Without an automated Document Processing workflow, companies limit their ability to adapt to varying workload and operations effectively. Manual document processing introduces the risk to errors and duplications, potentially jeopardising the overall reputation of any organisation. Let’s understand what is document processing.

What is Document Processing?

Document processing is defined as a procedure where unstructured data from physical or native digital documents is converted into digital format that keeps the original layout, text, and pictures of the document intact. 

Types of Document Processing

There are three types of document processing: Manual document processing, Template based/rule based data capture, and Intelligent document processing (IDP).

Consider vendor invoice processing. In the past, businesses manually entered invoice details, which was time-consuming and prone to errors. Manual document processing requires 100% human intervention. It can often lack scalability for large volumes of documents.

But ith template based/rule based data capture, these invoices can be processed using predefined templates and rules. Additionally, with automated intelligent document processing (IDP), these invoices can be scanned, and data can be automatically extracted and validated, thus significantly reducing overall manual effort, and errors, while accelerating the payment cycle. 

Document processing helps companies automate document organising, sharing, and retrieval. 

Thus, in such a context, exploring how document processing works and how you can automate it becomes essential.

How Does Document Processing Work?

Document processing involves a series of steps to extract unstructured data and convert it into digital formats. The journey typically includes pre-processing, data extraction, validation, and integration. Let’s walk through each step:

• Pre-processing: Firstly, documents are scanned or uploaded. Pre-processing involves tasks like noise reduction, image enhancement, and format standardisation. For instance, imagine a stack of handwritten customer feedback forms. The pre-processing step would involve converting the scanned images into clear, standardised digital files.
• Data Classification: The second step involves classifying documents into categories based on content or purpose. For instance, incoming documents might be categorised as invoices, contracts, bills of lading, packing lists, bank statements or customer inquiries. This classification sets the stage for streamlined processing.
• Data Extraction: In this step, relevant information is extracted from the documents. Optical Character Recognition (OCR) technology can extract raw text from images. This extracted textual and visual content is furthur interpreted by an AI/ML layer For example, consider a batch of invoices. Data extraction software would identify and extract fields like invoice numbers, dates, and item descriptions.
• Data Validation: Extracted data is cross-checked for accuracy and completeness. In the case of invoice processing, the document processing system might verify that the calculated totals match the sum of individual items. Any discrepancies are flagged for review.
• Integration: Processed data is integrated into storage systems or APIs for further use. For instance, in a customer relationship management (CRM) system, customer details extracted from forms would be integrated to update records or trigger follow-up actions.

Here’s an example of a document processing journey: 

KlearStack works with a company that generates 200,000 sales transactions each day to eliminate 98% of manual work and optimise its sales document processing , enhance efficiency and customer experience.

• Data processing: When a customer shows interest, their inquiry documents are scanned and uploaded into its standardised digital data.

• Data extraction: The KlearStack system extracts and creates sales quotes or contracts with accurate prices and details.
• Data validation: The sales team reviews and adjusts the document, and if needed, managers or legal teams give their OK. The customer gets the document by email, signs it electronically, and sends it back.
• Integration: The signed document is saved in the system, easy to find and refer to whenever needed.

This end-to-end journey illustrates how document processing takes unstructured documents and transforms them into actionable, structured digital information. This efficiency not only saves time and reduces errors but also empowers businesses to make informed decisions and enhance their operations.

Benefits of Choosing Automated Document Processing

• Cost Savings: Reduced paper usage, printing, and storage costs lead to financial savings. For instance, a legal firm switching to digital contracts eliminates the need for paper and ink expenses.
• Time Savings: Businesses save valuable time by automating repetitive tasks. For instance, in customer service, automated responses to common inquiries speed up response times, enhancing customer satisfaction.
• Error Reduction: Automation minimises human errors that can arise from manual data entry. For instance, in healthcare, accurate patient data entry ensures correct medical treatments and prevents potential complications.
• Operational Efficiency: Automated workflows ensure tasks progress seamlessly. In project management, automated task assignments and notifications keep teams aligned, reducing delays and enhancing project completion rates.
• Data Security: Controlled access and digital encryption enhance data security. For instance, in finance, secure document processing ensures confidential client information is protected, building trust and compliance with privacy regulations.
• Scalability: Scalable solutions adapt to changing workloads. For a retail store during holiday seasons, a scalable order processing system handles a surge in orders without slowing down or crashing.
• Customer Experience: Faster processes lead to improved customer satisfaction. In the telecommunications industry, quick activation of new services enhances the customer onboarding experience.

