A Deep Guide to Text-Guided Open-Vocabulary Segmentation
Discover the power of text-guided open-vocabulary segmentation using large language models like GPT-4 & ChatGPT for automating image and video processing tasks.
Invoice matching is a critical prerequisite in the B2B payment process but it’s hampered by slow manual processes. Many accounting professionals have a strong desire to improve the process — as many as 43.8% would like automated matching implemented.
In this article, you’ll understand the basics of invoice matching, different ways of implementing it, and its importance to a business. You’ll learn about the challenges of doing it manually and the benefits of fully automating it. Finally, you’ll see how fully automated invoice matching works under the hood.
Matching invoices is an important accounting task that compares the information in four types of documents to verify that they are all consistent with each other. The documents are:
When the accounting department receives a supplier invoice, the accounts payable (AP) team is responsible for matching invoice details against the details in the purchase order.
Some companies also match both against supporting documents like the goods receipt and inspection report, especially if the goods have high value or are supposed to meet stringent quality criteria.
If there’s a mismatch in the details of any two documents, it’s called a “match exception” or “deviation.” Mismatches can be of two types:
In the real world, there may always be tiny discrepancies in price or quantity. Prices may deviate due to taxes or currency exchange rates. Quantities may deviate due to unexpected damage or transport mishaps. In a shipment of 1,000 items, if 995 are fine and five are damaged, it may not be a wise choice for a supply chain to reject all 1,000 items.
To cater to these realities and avoid payment delays, businesses define some tolerances. If the deviations are within those tolerances, the documents being compared are treated as a successful match, and payment is approved. If the deviations are above the tolerance levels, the invoice is put on hold and sent back to the vendor for replacement, clarification, or rectification.
Let’s explore the different ways these documents are matched.
Depending on your business practices, industry, and local laws, there are four ways to select and match these documents with each other.
In two-way matching, the AP department verifies the vendor’s invoice against the corresponding purchase order. The items, prices, and quantities in the invoice should match those in the purchase order within acceptable tolerances.
Such PO matching is preferred when receipts and inspections are impractical or cost more than the goods themselves. It’s suitable for:
In three-way matching, accounts payable team compares the details in three separate documents:
This is the most popular method in most industries.
Four-way matching is like three-way matching but includes a fourth document in the comparison: the inspection report by the accepting department. The accepting department inspects the goods for quality and conformity to requirements. Only accepted goods are cleared for payment.
This method is ideal for industries like automotive and manufacturing where quality control is critical.
Some transactions, especially involving services, use contracts instead of purchase orders. The contract specifies the services, deliverables, metrics, and payment terms that have been agreed upon between the parties.
Because the information in a contract tends to be domain-specific and unstructured, it can’t be matched with an invoice mechanically. Instead, AP typically consults with the receiving department to understand the extent to which each term has been fulfilled and checks if the vendor has billed them correctly.
If the vendor has charged for a service that hasn’t been provided or fulfilled satisfactorily, the invoice is put on hold and sent back to the vendor for clarification.
Invoice matching, when done accurately, benefits many of your business practices.
First, invoice matching is recommended by accounting standards like the Generally Accepted Accounting Practices and the International Financial Reporting Standards. Your business will be compliant with all financial auditing and reporting requirements.
Second, invoice matching helps you keep an eye on your bottom line and avoid financial losses from invoicing mistakes or fraud.
And finally, invoice matching reduces the risks of legal actions or penalties from vendors.
Invoice matching remains a largely manual process because until recently, automated approaches have not been very reliable or accurate. Older automated approaches have problems understanding the wide variety of layouts and information in accounting documents like invoices and purchase orders. Companies are forced to rely on manual matching.
Unfortunately, doing it manually has challenges that cancel out many of the benefits of invoice matching:
Fully automated invoice matching combines deep learning, computer vision, and natural language processing to understand invoices and other documents the same way people do regardless of variations in layouts, terminologies, languages, or other aspects.
Older semi-automated workflows involve some manual steps like creating invoice templates for data extraction, entering data in spreadsheets before the automated steps take over, or verifying the extracted data after the automated steps.
Unlike these workflows, fully automated invoice matching is done by machines with minimal manual intervention. Let’s understand all the benefits of such AP automation.
Fully automated invoice matching replicates the deep semantic understanding of documents that people have. This allows accurate semantic matching of information across documents if there are variations in headings, words, terminologies, languages, currency formats, or date formats.
For example, if there are special conditions in both the purchase order and invoice, it can interpret those conditions just like people can and check if the items in an invoice match those conditions.
Even 4-way matching can be done in a matter of seconds compared to the hours (or even days!) that manual matching requires. The entire process is highly scalable because it’s automated end-to-end and supports millions of invoices per day.
