Data Mining: Document Processing Automation: Streamlining Paperwork with AI and OCR

Document Processing Automation: Streamlining Paperwork with AI — technical and otherwise like contract agreements, blue prints, supply orders, invoices, receipts, and what not? For the smooth running of processes, these documents need to be properly classified, sorted, stored and processed. Appropriate documentation is a time-consuming and tedious process, prone to human-error and oversight. Document processing automation is the answer to solving this challenge.

Nevertheless, in this age of artificial intelligence, adding the power of AI to regular document processing automation has added advantages. With the use of modern technologies like AI, optical character recognition (OCR), intelligent character recognition (ICR), machine learning (ML), robotic process automation (RPA), natural language processing (NLP) and deep learning, document processing automation can be made not just more efficient but smarter.

Well-known as intelligent document processing (IDP), it is a technology that offers end-to-end document automation solutions by extracting and organizing data from both structured and unstructured documents to fuel business process automation. Unlike traditional OCR solutions, IDP not only recognizes and extracts text from documents, converts images and other graphical elements in a document into machine-readable data, but also understands the context and meaning of the information.

What Is Document Processing?

In a nutshell, document processing involves analyzing physical and digital documents, extracting relevant information, and converting it into structured data based on which businesses can take actionable decisions.

In the traditional manual process, data operators sift through pages of documents, searching for relevant information and key fields, and entering them manually into books or a system. This system came with its inefficiencies as the volume of data kept increasing. More work meant more humans in the process and as the saying goes – too many cooks spoil the broth. The data thus processed started becoming irrelevant and unusable because of human errors, where values are missed, documents are misplaced or filed improperly, and many more.

With time, and as part of digitization, businesses adopted OCR technology, which could convert scanned documents and images of physical files (handwritten and printed) into machine-readable formats without requiring much human intervention.

Nevertheless, this system also had its limitations as OCR is rule-based and would work well only with data that is structured and in a fixed format. The challenge remained that about 75-85% documents businesses received were unstructured, semi-structured or in different formats.

AI-assisted Document Processing

To mitigate this challenge, AI was introduced. AI-enabled OCR was a significant advancement as it integrated the advanced technologies of machine learning, NLP and deep learning, which enabled it to process even unstructured data with ease.

While AI-powered OCR significantly tackles the major challenge of data extraction with minimal errors, businesses need something more—they need a system that can automate end-to-end document workflows. The need to integrate the capabilities of AI at every step of the process, and not just at the data extraction-level, has become inevitable as businesses keep growing and the inward flow of documents keep increasing.

The holistic approach towards document processing automation using the power of AI is what came to be known as Intelligent Document Processing (IDP), or smart document processing. This approach goes beyond just data extraction, as IDP can understand context, validate data, and automate end-to-end processes. This makes document processing not just more accurate, but also more efficient, and fraud-resistant.

The major benefits of IDP are the reduced time taken for document processing and the removal of manual intervention unless required thereby eliminating the possibility of human errors. Besides, IDP runs on secure technology that prevents the misuse or manipulation of data, and hence businesses need not worry about data privacy issues. It also leaves behind a trail of the process, which makes it easy for audits and to comply with elaborate regulations. All these together contribute to a significant reduction in costs, including human labor, in the long run.

Document Processing Automation Workflow Explained

Data is the main fuel for digital transformation tools, and this data comes from various documents of various forms and factors, emails, images and PDFs. AI document processing or intelligent document processing makes this data easily accessible by converting both structured, semi-structured and unstructured data into machine-readable data which can then be used to automate various business processes.

IDP makes use of different technologies like machine learning (ML), optical character recognition (OCR), natural language processing (NLP) and generative AI to sort, categorize, and extract relevant information from various documents. IDP can also verify and validate these data by cross-examining them with internal or external databases. IDP tools are non-invasive, integration-friendly, scalable, and works seamlessly with legacy systems to accelerate the digital operations process.

The following are the steps involved in IDP:

• Document pre-processing: The first step in IDP is document pre-processing or prepping. As the name suggests, this is the stage where documents are made ready for being treated by OCR and AI algorithms, and involves procedures like binarization, noise-reduction, de-skewing, and de-speckling. This ensures that the data extracted is as accurate as possible, minimizing errors in downstream processes.

• Data classification: Once the pre-processed data is fed into an IDP, the system uses NLP, OCR and Google Vision, supervised and unsupervised learning to sort and classify documents based on their type and content. This helps in routing the different documents to the appropriate processing workflows. The system also employs intelligent character recognition (ICR) at this stage to decipher elements that are difficult to read such as glyphs and other unclear texts like handwritten ones.

