Purchase Order vs. Invoice: A Deep Dive on Similarities and Differences
Get a comprehensive overview of a purchase order vs. invoice, including when businesses use each, what information goes in them, and more.
Your business purchases goods and services from multiple vendors and receives their invoices. Unfortunately, most supplier invoices are designed for people and not software systems, posing several problems to your business operations and decision-making.
In this article, you’ll learn about the challenges of manual and template-based invoice processing and the tremendous advantages of automated invoice data capture over them. You’ll get to know all the features of Width.ai’s invoice data capture software and find out how your business can deploy our software in just six easy steps.
Manual processing of invoices involves manual data entry into your accounting system by your internal invoice team, checking it for problems, confirming details with other departments, and reviewing by senior accounting roles or by management. This standard process comes with a number of downfalls seen to cause longer terms problems for small businesses.
Given these problems with manual processing, most businesses use semi-automated invoice capture that combines invoice digitization, optical character recognition (OCR), and invoice templates.
Because invoices vary so widely in layouts and positions, you need a configurable way to make the software associate the text in an invoice with suitable invoice fields. Invoice templates solve this to an extent. An invoice template is a set of positional information and parsing rules that enable the software to transform the unstructured text from an invoice into structured data if that invoice matches that template. Some invoicing applications provide visual editors to create these templates easily.
Unfortunately, this approach also suffers several challenges that can’t be solved but are merely ameliorated using custom post-processing software and frequent manual intervention.
Given the enormous variance in invoice layouts out there, the template approach just doesn’t scale well beyond a point. Businesses that get hundreds of invoices from different vendors daily face scaling challenges:
The accuracy of template and OCR based approaches does not reach an accuracy level that fits business use cases when you grow the number of templates.
Invoices in the real world can contain lots of noise that confuse the template approach:
Even something as simple as moving the invoice date field around the key can create problems in template based processing. These OCR and template architectures do not learn a relationship between the key and the field which makes it very difficult to map these two together when the proximity and order changes.
The template approach does not allow for a deep understanding of the relationships between the invoice text, positions, and fields. As a result, follow-up tasks that require such understanding, like invoice matching, also suffer from correctness problems.
Older OCR approaches use only visual features like contours for text recognition. As a result, typos and wrong numerical values are common. This problem is worse when processing paper invoices and handwritten text.
Due to the unpredictable nature of these errors, businesses have to recheck everything manually and are forced to employ support staff in this approach too.
These semi-automated approaches are not intelligent enough to automate mandatory practices like mapping invoice items to general ledger codes.
Because they lack semantic understanding of the data, such approaches cannot match purchase orders, invoices, and receipts reliably. Manual intervention is necessary.
The semi-automated approaches reduce some expenses compared to manual processing. But those savings are lost again when hiring support staff for other tasks like template creation and manual verification of values.
Semi-automated approaches improve the chances of detecting mistakes and fraud across invoices. But only slightly, because they don’t understand the invoices semantically and can’t match data across invoices as people can.
Lacking any semantic understanding of invoices, semi-automated approaches just can’t support a high level of customization beyond invoice templates and some settings.
What is the state of invoice processing out there? Consider these alarming statistics:
Fully automated invoice data capture is the solution to all these challenges. From the same study, companies with fully automated invoicing “were 63% more likely than average to feel very confident in their fraud prevention” and 72% of finance leaders said, “it would make their companies less susceptible to ransomware attacks and other issues.”
Fully automated invoice data capture uses machine learning to accurately identify invoice elements such as prices, names, quantities, products, and so on in any invoice regardless of its layout and other specifics.
It extracts that information as structured data that can be sent to downstream systems like enterprise resource planning (ERP), customer relationship management (CRM), and internal databases. Automated invoice data capture brings tremendous benefits to your business.
