How Intelligent Document Processing for Enterprise Streamlines Business Processes?

Ashutosh Saitwal
Ashutosh Saitwal

Founder CEO - KlearStack AI

Intelligent Document Processing for Enterprise’s Success

Table of Contents

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In the digitization and automation age, artificial intelligence is changing the way of many business and industrial processes. This transformation is not just about addressing current business expectations, but also about unlocking new potential for growth and efficiency.

One of the processes where the intelligent automation model seems set to drive massive value in enterprises is unstructured document processing – how information in documents is extracted, stored, processed, and managed. This new technique that possesses cognitive capabilities beyond mere rule-based automation is what we refer to as Intelligent Document Processing.

At the core of modern business processes lies the challenge of extracting information from a document and taking a specific action. Businesses have created standard workflows for most tasks in the quest for consistency, repeatability, and efficiency.

Without the right intelligent automation, documents must be reviewed manually to extract the relevant information. The manual process is inconsistent and takes a lot of time and effort, eventually leading to errors and additional costs. Intelligent Document Processing (IDP) assists banks in improving overall customer service and staying ahead of the competition.


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Intelligent Document Processing for Banking
Intelligent Document Processing for Insurance


IDP Challenges in All-Human Interventions

Human potential is not just a driving force, it’s the driving force behind creations, innovations, and connection. But it’s in the mundane processes that this potential is truly tested, and the value it brings is truly realized. 

Humans often need stimulation for routine tasks. However, in such mundane, repetitive, and everyday tasks, humans also commit errors, delay, and fail to deliver consistency.

As it happens, the routine can be easily automated for better outcomes.

Consider a process like unstructured document processing such as invoices in an enterprise. Large enterprises will have hundreds of suppliers generating thousands of invoices. These invoices must be read and interpreted to extract relevant information for the kick-off payment process. 

Given the sheer volume of invoices, think of the productivity improvement if the manual data entry process gets automated.

Intelligent Automation with OCR

Automation demands addressing semi-structured and unstructured data. Handling structured data is essentially a solved problem; the challenge arises for unstructured data. 

Let’s consider the typical industry-wide case of automated invoice processing. Think of the different layouts, varying text, descriptions, and standards that different vendors follow. How do we automate that?

What adds human-like intelligence to computing capabilities is the integration of ML (Machine Learning). This is the key to enhancing cognitive capabilities and addressing the complexity of the processes being automated. This is also where intelligent OCR technology helps in document definition, identification, data extraction & classification.

OCR looks at the digital form of images and texts to identify underlying patterns and converts them into digital text. However, just OCR is not enough. Once you extract all the text from the document, it is essential to extract only the text/ fields that are of the most relevance. 

Another recent technique gaining popularity due to its simplicity in integration is  ‘the template-based approach. ‘ However, the user efforts involved in such template-based solutions are quite a resource and time-depleting.

Using various ML techniques, one can train the machine to predict the parts of the entire text into relevant fields. Due to the diverse types of documents (contracts, invoices, resumes, payment advice, purchase orders, etc), it is essential to classify, validate, and constantly learn from user inputs.

Advantages of Intelligent Document Processing (IDP) for Enterprises

Here are some of the main benefits that IDP can provide you to upgrade your business performance. These advantages will help to make better business decisions  – 

  1. Can Process Semi-Structured & Unstructured Documents
  2. Can Process Hand-written Documents
  3. Ability to define, extract, and validate relevant data
  4. Low Manual Effort
  5. Rule-Based & Self-Learning Algorithm
  6. Cognitive Capability
  7. Simplification Document Complexity
  8. Reduction in Errors
  9. Improved ROI
  10. Enhanced Productivity
  11. Faster Processing Speed 

Essential Components of Intelligent Document Processing for Enterprise

Here’s how intelligent document automation of enterprise processes could pan out:

1. Defining the Documents –

Defining the document is initially a complex process where the machine learns to discriminate between different types of semi-structured or unstructured data. 

This phase teaches machines how to classify and process documents. The performance will improve as more documents are used for training. 

2. Classification –

Classification seems similar to the first step but it involves reading a scanned document and matching it with an in-built template in the system. 

Hence, if you have defined templates X and Y already, the solution will read and classify the input document as X or Y.

3. Extraction – 

Extraction stands for identifying the relevant data elements individually from the document. For every specific use case, specific rules are defined to extract the information, such as objects like signatures, titles, checkboxes, buttons, bar codes, characters, etc. 

The extraction happens in a more traditional manner i.e., by using text extraction templates. This is a completely rules-based method of extraction.

4. Validation –

Validation is all about confirming the data items extracted by the system, especially for complex items such as hand-written text human signatures. This potential unit of intelligent automation software comes in handy for improving accuracy in future processes.

The evolution of technology is visible even as we consider the process outlined here. As laid out here, this process involves a significant manual effort at the beginning to define the templates and lay out the rules. 

The value of automating this process can be enhanced when the unstructured document processing becomes independent of templates and intuitive so that rules do not restrict it. That’s the emerging standard in intelligent automation. ML presents a more sophisticated and, importantly, cost-effective method, providing reassurance about the financial benefits of this approach.

Conclusion

Automation is well-placed to become the norm across businesses for handling mundane tasks. However, the next wave of process automation would revolve around amping up the processing of semi-structured and unstructured documents. 

Processing invoices, resumes, legal contracts, and other documents manually would be inefficient, non-scalable, complex, and might lead to errors. Intelligent Document Processing models built using OCR, AI, and ML to automate these routine processes will improve efficiency, effectiveness, accuracy, and consistency. That will, no doubt, drive the next wave of automation in enterprise processes.

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