Intelligent Document Processing (IDP) is one of the growing technologies that help businesses save time and money. This article will explain what IDP is and how it combines Artificial Intelligence, Natural Language Processing, and Machine Learning. We’ll also cover how Intelligent Document Processing improves business processes.
Despite its rising popularity, automation solutions remain a tech gimmick for most companies.
According to McKinsey, only 31% of global businesses have successfully automated at least one process as of 2020.
Any process, to be precise.
That means that the vast majority struggles with manual processes, letting their employees drown in tedious work instead of unleashing their full potential.
These processes vary from sales funnel optimization to marketing automation and accounting practices. Many of them also require lots of paperwork – the one aspect of work that we can all unanimously agree on is just life-draining.
If you’re as ecstatic about manual paperwork as Sheriff Woody, then (a) Intelligent Document Processing (IDP) should really pick your interest and (b) you’re in the right place to learn more about:
- What is Intelligent Document Processing (IDP)
- Benefits of implementing IDP software
- What is Automated Data Processing
What is IDP?
Every second, hundreds of documents – from invoices, receipts, and contracts to passports, IDs, financial analyses, etc. – are received, categorized, analyzed, and distributed worldwide.
Judging only by the astonishing number of emails sent every day – that is 361 billion by 2024 – handling paper and online documents is, whether you care about it or not, a fundamental part of conducting business.
That’s where IDP comes in handy.
IDP, which stands for Intelligent Document Processing, refers to a process of transforming unstructured data extracted from documents into structured and relevant information.
This process of transforming data can be called intelligent due to the technology that exists at the core of IDP – artificial intelligence, machine learning, NLP (Natural Language Processing), and computer vision. These techniques enable IDP platforms to properly recognize documents and perform tasks that may include information extraction, full-text search, page parsing, and more.
In even simpler terms, IDP helps businesses in the automation of finding, categorizing, and processing data from documents.
While Intelligent Document Processing doesn’t reduce paperwork by limiting the need to generate documents, it digitalizes them and helps immensely with processing them.
Furthermore, IDP helps turn various documents into structured sets of data, and streamline them into other integrated tools your company might be using already.
We will illustrate the application of document automation in an example of a medium-sized bank.
Case Study: Document automation in a medium-sized bank with growing complexity and number of processed documents
Background: A medium-sized bank with documents in several languages and manual processes
Depending on the target audience, a medium-sized banking company may have several departments – insurance, debt collection, and separate corporate and private customer branches. Our imaginary bank operates in several European countries and specializes in personal banking.
Problem to solve: Internal manual processes related to documents hold back cost reduction and acquisition of new clients
The company has been in the market for decades, but only now has the management decided to look into automation. Voices from the mid-management indicate concerns over time and resources wasted on manual consumer data processing.
On top of that, the bank has seen its market share reduced, losing to modern alternatives that capture the attention of the younger clientele.
Note: By the way, that wouldn’t be too different from conventional business conduct nowadays. In fact, a report from Billentis Market indicates that over 70% of international invoice processing is still paper-based.
The bank processes various documents, from loan agreements to account opening forms, credit forms, transfer requests, etc. The traditional way of catering to an individual customer would be to handle all paper documents manually in a branch.
Hours of inputting data into an internal system would follow each successful sale. During that time, another deal was lost to the competition.
Solution: Speeding up internal workflows with Intelligent Document Processing
The bank needs a more robust system to handle document workflows.
Clerks working in the branches should not spend hours on manual data inputs. Most paperwork should be digitized immediately, without needing to collect papers and only then digitize them.
Moreover, the bank needs to allocate its resources more efficiently.
Document automation can be a crucial piece of the puzzle. Employees of the branches will learn new skills and focus on upselling and adjusting the offer to attract new customers.
Automated document processing will help the bank mitigate risks associated with sunk costs and outdated processes. The direct business benefit of IDP implementation in the case of our bank would be more time to either close deals or upsell products, as well as increase the overall skillset of the company’s workforce.