Unlike both manual and template-based approaches that impose high employment and overtime costs, automated invoice capture software requires minimal support staff and human intervention. Over time, even that reduces as the software automatically learns your invoicing needs. Costs on other downstream tasks are also reduced.
Automation brings a very high ROI. It reduces labor costs for invoicing by a third. Plus, 74% of chief financial officers CFOs at firms with annual revenues between $1.5 billion and $2 billion felt that digitization improved their balance sheets.
Unlike traditional OCR, this approach combines visual features and natural language models using deep learning for true entity understanding. It can correctly identify invoice fields, amounts, quantities, and dates based on context and values. It is largely immune to typos and misidentification of characters. The high data accuracy brings significant downstream benefits to your business:
Semantic understanding, high accuracy, and automated invoice matching enable real-time detection of any incorrect entries, duplicate entries, and deliberate fraud in the invoices. This is something even manual processing by experts cannot do efficiently. This benefits your business by avoiding financial losses due to fraud. You’ll also avoid problems and penalties during financial audits, which are particularly problematic for your business reputation if you’re a publicly listed company or undergoing due diligence for an acquisition.
Instead of relying on rigid rules and template matching like template-based approaches, this approach understands the semantics of invoice data the way people do. It can handle invoices having any layout, lighting conditions, formatting, and other variations without requiring any invoice templates at all. As a business:
Automated invoice capture that matches human-level understanding can streamline every step of the process. It can accurately extract invoice data from even the most complex invoices within seconds. They significantly reduce time, effort, and money compared to manual data entry and double-checking.
Semantic capturing of invoice data enables you to automate many of your downstream business practices too:
These teams are critical to your data-driven decision-making. With automated invoice capture, they can get up-to-date cash flow, lifetime value, and the financial state of your business in real-time at any time. In one study, 95% of CFOs said automation played a very important role in maintaining healthy balance sheets. Moreover, unlike manual or template-based approaches, this information is both reliable and up-to-date. It can help you make decisions about pricing, vendor contracts, and deals based on the latest reliable data.
Width.ai’s automated invoice data capture software brings all the above benefits to your business and comes with compelling features.
The software uses state-of-the-art deep learning to understand invoices the way people do. This deep, human-like, semantic understanding of invoices enables it to process any invoice layout and extract data accurately. We’ve used the same underlying deep learning pipeline for information extraction in legal document cover sheets and supported over 50+ different layouts.
Our software provides fully automated, hands-off invoice data capture that automatically fetches invoices from multiple sources, processes them in bulk, extracts their data accurately with minimal human monitoring, and exports the data to multiple formats or systems. Your invoice can go from uploaded to processed with extracted data in a matter of seconds.
Our software processes even complex multi-page invoices with handwritten text in seconds with astonishing accuracy. It can process millions of invoices every day.
If you have an archive of historical invoices (even handwritten or typewritten ones), we can provision additional cloud resources to process them quickly. Historical invoices can help your business intelligence teams detect long-term trends.
Our software supports a large number of invoice formats:
The software supports automatically pushing the grabbed invoice data to your ERP accounting systems like SAP FICO, CRM, database, or email.
You can set up custom export workflows that select invoices using custom criteria (like invoice amounts or vendor names) and export the extracted data to multiple destinations or approval workflows.
The entire pipeline is fully customizable. It can be fine-tuned for your specific invoices to further increase accuracy.
In addition to invoices, our software also understands purchase orders and receipts semantically. It can do three-way matching between purchase orders, invoices, and receipts automatically.
Our software supports routing invoices for management approval based on invoice amounts, vendor names, or other criteria.
Our software builds a deep understanding of the relationships between invoice text, positions, semantics, and extracted fields. It can identify fields and values accurately even when field labels are missing. It has built-in support for over 50+ common invoice fields.
In addition to the common fields, your business may want to extract other important information from invoices. For example:
Our software has features like language comprehension and post-processing actions to handle such special needs. For example, its natural language capabilities enable it to identify a sentence containing payment terms and file it against the payment terms field.