Fully automated invoice matching lowers both your capital and operating expenditures. The capital expenditure on the invoice matching software and infrastructure is a lot lower than that required for hiring and training people. The operating expenditure on cloud infrastructure is far lower than ongoing employment costs. The cost per invoice also reduces by about 75%.
The accuracies of both the data extraction process and the semantic matching of information across documents are extremely high compared to both manual and older semi-automated approaches.
Unlike people, fully automated invoice matching can sift through all your historical invoices in seconds. This enables it to detect overpayments, duplicate payments, and other kinds of vendor fraud that are spread out over time.
Aside from vendor fraud, some medium and large businesses face insider fraud where employees collude with vendors for mutual benefits. Dishonest employees may cooperate with fraudulent vendors to tamper with purchase orders, hide overpayments, or allow duplicate payments. Fully automated invoice matching helps detect them.
The reliability and accuracy of the matching bestow a high degree of confidence in its results. Your financial audits are guaranteed to go far more smoothly with such accurate results.
The ability of fully automated invoice matching to complete even 4-way matching within seconds enables your business intelligence teams to get accurate cash flow and expense data in real-time.
A fast, accurate, and deep understanding of invoices enables you to avoid payment delays and fulfill all payment criteria of vendors. You may even receive early payment discounts from vendors. Accounts payable invoice matching strengthens your relationships with your suppliers.
Let’s understand some of the state-of-the-art techniques under the hood of fully automated invoice matching.
The first step involves accurate, deep, and semantic data extraction from all the relevant documents like invoices, purchase orders, goods received notes, and inspection reports.
This is achieved by combining deep learning, large language models, and text recognition models to identify and understand both printed and handwritten text just like people do.
Regardless of layout or other aspects, all the data in these documents are converted to structured data consisting of fields and their values. For example:
Fully automated invoice matching is capable of identifying tables, columns, rows, and cells across multi-page documents. Each invoice line item in a table is extracted and labeled based on its respective column.
Matching the products or services across invoices, purchase orders, and other documents can get complicated because:
Fully automated invoice matching uses intelligent data matching based on deep learning and natural language processing to solve all these challenges. Product titles, descriptions, and data like SKU numbers are matched intelligently using state-of-the-art deep large language models like GPT-3 to achieve human-level understanding.
Quantities and prices are matched regardless of currency and number localization. The tolerance levels for matching can be checked through price outlier detection techniques.
Payment and contract terms are typically written in natural language sentences that don’t conform to any strict syntax. While they can be very challenging for semi-automated approaches, fully automated invoice matching extracts and understands them effortlessly using large language models.
In this article, you saw how invoice matching benefits your business but how manual matching cancels some of those benefits. By using Width.ai’s fully automated invoice processing solution, you can process millions of invoices, matching them within seconds. Contact us to see a demo of our touchless invoice processing.
Discover the power of text-guided open-vocabulary segmentation using large language models like GPT-4 & ChatGPT for automating image and video processing tasks.
Learn how CLIPSeg segmentation, in combination with GPT-4 and ChatGPT, can enable diverse applications from medical image diagnosis to remote sensing.
Can GPT-4 make your life as a finance or banking employee easier? Learn how GPT-4 and NLP can be used in finance to increase revenues and streamline workflows.
A deep dive into how we reached SOTA accuracy in product similarity matching through a custom fine-tuning pipeline that refines the CLIP model for image similarity.
Boost your conversions and sales numbers with NLP in sales using OpenAI's GPT-3 and GPT-4. You can use chatbots to improve customer experience and loyalty.
Explore the use of GPT for opinion summarization through innovative pipeline methods, evaluation metrics like ROUGE and BERTScore, and human evaluation insights. Dive into novel entailment-based evaluation tools for a comprehensive understanding of model performance in capturing diverse user opinions.
Come aboard the large language model revolution with our deep dive on AI21 vs. GPT-3 for business use cases like ad copy generation and math proof generation.
A technical guide to using BERT for extractive summarization on lectures that outperforms other NLP models
Discover how prompt based LLMs like GPT-3 & GPT-4 are transforming news summarization with its zero-shot capabilities and adaptability to specialized tasks like keyword-based summarization. Learn about the limitations of current evaluation metrics and the potential future directions in text summarization research.
Discover the PEZ method for learning hard prompts through optimization, a powerful technique that enhances generative models for image generation and language tasks, improves transferability, and enables few-shot learning
Take a look at how Width.ai built 17 generative ai pipelines for use in the Keap.com marketing copy generation product
A deep look at how recurrent feature reasoning outperforms other image inpainting methods for difficult use cases and popular datasets.