Components of Intelligent Document Processing
Since IDP platforms are engines used to tackle various document-related challenges, it’s good to understand the essential gears that make this machine whirring.
You can think of these gears as components used to automate a particular workflow.
This section will explain the following concepts:
- Document Classification
- Page Parsing
- OCR and AI OCR
- Data Extraction
- Document Search
Document Classification & OCR
Most automated document workflows begin with document classification. The AI engine recognizes its elements when documents are uploaded to a platform thanks to Object Detection and Optical Character Recognition, and these two techniques mimic human vision. IDP software understands the difference between handwriting, printed text, stamps, and tables and determines the document type based on these elements.
Before we proceed with other components, let’s clarify doubts about the difference between IDP and OCR.
Difference between OCR and IDP
OCR, which stands for Optical Character Recognition, is often confused with Intelligent Document Processing.
What is the relationship between the two?
Is it the same thing?
From the technological point of view, OCR converts specific templates of images or scans of documents into machine-encoded text.
Traditional OCR systems are rule-based though, meaning the extraction happens within particular fields in a document template. Areas defined by the user need to be shared for all documents the OCR tool processes.
As an example, take a look at the invoice below.
The so-called legacy OCR tool would simply read text from the image, but that’s about that. There’s no structure in the extracted information – just a crude block of text that needs further formatting.
When the template of such invoice changes, a traditional OCR-based tool needs to be trained to extract data properly. A change could be anything related to the visual layout – the amount to be paid moved elsewhere, or the address moved to the other corner of the doc.
Rule-based OCR can, therefore, seem too limited for businesses that generate or process varying types of documents since every deviation from a set of used templates needs to be separately processed by the tool.
What it also means, in reality, is that your company would need to receive and create documents within strict formatting boundaries, and that’s seldom a case if you work with various contractors or branch out to different countries.
AI OCR is a different take on this image-to-text technology, where Artificial Intelligence supports the process and allows it to improve continuously. Furthermore, changes in document layout do not affect the results and quality of information extraction.
IDP solutions, such as Alphamoon, can take an invoice and extract data that can be streamlined into other integrated tools. Intelligent Document Processing is designed to help establish a more efficient process and provide you with a better grip over the content of your documents.
The task of dividing pages from a large PDF is most likely a nightmare that you’ve experienced on your own. Intelligent Document Processing platforms often need to correctly divide bulk scans of documents before proceeding with OCR and data extraction.
Data extraction is one of the critical components of any IDP tool.
Based on the annotations made by users, this component enables IDP software to find relevant pieces of information in a document. While OCR scans and understands the document’s visual aspect, data extraction mimics the human skill of reasoning and cognitive perception.
Data extraction provides a summary of records consisting of numbers and texts derived from the document.
Such as the one below:
Let’s look at an example of automated invoice processing to understand the data extraction feature better.
A well-trained, AI-based software can extract data from invoices in seconds and indicate any potential errors or faulty parts. The best data extraction tools can yield over 98% accuracy, dramatically reducing the time needed to approve a single invoice.
Records extracted from invoices are often the same, but the trick is the lack of a single, universal template.
The automation of invoice processing can help achieve cost reduction and shift the accounting team’s focus to more complex and valuable challenges business-wise. Accountants, who often tremble whenever the word – automation – appears, are one of the groups IDP will heavily influence in the near future. Digitalized document processing and powerful AI-based financial forecasting tools will make their professions significantly more business-oriented and value-oriented.
Note: See below for a comparison of data extraction tools – Alphamoon, ABBYY, Microsoft, Google, and Kofax.
Machine Learning, Natural Language Processing, and Computer Vision
We’ve also mentioned that IDP tools perform complex analysis thanks to the combination of Artificial Intelligence, Machine Learning, Natural Language Processing, and Computer Vision.
Now, what do these techniques mean in the context of IDP?
Let’s start with Natural Language Processing. You may know that AI models are algorithms that – even when processing pieces of text – analyze variables as numbers.
