Intelligent document processing (IDP) helps companies manage documents more efficiently and digitizes unstructured data from multiple sources.
IDP is part of modern digital transformation, which is changing how businesses operate. Artificial intelligence (AI) is one of the key drivers of digital transformation. AI makes business processes more efficient, reduces costs, and improves customer experiences. According to an IBM study conducted in 2022, AI helped:
- 54% of businesses reduce costs with efficiency
- 53% of businesses improve their IT or network performance
- 48% of businesses improve customer experience
IDP offers similar benefits. It encompasses multiple technologies, including AI, machine learning (ML), natural language processing (NLP), robotic process automation (RPA), and optical character recognition (OCR) to automate your document processing workflow.
That’s barely scratching the surface of what IDP has to offer a modern business. This guide explains the meaning of IDP and how modern businesses can use it to their advantage.
What is Intelligent Document Processing?
Intelligent document processing is a technology that allows businesses to digitize unstructured data from multiple sources of documents. For example, your business may need to manage unstructured data from online survey forms, word files, PDFs, and similar document types.
Imagine manually scanning through each of these documents to extract information, convert them into digital documents, and organize the data. You’ll end up wasting resources and time on a mundane task.
Fortunately, IDP can automate the entire process. Automating the documentation workflow enables you to free up your team’s time for more value-adding tasks. Moreover, you also spend less on handling and routing these documents and errors your team might commit during the process.
Here are five technologies IDP uses to automate your documentation workflow:
Robotic Process Automation (RPA)
RPA is integral to IDP — it’s often even confused with IDP, but they’re technically different. RPA is a technology used in building software robots that automate tasks that otherwise require human effort.
For example, RPA can help populate data from a document into your ERP without involving any humans. This translates to greater efficiency because RPA is faster and doesn’t need coffee breaks.
However, IDP goes a step further. IDP combines the power of RPA and AI. RPA is a rules-based technology that can’t make data-driven decisions. On the other hand, AI can perform more complex tasks.
Artificial Intelligence (AI)
IDP uses AI technologies like machine learning and natural language processing for data extraction, document classification, and claims processing. For example, AI reads and labels information on documents and can accurately route documents without requiring manual effort.
If you’re in banking, AI can help automatically classify mortgage documents, tax records, and pay stubs. IDP can also automate claims processing using AI. For example, it can find the relevant customer for a specific claim and then route it to the appropriate department.
Machine Learning (ML)
ML is a branch of AI that allows an algorithm to learn as it processes more data. Over time, ML helps IDP extract data from documents more accurately.
For example, the ML algorithm uses pre-processed documentation to collect information, including names, amounts, and dates. It stores the data and further analyzes it. This way, it can process future documents more accurately.
Natural Language Processing (NLP)
NLP is a branch of AI that allows computers to understand text and speech, just like humans. IDP relies on NLP to understand data faster. It does so using sentiment analysis and tags language elements like named entities to derive context.
As you can imagine, NLP plays a critical role in understanding the contents of a document and data extraction.
Optical Character Recognition (OCR)
OCR is one of the key technologies used for processing handwritten or scanned documents. With OCR, IDP can copy text from a document image — text that a computer can’t directly copy into its system. OCR converts the information in the document image into editable text, which allows for processing and storing this information.
IDP Vs. OCR
Traditional OCR has limited capabilities. For example, it can only read templatized documents formatted using specific rules. OCR is also limited to just extracting text and can’t derive any context by itself, which means it can’t make any decisions for you.
As a result, OCR fails to process unstructured or handwritten documents, rendering it less valuable for modern businesses and limiting scalability.
On the other hand, IDP combines OCR with other AI-based technologies and RPA for extraction, context, and execution.
A traditional bank check is a classic use case of IDP. Suppose you’re a multinational financial organization that processes hundreds of checks daily. You receive checks from different banks that use a different format. Each issuer has different handwriting. No two checks look the same, so OCR can’t process these checks accurately.
However, IDP can process these checks far more accurately. IDP uses OCR to convert handwritten and scanned data into text. Then, it uses NLP to derive context about the text extracted from the scanned check. The RPA takes over and executes an action based on a preconfigured set of rules. Over time, the ML algorithm gets better at processing checks.
How Does IDP Work?
IDP uses a five-step process for document processing:
- Document pre-processing
- Data Capture
- Document Classification
- Document Extraction
- Document Verification
Before we begin processing documents, they must be cleaned up for ‘noise’ so that they become machine readable. The quality of pre-processing often determines the accuracy of the final result. Reducing noise may include splitting sentences into words, lower-casing words (e.g., the word Bank and bank mean the same but are represented as two separate words in certain document processing models), removing stop words like ‘a’, ‘an’, ‘the’, etc. It may also involve improving image quality for better readability.