• Data extraction: The third step in the process is data extraction, where AI algorithms are employed to extract relevant data from the classified documents. ML, OCR, NLP, deep learning, and Google Vision are used to extract data such as simple text, numeric values, and even images or signatures.

• Data validation: This step involves the use of technologies like fuzzy logic, regular expression (RegEx), rules, and scripts to assess, match, and manage the extracted data for accuracy and relevance to the specific industry or business context. IDP also makes use of robotic process automation (RPA) to further verify the extracted data for suitability to the prescribed purpose or process.

• Human-in-the-loop (HITL) validation: This is the stage where the automated data is sent for human review and validation. Any corrections or revisions are done at this stage. This helps in optimizing the quality of the processed data. Supervised learning is used to provide a rapid feedback loop and fine-tune AI training by correcting data via human input.

10 IDP Applications

Intelligent document processing is used across industries. The following are the top 10 practical examples of how IDP is employed in different industries:

1. Invoice Processing

One of the main applications of IDP across industries is in invoice processing. The traditional way of invoice processing has always been inefficient and prone to human error, especially while dealing with large volumes. Besides, invoices come in different forms and formats and hence using robotic process automation (RPA) also had its limits while dealing with such documents as RPA works well with structured data.

IDP addresses these challenges by automating and streamlining data extraction and processing. AI-enabled IDP can detect and extract relevant information like vendor details, line items, invoice numbers, and due amounts with accuracy from any kind of invoices with the help of optical character recognition (OCR) and machine learning (ML) algorithms. The data thus collected are converted into a machine-readable format and sorted into relevant fields like invoice number, amount, date etc., and this helps in seamless integration with ERP systems. IDP systems can also detect any discrepancies or anomalies in the data in real time and these would be flagged and sent to human reviewers for quick resolution. The AI algorithms also help these systems to learn continuously from feedback loops, thereby reducing probabilities of errors with time.

IDP systems thus make invoice processing not just faster but more efficient and also less reliant on human interventions.

2. Contract Management

Contracts are agreements between two parties which contain a lot of legal terms and other important details like expiry date, renewal terms and other information. A contract will also have various clauses that need to be checked across many documents and systems. Manually accomplishing this task is not just time consuming but also prone to mistakes. This can lead to missed deadlines and not meeting required terms, which ultimately results in prematurely ending contracts or inefficiencies in renegotiations.

IDP systems can automate the entire management of contracts by extracting and categorizing all relevant information from these contracts like business names, clauses, and financial terms. IDP also ensures that the contracts are in line with relevant governing laws and reduces the risk of penalties. It streamlines the entire contract management process by automating tasks like creation, review, approval and monitoring.

Besides using strong security measures to protect sensitive data and block unauthorized access to contracts, IDP also ensures compliance. All these make the contract management workflow cost effective and efficient. It lets businesses respond to chances of renegotiating or renewing contracts on time.

3. Automated Claims Processing

Claims processing is an important function in the insurance industry. It is one thing that directly affects customer satisfaction and any delays or discrepancies would lead to customers losing trust and confidence in the insurance provider.

Automated claims processing requires precise data extraction and interpretation as claim forms usually come with various supporting documents that could be in different formats and layouts. IDP systems, which come with technologies like OCR and machine learning adept at handling structured and unstructured data, make this procedure easier and resolve claims on time. All relevant data like policy number, incident dates, and claim amounts can be easily detected by IDP, and then apply ‘matching rules’ to verify the validity of the claim.

IDP also parses detailed information from extensive medical records, thereby making claims adjudication faster. In all, ADP helps insurance companies by making the claim settlement process much faster, efficient, secure and reliable, and this in turn would improve customer satisfaction and retention.

4. Boosting HR process

The HR department of any enterprise is burdened with a number of documents like personal identification documents, leave requests, benefits forms, training material and many more. These documents come in various forms and complexity and manually managing those presents a considerable challenge. Adding to it is the need for precise data entry and strict confidentiality.

IDP systems can leverage the capabilities of OCR and machine learning to automate the extraction and processing of data across the full spectrum of HR documents, and then categorizing and integrating this data into HR systems, enhancing data accuracy and security.

IDP systems can also automate the approval of leave requests, and compile and organize data on employee training materials. This automation not only speeds up HR processes but also significantly reduces the administrative burden, allowing HR professionals to devote more time to strategic initiatives such as talent development and employee engagement.

5. Customer Onboarding

Customer onboarding is a critical task in many industries, especially healthcare, telecommunications, banking and insurance. Capturing customer data is essential for the activation of services and this process traditionally involved considerable paperwork, which sometimes included handwritten notes and manual entries, which were prone to human error.

