by
June 24, 2022
A large percentage of invoices are still being processed manually inducing a lot more cost that can be minimized. The blog focuses on the different costs related to invoice extraction, and possible invoice extraction automation remedies…
As reported by Billentis, in 2019 alone, the amount of invoices generated throughout the globe were approximately 550 billion in volume. Out of this, only 55 billion were paperless which meant the remaining 90% required some level of manual intervention.
Invoice data extraction is a common denominator between processing and approving invoices in logistics. It is essentially done to maintain healthy financial transactions and records, therefore, making it necessary for organizations to focus on it. While the weight of invoice extraction trumps multiple other ongoing processes, industries like logistics today are still seeking cost-effective ways to automate data extraction and verification of invoices.
Manual invoice extraction is the task of extracting data from an invoice with the help of a human data-entry operator. The operator would scan the entire invoice, pull out usable data, verify it with the existing database, and make entries into the final ledger for record keeping and further processing.
Any document including invoices comes from different geographies which includes multiple types such as commercial invoice, pro forma invoice, credit invoice, etc. It is especially the case with logistics.
It gives rise to multiple challenges of processing (data extraction and verification) different fonts, multiple formats, varied structures, tabular data, and even custom data entries, all at the same time. It makes the data to be processed a lot less discernible because it is unstructured. It is for the same reason, traditional data extraction systems can’t tackle these challenges as they don’t have the capacity to distinguish data like humans. On the contrary, with modern-day cognitive platforms like intelligent document processing, it is achievable and can be automated.
Invoice extraction is a critical part that helps accounts payable to function properly. Accounts payable requires data from invoices at each level to further process payments and save money with timely operations. However, it is not the only task that needs attention. It leads multiple companies dealing with heavy document processing to outsource invoice extraction to a third party or hire a dedicated team of data-entry specialists.
As per a 2020 report by the U.S. Bureau of labor statistics, hourly wages of a data entry operator ranges from $11.77 to $24.25 for multiple industries including logistics. Also, the wage rate varies depending upon the state. For example, New York's average hourly rate is $19.40 while it is $18.98 for New Jersey.
Even for organizations that hire freelancers, the cost of hiring a data-entry specialist can range from $10-$20 per hour (as suggested by Upwork).
If we aggregate the entire cost of hiring a data entry operator, the annual cost for a single employee can range from $26,000 to $50,000 annually. It means a team of ten data-entry specialists will cost between $260k to $500K annually.
The entire cost of running this operation is a lot higher than deploying an automation solution that can take up the entire invoice data extraction and verification work. It ends up creating an alarming situation for organizations that are heavily reliant on document processing.
Excluding the direct labor cost, there are a multiple additional costs that are often left out in the statistics such as:
Plenty of organizations today are still using paper-based invoices at massive scale which requires large quantities of paper. This cost of purchasing the paper for invoices and even printing data on it comes from the organization’s funds. Besides this, it also contradicts the “Go Green” motto that is rampant on a global scale and should be supported.
Physical documents require folders, files, and cabinets for storage. Paper-based invoices are stored using these storage units overburdening organizations with its additional cost. These units also require storage space inside the premises which can be used by any other department. On the contrary with digitization, the data can be stored on cloud completely safe from any accidents such as fire.
Manual data extraction and validation is prone to human errors. It is a substantial problem because of which many organizations are automating manual data entry for zero errors. To avoid erroneous entries into the general ledger, regular audits to verify existing documents are performed that induce both time and cost to the organization.
Suppliers offer discounts to organizations for processing early payments within a time span. Since manual invoice extraction is time consuming, it often ends up adding more time to the entire operation. This leads to a loss of opportunity of early payment discounts that are often chased by accounts payable.
Traditional OCRs work on a template based model. These templates are either based on Zonal OCR that looks for specific locations on the page or a rule-based OCR configured with a sequence of if-else scenarios. They are faster than manual extraction but have limitations.
For example, the OCR templates are based on 70% logistics industry templates and for the rest, they can’t be implemented. The installation takes a day. The model also needs to be trained regularly. It is an expensive system and even with the processed documents, it requires some level of manual intervention because it is prone to errors.
These are documentation solutions developed for a specific industry. Companies like Capsys, Ikarus, Mitek, etc. provide these solutions. It works for specific models and is tailored to automate document processing for a specific use case of the entire process.
Backed by advanced technologies like Artificial Intelligence, Machine Learning, Computer Vision, Natural Language Processing, etc these solutions are often industry agnostic. These kinds of platforms are ideal for heavy document-driven organizations with data ranging from structured to unstructured. They can segregate, sort, extract, validate, and process data from all invoice formats automatically.
VisionERA is a cloud-native IDP platform that is built as a cognitive solution which learns and evolves with each iteration. It helps in digitization of data but has the capability to process a wide range of unstructured documents from scanned paper-based sources and digital sources. The platform seamlessly integrates with an organization's existing infrastructure and is compatible with downstream applications like CRM, CMS, legacy application, emails, etc.
It is an industry agnostic cost intensive platform that can be applied on a wide array of document related operations for data extraction and validation. The platform allows custom workflows along with applicable business rules and a smart insights board where our clients get useful KPIs for process optimization. With VisionERA, our clients have not only reduced the turnaround time for the entire workload but enhanced productivity with reduced cost of operations.
Invoice extraction is one of many processes in an organization that demands aggressive costs to successfully run. With automation, organizations have started to transform themselves and are adopting cost optimizing solutions.
A state of the art IDP platform can process invoices in bulk and is free from human errors. It is a platform that not only brings automation to processes but liberates company’s resources and assets from redundant and repetitive tasks.
Hence, it is important that companies begin to realize the potential of automation and evolve each of their processes to run parallel to their short-term & long-term goals. For further information on how your logistics business can achieve cost efficiency while automating the mundane paper-work tasks, speak with our automation experts now.