See a comparison of GPT-3 vs. GPT-J, a self-hosted, customizable, open-source transformer-based large language model you can use for your business workflows.
Discover how transformer networks are revolutionizing image and video segmentation, and get insights on modern semantic segmentation vs. instance segmentation.
Discover how the state-of-the-art mask-aware transformer produces visually stunning and semantically meaningful images and how it stacks up against Stable Diffusion & DALL-E for large-hole inpainting
Unlock the full potential of spaCy with this guide to building production-grade text classification pipelines for business data.
We compare 12 AI text summarization models through a series of tests to see how BART text summarization holds up against GPT-3, PEGASUS, and more.
Let’s take a look at what intent classification is in conversational ai and how you can build a GPT-3 intent classification model for conversational ai and chatbot pipelines.
Discover the capabilities of zero-shot object detection, which enables anyone to use a model out-of-the-box without any training and generate production-grade results.
What is facial expression recognition and what SOTA models are being used today in production
Get a simple TensorFlow facial recognition model up & running quickly with this tutorial aimed at using it in your personal spaces on smartphones & IoT devices.
Explore accurate classification algorithms using the latest innovations in deep learning, computer vision, and natural language processing.
Learn what human activity recognition means, how it works, and how it’s implemented in various industries using the latest advances in artificial intelligence.
What is the the SetFit architecture and how does it outperform GPT-3 and other few shot large language models
What is image classification and how we build production level TensorFlow image classification systems for recognizing various products on a retail shelf.
Explore the application of intelligent document processing (IDP) in different industries and dive in-depth on intelligent document pipelines.
How to build an image classification model in PyTorch with a real world use case. How you can perform product recognition with image classification
Let's build a custom CTA generator that you'll actually want to use for your website copy
We’re going to look at how we built a state of the art NLP pipeline for blended summarization and NER to process master service agreements (MDAs) that vary the outputs based on the input document and what is deemed important information.
Get a comprehensive overview of a purchase order vs. invoice, including when businesses use each, what information goes in them, and more.
Learn what Google Shopping categories are used for and how you can automate fitting products to this taxonomy using ai.
Automatically categorize your Shopify store products to the Shopify Product Taxonomy instantly with ai based PIM software
Dive deep into 3-way invoice matching, including how it works, eight benefits for your business, and the problems with doing it manually.
Smart farming using computer vision and deep learning provides the most promising path forward in the slow-moving industry of agriculture.
How we leveraged large language models to build a legal clause rewriting pipeline that generates stronger language and more clarity in legal clauses
Using ai for document information extraction to automate various parts of the loan process.
Apply AI to your favorite sport with this guide. Learn how automated ball tracking can change the game for coaches and players.
Categorize your ecommerce products to the 2021 google product taxonomy tree instantly with our Ai software
Surveying the current landscape of ecommerce automation and how you can use ai to automate huge chunks of your product management.
Classify your product data against an existing product category database or generate categories and tags in seconds using artificial intelligence
Warehouse automation plays a crucial role across your supply chain. Learn about how machine learning and ai software can be integrated into your warehouse automation stack.
4 different NLP methods of summarizing longer input text into different methods such as extractive, abstractive, and blended summarization
iscover an invoice OCR tool that will revolutionize the way you handle invoices. There’s no human intervention needed & a dramatically lower per-invoice cost.
Manual and template-based invoicing are riddled with low accuracy and required human intervention. Learn how to systematically eliminate these issues with the right invoice data capture software.
A complete walkthrough guide on how to use visual search in ecommerce stores to create more sales and real examples of companies already using it.
Automating the extraction of data from invoices can reduce the stress of your accountants by finding inaccuracies, digitizing paper invoices, and more.
How you can optimize email marketing campaigns with machine learning based models that improve conversion & click-through rates.
How you can use machine learning based data matching to compare data features in a scalable architecture for deduping, record merging, and operational efficiency
Learn how lifetime value or LTV prediction can improve your marketing strategies. Then, discover the best statistical & machine learning models for your predictions.
A deep understanding of how we use gpt-3 and other NLP processes to build flexible chatbot architectures that can handle negotiation, multiple conversation turns, and multiple sales tactics to increase conversions.
The popular HR company O.C. Tanner, which has been in business since 1927 and has over 1500 employees, was looking to research and design two GPT-3 software products to be used as internal tools with their clients. GPT-3 based products can be difficult to outline and design given the sheer lack of publicly available information around optimizing and improving these systems to a production level.
We’ll compare Tableau vs QlikView in terms of popularity, integrations, ease of use, performance, security, customization, and more.
With a context-aware recommender system, you can plan ways to recreate some of the contextual conditions that persuade them to buy more from you.
