Nowadays, businesses handle countless documents such as invoices, order documents, and reports. According to projections from IDC, 80% of worldwide data will be unstructured by 2025. Managing all of this manually can be slow, prone to human error, and inefficient. That’s where Intelligent Document Processing (IDP) comes in.
Using AI, IDP can automate the way documents are processed, making tasks like data extraction and classification faster and more accurate. In this post, we’ll explore the benefits of IDP, how it works, and why it’s transforming document management for companies everywhere.
The benefits of IDP
First things first: the benefits. Automating your document processing using IDP has many advantages that can boost your business and take it to the next level.
Security
A fully automated IDP process ensures that your sensitive documents are handled securely, without any unauthorized access. It can also be more easily adapted to regulations or guidelines within your company that reduce risks.
Efficiency
An IDP tool can automate, or partly automate, the very labour intensive task of manually processing documents. By extracting key value pairs automatically from a document to insert into a CRM system, you can fully automate the document processing flow. If needed, a Human-In-The-Loop (HITL) can be used to verify the outcome of the IDP tool. This would still speed up the processing since verifying something is faster than manually processing it. With a HITL it is also possible to improve your model over time using reinforcement learning.
Improved accuracy
IDP reduces human error in document processing. You can have extra validation by referencing the outcome of the IDP to your master data. Using reinforcement learning can improve the model even further.
Cost reduction & scalability
Saving time is saving money. IDP can scale together with your business. Currently, your company may only be processing a few dozen or perhaps a few hundred documents. Once your business grows, manually processing documents becomes a huge cost. When using IDP, automated document processing can easily be scaled to thousands of documents or even more.
How Intelligent Document Processing works
Intelligent Document Processing combines machine learning, AI and natural language processing (NLP) to extract key value pairs out of documents that are unstructured, semi-unstructured or fully structured. Let’s have a look at the flow:
- Capturing your data.
Collecting your document is naturally the first step of your automation. This can range from listening to a folder in a cloud storage system to listening to a mailbox. - Classification.
You should know what kind of document you are processing since you need to select a data extraction model. These models are expecting a specific kind of document. If you, for example, provide a transport order document to an invoice model, it will not behave as expected. This means classifying your documents is an essential step, and it can be automated using machine learning and AI as well. But it can also be as simple as having a specific mailbox for specific document types. - Data extraction.
In this step, the data gets extracted from a document. The document is first analysed with optical character recognition (OCR) and then processed using NLP. Usually pre-built models exist for general documents such as invoices, but some technologies allow you to train a custom model based on your own data as well. - Validation.
After the data is extracted, you should validate it. This can be done by a HITL if the confidence score does not pass a certain threshold. You can also compare the output to your master data and combine data from multiple sources. For example if you have a VAT number in your invoice, you can check if the company info matches the data with your master data. - Integration.
Once you have the data, it is easy to integrate it with your existing systems. Automatically integrating your data into a CRM or ERP system is the finishing touch.
Key features to discover
There are plenty of different tools handling data extraction for documents. So what are key differences that you need to consider? And how do you find the right match for your needs?
We narrowed down a list of key features to look out for:
- Pre-built models.
Pre-built models are amazing, since you don’t have to spend time training custom models. Classic document types, such as invoices, are usually supported over all platforms, but more specific types of documents, such as contracts, do not always have a pre-built model on all platforms. - Custom models.
The ability to train custom models is very powerful. Your company may have a specific kind of document, or specific fields that are not supported by out-of-the-box pre-built models. - Method of labeling data.
If a technology provides the ability to train custom models, the next question is: how easy is it to train your own model? Some technologies require labeled training data, which involves selecting your words and assigning a field name to them. Others may work more like ChatGPT and require a specific prompt. In that case, you also want to know which languages are supported for both the prompt and the document. On top of that, with manually labeling documents you should know the amount of documents you need to label before you can train a custom model. - Accuracy.
It is hard to know the accuracy of a model without testing. We would always recommend trying out pre-built models with your own dataset, especially if you’re using another language than English. It is also worth training a custom model and testing it if you have a more niche document type.
But keep in mind, this is not all. There are more differences between technologies. Think of available API’s, pricing, limitations on types of files…, but these are – according to us and our experience – the biggest differences between the tools that are currently on the market.
The importance of labeling data
It’s essential to have the correct training set or correct prompt when training a custom model. If you, for example, have a training set that includes transport order documents of only one of your customers and you try to use that model on another customer’s document, it will most likely disappoint you. Or if you have a prompt that is not specific enough, the result will probably not be what you hoped for.
That’s why people who know how to train a custom model are key to successfully integrating IDP into your business.
TL;DR
… or Intelligent Document Processing in a nutshell
Intelligent Document Processing (IDP) uses AI to automate document handling, such as invoices and reports. It improves efficiency, accuracy, and scalability, helping businesses save time and money while ensuring security. IDP processes documents by capturing, classifying, extracting, and validating data before integrating it with existing systems like CRMs or ERPs. Key features to consider when choosing an IDP tool include pre-built models, custom model training, ease of labeling data, and accuracy. These features help businesses process documents more efficiently, even as they grow.
