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Streamline Your Work: How IDP Saves Time and Money

How IDP Saves Time and Money

Introduction

Imagine an office overwhelmed by paperwork and manual data entry, struggling to manage large volumes of documents. Intelligent Document Processing (IDP) transforms this scenario by automating these tasks, improving both efficiency and accuracy. Without IDP, a significant amount of resources is lost to manual processes that are time-consuming and prone to errors, leading to delayed decision-making, increased operational costs, and missed growth opportunities.

IDP allows businesses to manage documents faster and more accurately, minimizing the need for extensive resources. By automating the extraction and processing of information from various document types, IDP significantly reduces the time and costs associated with manual data entry.

This blog discusses the challenges of traditional document processing, the shift to modern solutions, and 7 Intelligent Document Processing applications transforming industries. 

Challenges Faced by Traditional Document Processing Methods

According to the IDC MarketScape Report, “Fortune 500 companies as a whole lose an average of $12 Billion per year to inefficiencies caused by the lack of a structure for managing their documents and digital assets.” This staggering figure highlights the critical need for improved document management systems.

Now, let’s look at the various challenges associated with traditional document processing methods.

  • Document Variability

Traditional systems struggle to process documents with different formats, styles, and layouts, leading to errors and misinterpretations.

For instance, invoices from different suppliers may have unique layouts, formats, and terminology. Traditional document processing systems struggle to accurately process this variability, complicating data extraction on important information like amounts, dates, or vendor names and increasing processing time.

  • Unstructured Documents

Many documents are unstructured, making them difficult for traditional systems to handle, resulting in longer processing times.

Let’s say a legal department is handling multiple contracts from different clients. Each contract varies in structure and phrasing for key terms, such as deadlines and pricing. Traditional systems often struggle to accurately identify these critical details, forcing teams to spend extra time manually reviewing each document for accuracy.

  • Data Extraction Accuracy

Poor data extraction can lead to inaccuracies in mapping business processes, affecting decision-making and operational efficiency.

For instance, if a financial institution incorrectly enters a borrower’s income or credit score on a loan application, it could result in approving loans for applicants who are not qualified. This would violate compliance regulations and risk significant financial losses, while also damaging their reputation.

  • High Costs

The expenses for paper, ink, printing, scanning, and storage can quickly add up, straining budgets.

For example, the HR department handles hundreds of job applications, requiring each resume to be printed, scanned, and stored, which increases costs for paper and ink. The process of scanning and organizing these files is time-consuming, detracting from valuable time that could be spent on more important tasks, such as interviewing candidates and improving recruitment efforts.

  • Slow Processing

Slow processing and redundant records can hinder document management, leading to inefficiencies and potential losses.

For instance, think of an insurance company handling claim requests. Each claim goes through multiple verification steps, and the company checks the customer’s details and claim history. Slow processing in a traditional system means longer wait times. This can delay claims, hurting customer satisfaction and loyalty.

Traditional document processing methods are often slow, inefficient, and prone to errors, leading to incomplete results and delays in workflows. These inefficiencies can hinder productivity and slow down decision-making. There is a need for more advanced solutions, like Intelligent Document Processing (IDP), to streamline document handling and improve overall efficiency.

Why Are Businesses Adopting Intelligent Document Processing?

As businesses expand, managing documents becomes more challenging, requiring a system that is smarter, faster, and more precise to keep up with the increasing operational complexity. This is where Intelligent Document Processing (IDP) comes in. 

Here are six reasons why IDP is so appealing to businesses in 2024:

  • Automated Tasks

According to a 2022 McKinsey Global Executives Survey, 70% of organizations pilot business process automation in one or more units. IDP can automate tasks like data extraction, sorting, and classifying documents. By reducing manual work, companies can process documents more quickly and accurately.

For instance, in finance and accounting, IDP can automatically extract data from invoices and receipts, reducing the time spent on manual entry. This allows teams to focus on higher-value activities such as financial analysis.

  • Improved Accuracy

IDP uses Optical Character Recognition (OCR) and Machine Learning (ML) to cut down on human errors. This is very important in industries like banking and healthcare, where even small mistakes can have serious consequences.

In healthcare settings, for example, IDP can accurately capture patient information from forms without the risk of misinterpretation that often occurs during manual entry. This leads to better patient outcomes and compliance with regulations.

  • Faster Access to Information

According to Gartner’s 2023 survey, almost 47% of employees have difficulty locating the relevant data in documents required for their work. To address this challenge, businesses should invest in reliable data capture solutions that ensure fast and accurate extraction of high-quality document data.