Here’s an example.
By working on BoW (Bag of Words), the algorithm learns the meaning of each part of a sentence below.
The more sentences including the word “sentence” the model processes, the higher the chance it will be ready to extract this exact word from any text. The application of this simple model is, however, only the baseline.
More robust engines use Deep Learning and teach the models on neural embeddings. Neural embeddings enable the text analysis to go beyond extracting one word. But instead, the algorithm can create connections between words. You can see a few examples of neural embeddings in the image below.
Computer Vision, on the other hand, supports the ability of the algorithm to understand the visual context. Think of it as a way to mimic the human capacity to see. That’s the missing link between receiving a document and transforming its visual form into text.
Alphamoon’s IDP platform deploys all these techniques.
What are the benefits of the implementation of Intelligent Document Processing?
All of the above gives you an overview of what to expect from Intelligent Document Processing.
We’ve covered the details for the tech-savvy and the general ideas of workflows that IDP platforms automate.
Now, let’s dig deeper into the business benefits of IDP implementation. We will explain the following benefits:
- Time savings
- Fewer human-made errors in the process
- Positive ROI of the investment in document automation in the long-run perspective
- Simplified internal communication and collaboration
- Increased customer satisfaction
1. IDP saves time for you and your employees.
A study by SnapLogic indicated that 90% of employees are burdened with repetitive tasks that could be automated. Moreover, that causes the losses equal to 19 working days per employee in a year.
90% of employees.
19 working days.
Instead of time-consuming paperwork and document classification, your Accounting or Finance Team should focus on tasks that generate more value for the whole business, while the document workflows are automated. Think of invoices processing using AI, where the Accounts Payables team can increase their capability in document processing if data extraction and manual entry into the ERP system is automated.
In other words, the same amount of employees can significantly improve their efficiency thanks to document automation.
While we’re at it, let’s break down the popular myth that the by-product of automation translates to potential lay-offs. In other words – do robots steal jobs from humans?
The main fear concerning automation is that automating processes removes people from the equation. When IDP decreases costs, it doesn’t necessarily refer to reducing people on board. Savings can be found in a fewer of errors in the document-related processes, as well as the shift in the team’s focus. The existing team’s given a better shot at wiser time management and generating crucial value thanks to the better allocation of resources.
Instead of repetitive tasks, your teams can deliver leaner solutions to existing processes and support other departments with more knowledge sharing.
Let’s consider an example of a company where finalizing a deal takes months – from the first negotiations to the implementation phase.
To process the necessary documentation, the company needs to dedicate the working hours of its employees who will analyze each page and prepare a thorough examination before the deal is concluded. With no automation set up, that task takes weeks.
However, if the analysis were automated, then the company benefits from two things:
- faster turnover of documents which maximizes the potential number of deals to be processed at a given time
- increased revenue thanks to the simple relation (ceteris paribus stands) more offers sent = more deals closed
More time, more revenue, and an automated process that saves people from frustration.
2. Having the support of AI means fewer human-made errors because the tool learns with time.
People are prone to noticing ups and downs in terms of their ability to focus. No employee’s productive at all times. You name it: a rough night, harsh weather conditions on the way to work, mood swings, personal problems. The canvass of factors that directly impact productivity is a large one.
Obviously, automation solutions aren’t affected by such factors.
Each AI model learns on the input from your documents, thus increasing its accuracy. Since the main economic principle for any business owner or process owner is to maximize output by minimizing intake, document automation provides you with more structured data and support for your decision-making process in the long-term perspective.
It’s easier for your employees to make the right call when they’re not tired by a dull routine.
3. From the investment perspective, your ROI increases after the AI teaches itself the specifics of documents your company processes. Also, your offer generation speeds up.
You no longer need to grow the team responsible for processing documents, while the existing employees can take on new challenges. Even when the company’s operation expands, automated document workflows reduce the need to hire more headcount, thus allowing you to maintain a more efficient, less costly hiring strategy.
