: Finally, KlearStack exports the extracted data to the appropriate system, such as an accounting software or ERP system.
Conclusion
Intelligent invoice data extraction using AI can help businesses like finance, banking, and legal with loads of paperwork and invoices streamline their processes and save resources on manual invoicing.
KlearStack’s artificial intelligence-led solutions are created to solve the challenges of the industry and equip them to undertake a technological leap to upgrade the invoicing process and reap the benefits of the highly productive system.