IDP scans, analyzes, and stores documents so they are available in real time. Employees no longer have to dig through piles of files to find what they need, allowing for quicker decision-making.

In legal services, for example, law firms can automate the retrieval of relevant case documents from extensive databases in seconds instead of hours. This swift access facilitates faster decision-making and improves client service.

  • Scalability

Cloud-based IDP platforms are gaining traction, providing businesses with scalability, flexibility, and real-time processing. With more enterprises moving to cloud infrastructures to manage document-heavy tasks, the adoption of cloud technologies in IDP solutions is projected to increase by 12% annually.

For instance, a manufacturing company that implements IDP can easily accommodate spikes in order processing during peak seasons without compromising efficiency.

  • Cost Reduction

By automating labor-intensive tasks, businesses can lower labor costs. Fewer mistakes mean fewer expensive errors, making IDP a cost-effective solution.

Companies implementing IDP have seen reductions in labor costs by up to 30%, with some organizations reporting annual savings of approximately $2.9 million due to reduction of their manual extraction workforce.

  • Enhanced Security and Compliance

Many IDP solutions focus on keeping data secure and meeting regulations. This is especially important in industries that deal with sensitive information, like healthcare and finance. These advanced tools ensure high precision in data extraction while safeguarding against cyber threats and legal issues.

Adopting IDP is like upgrading your software; everything runs more smoothly, quickly, and with fewer problems. 

Key Technologies Behind Intelligent Document Processing

The success of Intelligent Document Processing lies in the combination of several advanced technologies working together to automate and optimize document workflows. Let’s break down the core technologies that make IDP possible:

  • Optical Character Recognition (OCR) in Document Processing

Optical Character Recognition (OCR) is a foundational technology in IDP.

For instance, a company wants to upload a stack of receipts into their system. Rather than manually entering each receipt into their expense tracking software, Optical Character Recognition (OCR) swiftly reads and digitizes all the information in an instant, significantly reducing the time and effort required for this tedious task. 

It enables machines to “read” printed or handwritten text and convert it into digital format.

Here’s how OCR works:

  • Scanning: The system scans a physical document (such as an invoice or contract) and captures its text.
  • Text Recognition: OCR then identifies characters, words, and numbers, converting them into a machine-readable format.
  • Data Extraction: Once recognized, the text can be automatically extracted and stored in a database or other digital format.
  • Natural Language Processing (NLP) and Its Role in IDP

Imagine a company looking to hire a manager with hundreds of resumes to sift through. Rather than manually reviewing each one, Natural Language Processing (NLP) handles the bulk of the work. In just a few minutes, the system generates a shortlist of qualified candidates, saving hours that would otherwise be spent on manual evaluation.

Natural Language Processing (NLP) is what enables machines to understand and interpret human language. NLP makes document processing smarter by allowing machines to understand human language in context.

Here’s why NLP is so important for document processing:

  • Text Understanding: NLP helps computers understand the content and context of documents, making it possible to extract key insights such as names, dates, amounts, or even sentiments from a document.
  • Data Extraction: NLP can help extract essential information, without having to manually scan each document.
  • Translation and Summarization: NLP can also be used to translate documents into different languages or summarize long reports into key points, making it easier to manage multilingual or dense content.
  • Artificial Intelligence and Machine Learning in IDP

Artificial intelligence will reach human levels by around 2029..” –Ray Kurzweil, a computer scientist, inventor, author, and futurist.

Consider an organization managing a large team where members submit various documents for approval—contracts, reports, or proposals. Rather than manually reviewing each document, AI and Machine Learning (ML) systems can learn to identify patterns based on past approvals. AI and ML make IDP smarter, allowing it to handle more complex tasks and improve accuracy as it processes more data

Machine Learning (ML) trains systems to recognize trends and patterns within data so they can make predictions or decisions without explicit programming. This is especially useful for document processing when dealing with large datasets or complex workflows.

Here’s how AI and ML enhance IDP:

  • Learning from Experience: When the system encounters a new type of document or layout, it learns from the experience and applies that knowledge the next time it sees a similar document, eliminating the need for manual reprogramming.
  • Decision-Making: AI helps the system “decide” what to do with certain documents, such as categorizing them based on content or routing them to the right department for further action.
  • Error Reduction: Over time, ML helps reduce errors by continuously improving its understanding of documents. This is particularly useful in industries like healthcare or finance, where the accuracy of data is critical.
  • Robotic Process Automation (RPA) and Document Processing

For example, a company’s finance team processes hundreds of invoices each month. Rather than having an employee manually input each invoice into the accounting system, Robotic Process Automation (RPA) can handle this task.