IDP, with its OCR and machine learning capabilities is a good tool to automate this process. IDP makes capturing customer easily and accurately. Be it patient data capturing forms, tax documents, or other paperwork, IDP can easily convert them to machine-readable data.

The data thus captured is then cross checked with internal or external databases for validation, completeness and compliance with regulatory requirements. The validated data is then fed into CRMs or customer management systems. This would prompt the system to move to further steps of onboarding like account creation, background verification, and initial customer communications.

Customer onboarding automation using IDP streamlines the entire process from application to activation, while staying compliant. It speeds up the process without compromising data integrity, and this improves customer satisfaction and retention.

6. Empowering Legal Teams

Legal teams of corporates have to deal with a variety of documents like legal contracts, corporate filings, litigation papers, and compliance documents. Managing these documents is hard because of the volume and types of legal data included in them. Complicity or discrepancies in managing these documents could have serious repercussions as it would invite legal measures and also increase the risks in decision making.

IDP can quickly identify and retrieve legal precedents from extensive databases, thereby reducing time spent on research. Moreover, the automation of scanning and categorization of these documents streamlines the discovery phase and improves accuracy and speed during litigation.

Besides, continuous monitoring of compliance documents by IDP can detect legal risks early, which helps businesses to take proactive measures. Also, when it comes to corporate filings and contracts, IDP extracts key information like renewal terms and dates, which could help legal teams to meet obligations and stay ahead of deadlines.

In all, IDP can enable legal document management with intelligent processing by reducing risks, managing workload, improving accuracy and enhancing the team’s efficiency.

7. Data Insights

IDP’s capability is not restricted to data capturing and classification. IDP can also analyze a variety of datasets like customer feedback forms, market research documents, operational logs and others and give concerned professionals useful insights.

IDP can organize information in an easily searchable and analyzable format, which provides managers with insights on customer interactions, market trends and business operations. Businesses can make informed decisions and drive strategic initiatives by transforming raw data into actionable insights.

IDPs are also scalable automation solutions, which means that they can process increasing volumes of data without compromising speed or accuracy. This comes very handy, especially for growing businesses.

All these with the added advantage of risk analysis capability of IDP helps companies make informed decisions, while improving operational efficiency.

8. Accelerating R&D

Research and development across industries rely heavily on managing and analyzing vast sets of documents such as patent documents, experimental data, research papers and others. Traditional ways of using paper and ink for documentation slows down the process and delays the introduction of innovations into the market.

IDP improves R&D in more ways than one. Besides automating the management and analysis of these critical documents, IDP makes it easy for researchers to sort and extract relevant data from complex documents, and also gives them easy access to information from a multitude of sources.

IDP makes use of advanced algorithms to maintain accuracy of data and eliminate redundant information so that researchers can reach more informed conclusions. It expedites literature reviews by extracting and summarizing information from numerous documents. NLP technology quickly identifies key concepts and trends.

It also automates patent information extraction and analysis, which speeds up the review process and gives a full understanding of the patent landscape. Moreover, IDP digitizes and standardizes data and makes it easy for sharing information and collaboration.

9. Regulatory Compliance

Staying compliant with industry regulations is critical for businesses operating in heavily regulated industries like insurance, finance, healthcare, energy etc., and this involves managing many documents like safety records, financial reports, patient files and more.

Non-compliance could attract heavy penalties for these organizations. IDP makes compliance easier by automating the extraction, management and analysis of data from the important documents. It also supports continuous monitoring and helps enterprises stay updated on the changing laws and regulations.

IDP ensures accurate data extraction thereby preventing errors and inconsistencies that might lead to breaches. The continuous monitoring of changing rules and regulations helps enterprises to make changes early. It also provides comprehensive audit trails, aiding compliance demonstrations during audits.

10. Enhance Mortgage Processing

As is the case with other credit processing procedures, mortgage processing also involves a lot of paperwork starting from loan application to approval, not to forget the numerous forms to fill, credit report and various levels of approvals in between. Manual handling of all these documents causes significant delays, and there’s also the chance of errors while doing data entry.

IDP automates this process end-to-end and takes care of everything from data capture from the different forms to routing documents to the concerned people for approvals. It not only makes this process quick but also minimizes the chances of errors. Technologies like OCR and machine learning involved in IDP also ensures compliance by recording all key information accurately, and making it easily accessible for audits and reviews. It also offers transparency at various levels of the procedure.

All these lead to better customer satisfaction and experience.

Benefits and Challenges of IDP

By automating business processes, there are many benefits that we can look forward to. Other than the obvious benefit of speeding up the process, it also brings other benefits ranging from simplified compliance tasks to improved talent retention.