Each benefit contributes to a more efficient, accurate, and adaptable business environment, ultimately leading to improved overall performance and growth.

Use-cases of Intelligent Document Processing 

Many businesses are now able to eliminate manual errors and streamline their document processing tasks. Consider the following cases:

CRE (Commercial Real Estate)

Document processing can help companies streamline lease management, reduce manual errors, and ensure compliance with lease terms, ultimately enhancing tenant satisfaction and operational efficiency.

For example: XYZ Insurance company recently faced challenges with manual processing of ACORD 126 forms for policy cancellations. To address this, XYZ Insurance implemented an automated document processing system that streamlined the entire cancellation process. The system automatically extracted key data from the forms, established efficient workflow routing, ensured data accuracy through validation, and stored processed forms digitally. The results were remarkable, with significant improvements in efficiency, accuracy, customer satisfaction, and compliance adherence. The company experienced reduced processing times, minimized errors, and lower operational costs. The insights gained from the system also enabled XYZ Insurance to enhance its offerings based on cancellation trends. 

Payroll

Document processing can significantly streamline payroll processes by automating tasks related to employee compensation, tax deductions, and record-keeping. 

For example: Imagine a medium-sized company with 150 employees. Previously, the HR department manually processed payroll every month. With document processing, the HR system automatically pulls data from the company’s time-tracking software, capturing each employee’s work hours, overtime, and time-off records. On payday, employees receive direct deposits in their bank accounts, and digital payslips are generated and emailed to each employee, providing a detailed breakdown of their earnings, deductions, and net pay.

Insurance

Tasks related to policy issuance, claims processing, customer communication, and regulatory compliance can be automated easily with the leverage of document processing, leading to improved customer experiences and reduced errors. 

For example: XYZ Insurance faced delays and errors in issuing policies, affecting customer satisfaction. But by automating policy issuance through KlearStack’s document processing, XYZ Insurance improved its efficiency and accuracy upto 90% and 99.9% customer satisfaction. Customers were able to submit policy applications electronically. The system then checked data for accuracy, reducing mistakes. Policies were generated with correct terms and conditions.This automated workflow ensured timely reviews. Customers signed electronically, speeding up the process. The signed policies were then stored digitally for easy access.

Fraud detection

Document Processing also enhances fraud detection capabilities, ensuring the integrity of insurance operations.

For example: Jeanette submits a claim for car damage, attaching photos of an accident. The insurer’s document processing system, KlearStack, analyzes the photos, cross-referencing them with past claims. It detects a pattern of 90% suspiciously similar accidents and alerts the fraud team. Investigation reveals a scheme involving staged accidents, preventing a fraudulent payout and safeguarding the insurer’s resources.

Mortgage processing

Document processing can reduce manual effort, expedite approvals, and ensuring accuracy throughout a customers’ mortgage journey.

For example: Lisa applies for a mortgage online, attaching her financial documents. Using KlearStack document processing, the system extracts key information, validates her eligibility, and assesses risk. Automated underwriting generates a pre-approval offer, making her mortgage journey swift and hassle-free.

Challenges of Automated Document Processing

Document processing, while incredibly beneficial, comes with its own set of challenges. These challenges can vary depending on the complexity of the documents, the accuracy of the technology used, and the specific industry requirements. Some common challenges include:

• Data Accuracy: Ensuring accurate data extraction from documents, especially when dealing with handwritten text, poor-quality scans, or complex layouts, can be a challenge. Mistakes in data extraction can lead to errors downstream in the process. However, companies who automate through KlearStack are able to extract and interpret unstructured data with up to 99% accuracy.
• Handling Large Volume of Documents:Processing a large volume of documents efficiently without overwhelming the system or compromising processing speed can pose a significant challenge. Systems need to be designed to handle peak loads without sacrificing accuracy or performance.
• Complex Structures: Documents with complex structures, like tables, forms, or diagrams, can be challenging to parse accurately. Extracting data from these structures while maintaining context can be difficult.