We’re going to walk through building a production level twitter sentiment analysis classifier using GPT-3 with the popular tweet dataset Sentiment140.
Find out how machine learning in medical imaging is transforming the healthcare world and making it more efficient with three use cases.
Discover ways that machine learning in health care informatics has become indispensable. Review the results of two case studies and consider two key challenges.
Accelerate your growth by pivoting key areas of your business to AI. Your business outcomes will be achieved quicker & you’ll see benefits you didn’t plan for.
We built a GPT-3 based software solution to automate raw data processing and data classification. Our model handles keyword extraction, named entity recognition, text classification | Case Study
We built a custom GPT-3 pipeline for key topic extraction for an asset management company that can be used across the financial domain | Case Study
How you can use GPT-3 to create higher order product categorization and product tagging from your ecommerce listings, and how you can create a powerful product taxonomy system with ai.
5 ways you can use product matching software in ecommerce to create real value that raises your sales metrics and improves your workflow operations.
Data mining and machine learning in cybersecurity enable businesses to ensure an acceptable level of data security 24/7 in highly dynamic IT environments. Learn how data security is getting increasingly automated.
Product recognition software has tremendous potential to improve your profits and slash your costs in your retail business. Find out just how useful it is.
Big data has evolved from hype to a crucial part of scaling your organization in every modern industry. Learn more about how big data is transforming organizations and providing business impacts.
Learn how natural language processing can benefit everybody involved in education from individual students and teachers to entire universities and mass testing agencies.
Here’s how automated data capture systems can benefit your business in some key ways and some real-life examples of what it looks like in practice.
Use these power ai and machine learning tools to create business intelligence in your marketing that pushes your business understanding and analytics past your competition.
We built a custom ML pipeline to automate information extraction and fine tuned it for the legal document domain.
In this practical guide, you'll get to know the principles, architectures, and technologies used for building a data lake implementation.
Find out how machine learning in biology is accelerating research and innovation in the areas of cancer treatment, medical devices, and more.
An enterprise data warehouse (EDW) is a repository of big data for an enterprise. It’s almost exclusive to business and houses a very specific type of data.
Dlib is a versatile and well-diffused facial recognition library, with perhaps an ideal balance of resource usage, accuracy and latency, suited for real-time face recognition in mobile app development. It's becoming a common and possibly even essential library in the facial recognition landscape, and, even in the face of more recent contenders, is a strong candidate for your computer vision and facial recognition or detection framework.
Learn how to utilize machine learning to get a higher customer retention rate with this step-by-step guide to a churn prediction model.
Machine learning algorithms are helping the oil and gas industry cut costs and improve efficiency. We'll show you how.
We’ll show you the difference between machine learning vs. data mining so you know how to implement them in your organization.
Here’s why you should use deep learning algorithms in your business, along with some real-world examples to help you see the potential.
Beam search is an algorithm used in many NLP and speech recognition models as a final decision making layer to choose the best output given target variables like maximum probability or next output character.
Best Place For was looking for an image recognition based software solution that could be used to detect and identify different food dishes, drinks, and menu items in images sourced from blogs and Instagram. The images would be pulled from restaurant locations on Instagram and different menu items would be identified in the images. This software solution has to be able to handle high and low quality images and still perform at the highest production level, while accounting for runtime as well as accuracy.
Deep learning recommendation system architectures make use of multiple simpler approaches in order to remediate the shortcomings of any single approach to extracting, transforming and vectorizing a large corpus of data into a useful recommendation for an end user.
Let's take a look at the architecture used to build neural collaborative filtering algorithms for recommendation systems
GPT-3 is one of the most versatile and transformative components that you can include in your framework, application or service. However, sensational headlines have obscured its wide range of capabilities since its launch. Let’s take a look at the ways that companies and researchers are achieving real-world results with GPT-3, and examine the untapped potential of this 'celebrity AI'.
How to get started with machine learning based dynamic pricing algorithms for price optimization and revenue management
Let's take a look at how you can use spaCy, a state of the art natural language processing tool, to build custom software tools for your business that increase ROI and give you data insights your competitors wish they had.
The landscape for AI in ecommerce has changed a lot recently. Some of the most popular products and approaches have been compromised or undermined in a very short time by a new global impetus for privacy reform, and by the way that the COVID-19 pandemic has transformed the nature of retail.
Extremely High ROI Computer Vision Applications Examples Across Different Industries
Building Data Capture Services To Collect High ROI Business Data With Machine Learning and AI
Software packages and Inventory Data tools that you definitely need for all automated warehouse solutions
Inventory automation with computer vision - how to use computer vision in online retail to automate backend inventory processes