Our software supports over 50 of the most common invoice fields right out of the box. But it also allows you to add the custom fields you want. We do all the fine-tuning necessary to add these new fields to your pipeline.
The custom fields we have added using these techniques include:
Customizations done on one vendor's invoice can be applied to all invoices, or a subset of invoices, across any vendor. Our system automatically searches for semantically similar information in every invoice (including old invoices) to populate the correct fields.
Our system can send status and progress alerts to your staff through:
Your business can start using our feature-rich invoice software in just six easy steps.
For a seamless transition of your business processes to our invoice processing software, we first assess aspects of your current invoicing workflows, like:
These details help us plan the integration and deployment of our software for your business. For example:
With the assessment done and a plan ready, the next step is configuring the software to handle your invoice sources and formats.
For invoices from FreshBooks, QuickBooks, Zoho Books, Xero, Pilot, or SAP FI, add them as sources and provide your authentication credentials.
Connect your s3 storage system to automatically process invoices.
For invoices received as email attachments, configure the software with authentication credentials and monitoring intervals for relevant mailboxes. The software periodically fetches emails and processes any attached invoice that’s in a supported format.
Paper and Handwritten Invoices
For paper invoices received by mail or fax, set up an invoice scanning pipeline using scanners or smartphone cameras to digitize them to PDF or image formats. The software can handle low resolution and noisy photos too. Store the digitized files in your internal network storage or cloud storage like AWS S3. The software automatically fetches new files from those locations periodically and processes them.
The ability to understand any kind of invoice layout, extract data accurately, and add custom fields are all indispensable capabilities of an excellent invoice data capture software, and ours has all of them.
Right out of the box, our system understands every invoice layout produced by popular invoicing software like FreshBooks & Quickbooks. Our built-in models also understand a wide variety of other layouts with common invoice fields, but your business may receive unique invoices or need custom fields. For such cases, we fine-tune our built-in deep learning models on your actual invoice and receipt samples. This is a big deal as it enables us to fully customize our state-of-the-art architecture into a customer-specific pipeline for your unique business requirements.
Our software can export the captured invoice data to a variety of formats, storage locations, third-party software, and external workflows. You can set up custom workflows that select invoices based on custom criteria and export their extracted data to multiple destinations or approval workflows.
Once you have configured the software and fine-tuned it, it's ready to capture your invoice data in bulk with minimal manual intervention.
Under the hood, the software uses a state-of-the-art deep learning architecture. It’s trained on thousands of invoices from popular invoice management systems like FreshBooks and QuickBooks. The training enables it to detect text, recognize the characters, and map invoice elements to appropriate fields. To do this, the system learns from the visual and linguistic characteristics of these elements, like:
We clone our latest model and fine-tune it on the invoices you provide in the third step to get a model that's customized to your invoices. Your preferences, like custom fields, help us refine this custom model even more to identify the exact data you want accurately.
This model scans each invoice for visual and linguistic characteristics. The combinations of characteristics help it identify an element as an address, a purchase order, an invoice number, and so on.
The results of this phase are a set of fields and their values for each invoice. This extracted data is forwarded to export pipelines for generating reports in various formats or for export to an accounting system.
Metrics like accuracy, precision, recall, and F1 scores enable you to evaluate the effectiveness of your fine-tuning. Confidence scores for each processed invoice enable you to pinpoint problematic invoices and fine-tune the software on them.
Alerts enable your employees to monitor progress in real-time and to know about any problems that crop up during processing. Our software supports alerting through email, Slack, Jira, your CRM, and PagerDuty.
You have seen how our invoice data extraction software brings an array of incredible benefits and features to your business. For any other special requirements, we bring expertise in developing accurate information extraction systems using the latest artificial intelligence and deep learning innovations. We can customize our invoice capture software to your specific business requirements and use cases. Contact us for a demo of our invoice data capture software.
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