Addressing these challenges requires a combination of advanced technologies, process optimization, user training, and continuous monitoring. Document processing solutions need to be adaptable and resilient to ensure accurate and efficient processing across a variety of scenarios.

Document Processing Technologies

Specific document processing technologies can overcome the challenges highlighted above:

1. Optical Character Recognition (OCR): OCR technology converts scanned images or printed text into machine-readable text, reducing manual data entry errors caused by human involvement. It can handle various fonts, languages, and layouts, enabling accurate data extraction from structured documents.

2. Intelligent Document Processing (IDP): IDP combines OCR with machine learning to improve accuracy over time by learning from past extraction results. IDP systems use machine learning algorithms to learn from new documents, thus enhancing their ability to adapt to changing formats and patterns.

3. Document Classification: Document classification uses machine learning to categorise documents accurately based on content, allowing systems to adapt to various document formats. By categorizing documents, classification systems can manage and process related documents as a group, addressing multi-document challenges.

4. Document Extraction: Document extraction is a process that can identify patterns in structured documents, enabling accurate data capture from forms, invoices, and tables. Extraction tools can be fine-tuned to recognize specific data points within documents, thus minimizing errors in data extraction.

5. Document Routing: Automated document routing ensures that documents are sent to the correct recipients, reducing the likelihood of errors caused by manual routing. They integrate seamlessly with existing systems, directing documents to the appropriate teams or individuals for further processing. 

6. Document Archiving: Document archiving systems store documents securely, ensuring compliance with data protection regulations. Access controls and encryption mechanisms safeguard sensitive information.

7. Data Entry: Automated data entry processes are powered by OCR and machine learning. They can handle a high volume of data entry tasks efficiently, reducing the risk of errors that can occur due to manual overload.

Need to add Data Auditing – data validation, reconciliation and transformation

Each of these document processing technologies leverages automation, machine learning, and data analysis to address specific challenges. By using a combination of these tools, businesses can significantly improve accuracy, efficiency, and data security while overcoming the obstacles associated with manual document management.

Conclusion

Whether you’re an insurance company seeking to expedite claims processing, a financial institution aiming to enhance accuracy in loan approvals, or a business looking to scale while maintaining data integrity, document processing holds the key to unlocking new levels of success. Embracing these technologies opens doors to improved efficiency, reduced errors, enhanced data security, and optimised workflows.

So, if you’re ready to transform the way you handle documents and propel your business forward, Klearstack is here to guide you on your path to document processing excellence. From converting handwritten documents to intelligently routing them, Klearstack’s suite of tools empowers businesses to harness the full potential of document processing. By leveraging it’s technology, you can elevate your operations, streamline processes, and ultimately drive your business toward a future of efficiency and success.

FAQs on Document Processing

What is document processing?

Document processing is defined as a procedure where unstructured data from physical documents, such as papers, manuals, and analogs is converted into digital format that keeps the original layout, text, and pictures of the document intact. 

How can a document be processed?

A document can be processed through a series of steps: pre-processing, data extraction, validation, and integration. This converts unstructured data or physical documents into digital formats, making them accessible, searchable, and usable. 

What are document processing tools?

Document processing tools enable users to create, edit, store, organise, retrieve, and share documents. They involve a variety of software and technologies to assist them in document-related tasks. 

What is an example of document processing?

Consider invoice processing. In the past, businesses manually entered invoice details, which was time-consuming and prone to errors. With document processing, invoices can be scanned, and data can be automatically extracted and validated, thus reducing overall manual effort, errors, while accelerating the payment cycle.

Why is document processing important?

Document processing helps companies handle large volumes of unstructured documents, reduce errors, speed up processes, enhance accuracy, and facilitate efficient communication, collaboration, and decision-making.