Today’s accounting departments are faced with multiple challenges, including long invoice processing time, difficulties managing large data volumes, and high associated costs.
Available solutions for invoice processing, such as manual data entry and template-based Optical Character Recognition (OCR), are slow, expensive, have high error rates, and therefore do not meet the needs of the modern Accounts Payable (AP) professionals.
Advances in Artificial Intelligence (Al), specifically cognitive data capture, can bring document data extraction to a new level of efficiency and efficacy, freeing employees from repetitive low-level work and letting them instead concentrate on added-value activities.
The automation can have a fast, direct, and measurable impact on many processes including not only invoice processing itself, but also compliance, security, relationships with external and internal parties, early-payment discounts, improved workflow planning, and the reduction of costs associated with lost and error-ridden documents.
In this white paper, we identify the biggest challenges in invoice data capture and explain how cognitive field extraction can help companies save time and money, as well as improve the accuracy and productivity of their accounting departments.
Current state of invoice processing
Over 49% of the total number of invoices issued globally are still processed manually, on paper. For AP departments and accounting firms, this means time, money, and resources wasted on manual data entry as well as diminished employee satisfaction due to the highly repetitive nature of their work.
However, companies are realizing that automation is a crucial tool to improve their AP processes. It can help reduce costs, decrease the number of lost or missing invoices, save time in the invoice approval process, and address a variety of other ongoing issues of accounting departments.
Available data entry solutions
AP invoice extraction is a complex process and its optimization is a struggle for many companies. Manual data entry is no longer a viable solution due to increasing labor costs, slow processing times, and high error rates. Alternatives, such as template-based OCR, solve some of these issues, but still require a great deal of time and investment to implement.
Traditional OCR technology
Template-based OCR is a widely used technology for capturing data from business documents. This technology generates a text layer from the document and then recognizes data fields using either image-based templates or text-based rules.
This solution works well on properly scanned documents (e.g. correct format, not rotated or blurred), and can only process documents with no alternation, such as fixed forms or generated reports.
The downsides of template-based OCR
Traditional OCR requires rules and templates in order to accurately capture the necessary d at a. Each individual alteration of the document requires a long and expensive configuration process. Thus, the companies with high document variability will be faced with (l) a variety of errors, including false positives, or (2) a need to constantly create new configuration rules for each new invoice field.
Cognitive data capture technology emerged as a practical tool to automate invoice data extraction and allow companies with high document variability to optimize their invoice processing.
Invoice extraction with AI: Cognitive data capture
Cognitive data capture uses deep neural networks to recognize patterns in a set of data and adapt to changing input. The technology infers the underlying general structure of invoices in a similar way a human mind does. Contrary to manual data entry or traditional OCR, it does not require a sizable workforce and the set up of endless rules or templates. Moreover, this solution is highly accurate and adapts to all kinds of layouts.
The key AP issues addressed by cognitive data capture
Benefits of AI in invoice data extraction
Cognitive data capture greatly reduces the time spent on invoice processing. Automation allows a company to drastically reduce the time of manually processing a single invoice from 3 minutes to 30 seconds. Thus, 6x the number of documents can be processed simultaneously, leaving more time for higher priority tasks.
Moreover, the technology is self-learning and does not require any layout configuration, rules, or templates. This saves a great deal of effort in the implementation phase, and with cloud-based technology, roll-out is possible on the same day.
Automation leads to more accurate and faster results compared to humans under the same conditions. With the development of Al, which constantly gathers information and self-corrects, it learns to become more efficient and reduces common human errors. Cognitive data capture is now able to reach 98% accuracy. As a result, users can pay more invoices with higher precision and on time.
When it comes to invoicing automation, the more invoices processed, the more accurate the technology becomes. This ability to continuously self-improve is called the ‘data network effect’.
With cognitive data capture it is possible to do the same tasks, but in less time and with reduced errors. This means less double-checking and repeated tasks, leading to higher efficiency and productivity. Based on our user study, it can result in a 97% reduction of keystrokes and other manual actions from analysts.
4. Cost reduction
Since there is no need to configure rules or templates (compared to alternatives like template-based OCR), implementation costs and subsequently overall labor and operational costs will decrease significantly. Added with increases in system productivity and a reduction of errors, the actual costs for automation will yield significant returns in a short period of time.
5. Better process management
Al-powered invoice processing helps avoid late payments. By eliminating human error from the process, no invoice will be forgotten. Thus, no penalties for late payments, no frantic phone calls from suppliers, and no rushed invoices. This all leads to improved relationships with partners and better financial performance in the long run.
Furthermore, AP automation reduces the invoice approval cycle and allows dynamic discounting programs to increase annual return on investment by more than 20%.
Conclusion: The future of Artificial Intelligence in invoice data extraction
With the rise of robots in business, we are just at the dawn of the automation revolution. The financial services industry is one of the leading sectors in the future of Al. Companies that are investing more in automation will eventually become leaders in their sectors as compared to those that fail to invest enough.
All of the previously stated benefits of cognitive data capture in invoice extraction are symbiotic, not mutually exclusive. Time management, accuracy, productivity, and process improvement, all work together towards increasing your efficiency, and subsequently the bottom line. While the technology is still young, it will only continue to develop and improve within a widening scope of business activities.