• Enhance process efficiency: Manual and repetitive processes are not only time consuming but also drain individuals of their productive energy. Automation improves your business process by making it faster and more efficient.
• Achieve agility: IDPs are built on low-code solutions that leverage the advantages of AI. It allows you to use even natural language queries to build new workflows, which can then be shared, reused, and iterated across teams and between business functions. This means you don’t have to wait for long development cycles but can quickly switch gears and rapidly respond to the changing business environment.
• Encourages real innovation: By freeing up the time spent on manual, high-volume, and repetitive tasks, IDP allows employees to focus on strategic challenges, cross-functional projects, and collaborations that spur real innovation.
• Improves employee satisfaction and retention: Since IDPs allow employees to engage in more meaningful, higher-value and productive assignments rather than getting bogged down by mundane tasks, they would gain more experience and this in turn would improve their job satisfaction levels. This goes a long way in retaining talents – after all, who wouldn’t like being appreciated for their efforts?
• Cost reduction: Though the initial cost of deploying IDP could seem high, you’ll be able to continuously remove inefficiencies and improve the workflow in the long-run. Besides, reducing human hours spent on repetitive and high-volume tasks means reassigning your workforce on more high-value and business-critical assignments that would improve decision-making, and process refinement which leads to improves return on investment (ROI).
• Improves accuracy: IDPs employ different AI tools like machine learning and NLPs that have an inherent capability to learn from feedback and improve their functions. This means that your business process workflows keep minimizing errors with time, and also reduce the time spent on reviews. This improved accuracy goes a long way in making the system more efficient, while also reducing time and money spent on correcting errors.
• Enhances customer service: Whether you’re dealing with customers directly or with partners, collaborators, or other external parties, providing a seamless and high-quality digital experience can deliver real-time competitive advantage. Gathering and delivering information quickly, reducing waiting time, and providing immediate status updates on inquiries can significantly improve customer service and increase customer loyalty.
• Improves compliance and eliminates fraud: Automating compliance tasks eliminates the possibilities of human errors or manipulations. It also creates a fully auditable record of activity logs. This not only helps businesses to meet stringent regulatory requirements but also helps in avoiding costly fines. Also, as IDPs are capable of self-learning and continuous monitoring, any changes in regulatory requirements can easily be incorporated into the system thereby helping businesses stay updated and compliant with current industry regulations.

Challenges in Implementing IDPs and How to Overcome Them

However beneficial IDPs are, there are also some challenges in implementing them properly so as to draw maximum benefits from them.

• High implementation costs: One of the first concerns that enterprises face when thinking of automating their business processes is the cost of implementation and not having clarity on the ROI. The best way to work around this is to start with automating high-impact processes like invoice automation. You can monitor the efficiency gain and cost reduction and then adopt automation to your other processes.

• Skill gap and employee resistance: It’s natural for employees to show hesitation to adapt to automation because of the fear of job displacement and concerns about workflow changes. Rolling out hands-on training and a phased approach to AI adoption can ensure that AI works alongside employees rather than replace them. You can further make employees become aware of the advantages of IDPs and the need for upskilling.

• Legacy system integration: Certain IDPs struggle to integrate with legacy systems, CRMs, and ERPs, causing workflow disruptions and IT complexities. In such scenarios, you can rely on API-driven AI solutions that enable seamless plug-and-play integration, reducing downtime and manual intervention.

• Poor data quality: IDPs are only as good as the data they are trained on. Businesses come across documents of various forms, factors and quality. Incomplete fields in documents, low-quality scans, and unstructured data can all result in automation not giving the desired results. This is where ‘pre-processing’ data becomes important. You should implement real-time quality checks of documents which are going to interact with IDPs, and flag off low-quality uploads and instead encourage users to rely on better versions of the documents.

• Compliance and data privacy: Businesses have to make sure to deal with data that are compliant with regulatory requirements like KYC, AML, and data protection guidelines. Use AI-powered IDP with end-to-end encryption, role-based access, and built-in compliance checks to prevent unauthorized data exposure.

Conclusion

Document processing is getting smarter, faster, and more secure with the introduction of AI. Technologies like OCR and AI are eliminating the need for paperwork and making them increasingly redundant and a thing of the past. This is not only making business processing faster but also smarter and in turn helps businesses make better data-driven and learned decisions.

DeepKnit AI has a proven track record in helping enterprises of various scales and tribes to automate their business processes. With scalable automation solutions, DeepKnit AI also has the capability to make legacy system integration easier with the use of our seamless plug-and-play APIs.