Data capture (or ingestion) is the first step, where you input the document into the process. OCR and the ML algorithm are key technologies used for data capture in IDP.
OCR is available on many commonly used tools like Microsoft Office. This means you don’t necessarily need an IDP system to use OCR, but you do need OCR to use IDP. OCR captures data from the document, whether it’s an image or digital document, and sends it to the IDP system for extraction.
We briefly discussed NLP in the previous section on data extraction. However, NLP has an even bigger role to play when classifying documents.
IDP systems use NLP, OCR, and long-term short memory (LTSM) to analyze and classify data. NLP and transformer models (first described in a 2017 paper from Google) establish a relationship between words in a sentence and assign weightage to each word to interpret the meaning.
Practically, IDP systems classify documents and extract data simultaneously. The system typically takes less than a few seconds to classify documents and extract data.
Document data extraction involves converting the captured data into usable data. IDP uses NLP and ML models to understand data and derive context.
- Structured: Data stored in an Excel sheet is a great example of structured data. It’s a data set where the system doesn’t need additional context to interpret the data.
- Semi-structured: Semi-structured data is where part of the data is structured. Examples include invoices, annual reports, and contracts.
- Unstructured: Unorganized data doesn’t follow any specific format and is often received in multiple types, including images. This type of data is the most difficult to process automatically. However, 80% to 90% of data organizations collect is unstructured, making it mission-critical for you to have the tools that allow processing unstructured data.
Structured data is easy to interpret, while interpreting unstructured data requires additional technologies like NLP.
The extraction process involves two aspects:
Textual data extraction: Textual data extraction involves identifying text in a particular document. The IDP system uses ML to identify and tabulate the text based on specific semantic parameters. The best IDP systems use entity extraction techniques like convolutional neural networks (CNNs), that allow the IDP system to extract data from documents that don’t follow any specific format.
Visual data extraction: This is more complex because it involves understanding elements like signatures and logos. The IDP system must detect, understand, and extract information from visual elements while using ML to understand the element’s structural relationship and relevance.
The best IDP systems offer accurate textual and visual data extraction. You can use them to extract data from multiple document types with great accuracy.
IDP verifies and validates document data for accuracy. It ensures that it’s extracting the right data from the document and that the extracted data is accurate.
KYC verification is an example of document verification. When customers provide an ID and complete the KYC form, you’ll need to verify these details against a database. However, you can eliminate manual effort and validate KYC data automatically using IDP.
Automated validation is especially helpful when you’re processing documents at scale. For example, a receipt might be mixed up with one of your invoice batches. The IDP system needs to be able to differentiate and disregard this document through validation.
Validating borrowers by approved vendors is an excellent example of data validation. You can use the IDP system to identify borrowers who have availed loans from an approved lender. You can automatically mark such borrowers during the extraction process without any manual effort.
Once the IDP system completes processing the data, it will create a JSON or XML output file containing the compiled data.
You can also use APIs to migrate this information to a data repository or third-party tools like enterprise resource planning (ERP) or customer relationship management (CRM) systems. If your IDP system doesn’t integrate with your business solutions, we can help you integrate any API-enabled application with your IDP.
Benefits of Intelligent Document Processing
Using IDP offers monetary as well as non-monetary benefits.
Minimizes Human Error
Manually scanning documents and migrating data is prone to human error, especially when processing a high volume of documents. Errors can be expensive—you might upset your customers, disrupt your workflow, or become non-compliant.
IDP helps nearly eliminate the risk of human error from your processes. As long as the data on the physical documents is accurate, the system will make sure everything that goes into your systems via the IDP is accurate.
Better Employee Experience
Automating document processing saves time and effort so your team can focus on more productive tasks.
Our partner, UiPath, surveyed 4,500 office workers worldwide and found 43% of employees believe automation allows them greater opportunities to focus on more important work.
The same UiPath survey also reveals that 52% of employees believe automation helped them achieve a better work-life balance.
Lower Compliance Risk
IDP helps streamline compliance processes. An IDP system automatically extracts relevant information from documents and classifies them based on predefined criteria, which means fewer errors and easily accessible records you might need for compliance.
You can configure the IDP system to compile data on a searchable database, which helps simplify audits by making information readily available. The best IDP systems can also detect sensitive information and determine how to treat it based on sensitivity.