Also, software for document automation adjusts to your needs – like the option to either calibrate the extraction accuracy based solely on your input or the input of external docs.
When assessing IDP from the business investment perspective, there’s also the benefit of faster offer generation thanks to quicker document turnaround. With automation in place, your team can quickly process emails from prospects and source data to create ready-to-send offers.
4. Another critical benefit: simplified internal document communication.
The above may sound like a headache for the biggest fish in the pond. However, even mid-market businesses employ various ERP and CRM systems that generate documents.
IDP does the heavy lifting of transforming data from a plethora of multiple documents and streamlining that data into a standard format. All team members will be looking at data processed in a standardized manner.
5. Increasing customer satisfaction.
While we’ve been focusing mostly on invoices so far, there are other types of documents that can vastly improve other processes, i.e. complaints processing.
By establishing specific templates for your complaints, IDP can help quickly extract critical data from complaint forms and boost the performance of your Customer Success team.
And hey, quicker responses and better quality of service will likely provide higher eNPS.
Size of the IDP market
It’s also worth looking at how the technology of document automation expands.
If numbers speak to you better, consider the size of the market that reflects the possibilities created by IDP tools.
Intelligent Document Processing as a market is predicted to achieve US$ 6.78 billion by 2027. The global e-invoicing market reached US$ 8.74 Billion in 2021 and is estimated by IMARC Group to be valued at US$ 29.68 billion by 2027. Note that these markets are expected to achieve CAGR – compound annual growth rate – at over 20%.
Last but not least, the same McKinsey report concerning automation indicated that the trend of leaning toward automation is on the rise, with 66% of businesses piloting solutions to implement (as of 2020).
Each year, new developments in NLP and AI – such as the Minerva model by Google – open new doors for IDP vendors.
Automated Data Processing (ADP) extends IDP
While intelligent document processing aims at extracting data from documents, AI-based technologies can also automatically process this extracted data, thus supporting many key processes where accurate data plays a pivotal role.
Automated data processing encapsulates all technologies that utilize machine learning and artificial intelligence to extract, store, process, analyze and streamline data.
IDP and ADP are tightly linked together.
By implementing both of these automations, your company benefits from documents on the highest possible level. Your data extraction achieves top-quality accuracy, can be transferred to other integrated tools, and then processed and put together as reports that support your decisions.
Note: Want to know how document automation will grow and transform? Read the IDP trends compiled by the founders of Alphamoon.
Intelligent Document Processing in a nutshell: a quick recap
Let’s recap the whole topic of Intelligent Document Processing.
- IDP refers to a process of transforming unstructured data extracted from documents into structured and relevant information. This process incorporates Artificial Intelligence, Machine Learning, NLP (Natural Language Processing), and Computer Vision.
- An IDP platform is built with components that address specific problems – page parsing, classification, data extraction, scanning, etc. These features can be bulked into solutions that automate particular use cases (document workflows).
- Contrary to traditional OCR tools, which simply “read” text from documents, IDP empowers companies to optimize their existing workflows and gain knowledge from digitized records.
- Implementing IDP brings about many business benefits, including reduction of costs, time savings, less error-prone processes, and increasing overall effectiveness of teams working with documents.
- The Intelligent Document Processing market is growing, with over 60% of companies planning to introduce a pilot automation program as of 2020.
- The next step in the automation chain after IDP is ADP, which stands for Automated Data Processing – a term encapsulating all technologies to process structured data.
Document Automation with Alphamoon
Well, that’s about that. After that long read, you (hopefully) feel encouraged to learn more about the implementation IDP technology.
Alphamoon offers a cloud-based IDP platform with ready-to-deploy use cases in HR, Accounting, and Debt Collection. We’re also adding more use cases and expanding to new markets. If you’re ready to start automating your document processing, click below.
Not sure yet? No worries. We’ve compiled a few articles that can help you explore the world of IDP in more detail.