When an invoice arrives via email, the RPA bot automatically extracts the relevant details—such as the vendor name, amount, and due date—and inputs them directly into the accounting software.

Robotic Process Automation (RPA) refers to the use of software robots that mimic human actions to complete repetitive tasks. RPA is often used in document processing to automate workflows that involve moving files, entering data, or initiating processes based on document content.

Here’s how RPA contributes to IDP:

  • Task Automation: RPA robots can automatically move documents from one system to another, extract data, and update records without human intervention.
  • Trigger Actions: Trigger actions such as moving the data to relevant teams or updating a system 
  • Compliance: RPA ensures that processes are followed consistently, reducing the risk of non-compliance or human error. 

7 Intelligent Document Processing Applications in 2024

In 2024, Intelligent Document Processing (IDP) is transforming how industries handle their documents, making it simpler and more efficient for organizations. 

  • Invoice Processing in Finance and Accounting

Manual invoice processing can cost up to $9 per invoice, while top companies using AI and automation reduce it to under $1.42. IDP eliminates the tedious process of manual invoice entry, reducing human error, speeding up approvals, and improving accuracy. IDP streamlines document processing, allowing businesses to optimize resources and focus on strategic goals.

Automating Invoice Processing: 

  • Traditionally, finance departments manually extract data from invoices, which is time-consuming and error-prone. 
  • IDP automates this task, handling invoices in formats such as PDFs, images, and paper, and inputs the data directly into accounting systems.
  • Claims Processing in Insurance

IDP boosts efficiency and accuracy in claims processing while enhancing customer satisfaction. Accenture reports 31% of customers are unhappy with slow insurance claims, with six in 10 citing delays as a major issue. 

Streamlining Claims Handling: 

  • Insurance companies process large volumes of claims involving forms, medical reports, and legal documents. 
  • IDP automates the extraction and validation of this information, accelerating claims processing times and reducing errors.
  • Loan Application Processing in Banking

By automating document handling, IDP helps banks process loan applications faster, improving both efficiency and customer satisfaction.

Accelerating Loan Approvals: 

  • Banks manage documents such as income statements, credit reports, and tax returns during loan application processing.
  • IDP automates the extraction of critical data from these documents, enabling faster decision-making.
  • Medical Records Management in Healthcare

Manually managing patient records is time-consuming and error-prone. With IDP, healthcare providers can streamline data management, improving operational efficiency and patient care outcomes.

Managing Patient Data: 

  • Healthcare organizations manage vast amounts of patient data, including medical histories, lab reports, and treatment plans. 
  • IDP automates the extraction and categorization of this data, ensuring quick access to accurate medical information.
  • Contract Management in Legal Services

IDP enables legal professionals to manage contracts more efficiently, reducing the time spent on manual document review and allowing for greater focus on strategic tasks.

Automating Contract Review: 

  • Legal firms handle large volumes of contracts requiring detailed analysis. 
  • IDP automates the extraction of key terms, dates, and clauses, simplifying the contract lifecycle.
  • Onboarding Documents in HR

IDP helps HR teams onboard new employees quickly and accurately, reducing manual data entry, improving compliance, and enhancing the overall onboarding experience.

Streamlining Employee Onboarding: 

  • Human Resources departments deal with numerous documents during onboarding, such as employment contracts and tax forms. 
  • IDP automates the extraction and verification of data from these documents, speeding up the onboarding process.
  • Customer Correspondence in Retail

IDP improves customer service by automating the handling of customer inquiries, resulting in faster responses and improved customer satisfaction.

Managing Customer Interactions: 

  • Retail companies receive a high volume of customer queries and feedback through emails, surveys, and social media. 
  • IDP automates the processing and categorization of this correspondence, ensuring it is routed to the appropriate department for follow-up.

Conclusion

Businesses across industries are realizing that traditional document processing methods are no longer sufficient to meet the growing demands of efficiency, accuracy, and scalability. Intelligent Document Processing offers a smart, automated solution to these challenges, allowing companies to process documents faster, with fewer errors, and at a lower cost.

In the coming years, we can expect even more advancements in IDP technology, with AI and Machine Learning playing an increasingly central role. Businesses that invest in IDP now will be well-positioned to lead their industries into the future.

So, if your business is still relying on manual document processing, it might be time to consider making the switch to Intelligent Document Processing. After all, why spend hours on tasks that machines can handle in seconds?

 

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