Improves Customer Experience
Fewer errors, faster turnarounds, and frictionless onboarding can greatly enhance customer experience.
Automated document processing allows you to serve your customer better in almost all client-facing functions. For example, if a customer submitted KYC forms last week and calls support to ask if KYC verification is complete, you’ll need to sift through a pile of paperwork to provide an answer.
On the other hand, if you use an IDP system, you can search the database and answer them faster. Customers don’t like being on hold—and when you use an IDP system, they won’t have to.
Scale Document Processing
As your business grows, you’ll need to process more documents. Manually processing documents can be resource-intensive.
Your team will spend a ton of time scanning documents and extracting and transferring data to your internal systems. You’ll need to keep adding more people to the team, which means you’ll essentially be investing money in mundane tasks.
Automating mundane tasks allows your team to focus on parts of the business that require a human touch. For example, a sales rep can work on selling—the task you hired them for—instead of collecting KYC forms.
Improves Data Usability
A large portion of your business’s data is unstructured. Similarly, a good volume of business data is locked behind PDF files, emails, and scanned copies of documents. IDP systems help structure this data, making it usable.
This means data previously lying dormant can help you make more insightful decisions once you start using an IDP tool. Digital documents are a critical source of information, provided you handle them correctly. As a McKinsey article explains:
“Incoming mail and other physical documents are an important source of data, but not the only one—many documents that arrive digitally can pose significant challenges if not handled correctly. Emails, for example, may require significant effort to become structured, digital data that can be processed automatically.”
Top 6 Use Cases for Document Processing
IDP has many applications in a modern business’s workflow. Most businesses are looking to use automation to improve efficiency and reduce costs, and that’s where IDP can help.
Estimates on the cost of processing an invoice vary, but it can be as high as $15 to $40 in some cases. The reasons for high costs include fat finger errors, mail costs, and labor, among other things.
Instead, you can use IDP to process invoices and other documents at scale and at a much lower cost. Here’s a closer look:
If you’re a financial organization, you know how automating your KYC verification process can free up a lot of time and resources. Why make your team work on mundane tasks like KYC verification even though performing them manually can result in a human error?
You can use IDP to process KYC documents, verify the customer’s identity, and automatically migrate their data to another platform. This ensures your KYC workflow is free from human error and reduces your cost of compliance. According to a McKinsey survey, automated KYC can also improve customer experience by 18%.
70% of onboarding projects aren’t completed on time. Translation? Cost overruns and unhappy customers.
Customer onboarding is critical because it sets the tone for your relationship with the customer, and IDP can help streamline a part of the onboarding process.
You might have to handle multiple types of documents when onboarding a customer, including credit reports and tax returns. You might be able to automate document handling with RPA, but the automation workflow will stop working as soon as you change the format or document type.
You’ll need AI to handle these changes, and that’s where IDP can help. IDP systems are more robust in handling various document formats and types than RPA, thanks to NLP and ML. Using IDP also helps reduce onboarding costs, but you won’t need to tie up human resources in manual document processing.
A spike in mortgage demand can overwhelm your team and workflow. In fact, a J.D. Power study revealed that customer satisfaction dropped five points on a 1,000-point scale in 2021 because of a major spike in mortgage origination volume.
Managing better demand requires streamlining the entire mortgage process, from application to approval. Underwriting is one of the most critical parts, where your team needs to scan through various documents and pull relevant data needed to approve or reject an application.
A single team member can only process so many applications a day. To scale the underwriting process, you need IDP. An IDP tool extracts applicant data and sends it over to the credit team or your credit evaluation system, streamlining the underwriting process.
Archiving involves storing data digitally to protect it against data loss and other disasters. Creating a digital archive is critical for modern businesses that rely on data to make data-driven decisions.
IDP helps archive documents such as financial statements, tax records, survey results, customer data, and more for future use. You should also ensure the archived data and documents are safe from anything that can potentially cause data loss.
Data entry is one of the most commonly automated tasks. Automating data entry is easy when you’re receiving structured data from a digital platform. However, entering data from physical documents into a digital tool isn’t all that easy with traditional automation solutions.
IDP uses OCR and AI to scan physical documents, extract information, and migrate the information to an output file or another system. For example, you can scan invoices and then update the inventory data in your ERP in real time using IDP.
Intelligent Document Processing with Blanc Labs
If you need to implement a comprehensive, intelligent document processing system, we can help. Blanc Labs works with financial organizations like banks and credit unions to automate their workflow. We can help you build a customized automation system based on your specific needs and internal workflows to make your document processing seamless.