Top 6 Intelligent Document Processing Trends 2024

Take a look at six new emerging trends of Intelligent Document Processing (IDP). Learn how AI, cloud computing, and even generative AI are shaping the future of IDP. See how these advancements can revolutionize your business efficiency, unlock data insights, and empower smarter decision-making.

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Top 6 Intelligent Document Processing Trends 2024

Data is critical to better understanding customers, optimizing business operations, reducing losses, and making strategic decisions. According to a CIO report, data experts estimate that unstructured data accounts for 80-90% of the digital data universe, making it difficult for employees to search through and find relevant information within documents. 

Moreover, Gartner’s 2023 survey reveals that nearly 47% of digital employees struggle to find relevant data in documents needed to perform their jobs. So, businesses must invest in reliable data capture solutions to quickly capture accurate, high-quality document data. 

Intelligent document processing (IDP) is one such solution that helps organizations efficiently capture data from structured, semi-structured, and unstructured documents and automate data extraction and analysis. 

It utilizes Artificial Intelligence (AI), Machine Learning (ML), and intelligent automation to sort, organize, extract meaningful data, and validate the extracted data to enhance precision and improve workflow efficiency.

Top 6 Intelligent Document Processing Trends

1. Document Processing Automation with Artificial Intelligence (AI) and Machine Learning (ML) 

Document Processing Automation with Artificial Intelligence (AI) and Machine Learning (ML)

1.1 Trend

Advancements in AI technologies in IDP allow businesses to automate end-to-end document processing workflows. 

Optical Character Recognition (OCR) technology integrated with AI technologies such as ML and natural language processing (NLP) algorithms automatically adapts to different formats, layouts, and templates of documents: 

  • OCR and ICR: OCR converts data from scanned images and documents into machine-readable texts. Intelligent Character Recognition (ICR) extracts information from handwritten texts by analyzing historical data
  • Natural Language Processing: NLP algorithms understand the context and language and perform sentiment analysis to provide insights for organizations
  • Machine Learning: Machine Learning algorithms adapt to different documents and improve their accuracy rate continuously by learning from errors and rectifying them

AI-powered IDP solutions automatically ingest, categorize, sort, classify documents, and capture data, leaving no room for human errors and inefficiencies. Intelligent document processing (IDP) workflow with advanced AI technologies is gaining widespread adoption due to its adaptability and high accuracy rate. 

1.2 Impact

Automate several tasks, including extracting data from bank statements, invoices, patient health records, and W-2 Forms, summarizing legal contracts, and creating new documents such as insurance policies from the extracted data.

1.3 Example

Valtatech, an Australia-based managed services provider, used Docsumo to automate invoice processing. They used IDP to validate data using custom rules in real time and reduce turnaround time.

2. Business Process Automation (BPA) with IDP 

2.1 Trend

IDP platforms allow enterprises to automate the conversion of unstructured data into a structured format for analysis and further processing. Organizations can automate tasks such as document ingestion, preprocessing, data extraction, validation, and conversion into standardized formats in the document processing workflow.

Moreover, the most significant advantage is that IDP can differentiate documents as it reads and understands the context using AI technologies. For instance, it can accurately differentiate invoice and purchase order documents and extract data accordingly. 

Robotic Process Automation (RPA), which handles repetitive tasks based on templates combined with IDP, can automate document processing workflow for enterprises. 

2.2 Impact

Automating document processes using IDP ensures standardized output, as no human intervention is involved. Businesses have the potential to realize substantial efficiency improvements by automating end-to-end customer onboarding or claims processing.

2.3 Example

Insurance agencies can use IDP to automate insurance claims processing. The IDP platform analyzes policy documents, images, and photos covering the damage to extract the required data and calculate an accurate coverage amount that helps the customer. This speeds up the customer payouts, improving customer satisfaction and efficiency. 

3. Humans in the loop with IDP

Humans in the loop with IDP

3.1 Trend

Although IDP efficiently automates repetitive data extraction tasks, you still need humans in the loop to validate the extracted data. You can leverage human expertise in the following three critical areas of automation: 

  • Handling exceptional cases and missing values
  • Training the API model for continuous improvements in terms of efficiency and reduced errors 
  • Validating the extracted data and correcting errors 

3.2 Impact

As a business leader, maximize your team’s efficiency using a combination of IDP and human operators for the most accurate results. 

3.3 Example

Use IDP to automatically extract the required data from claims forms and complete validation with internal computations and available databases. Once the IDP flags out mismatched fields, humans can correct those errors, training the IDP to reduce inaccuracies in the new documents. 

Humans can handle exceptional cases, such as missing data values, to capture errorless data from complex claims documents. Lastly, the extracted data is directly sent to the respective personnel (underwriter) for final review and further processing. 

4. Cloud computing in IDP

Cloud computing in IDP

4.1 Trend

IDP solutions with cloud computing dominate this year over on-premise IDP solutions due to several advantages, such as scalability, agility, flexibility, and cost-effectiveness. 

Secondly, businesses possessing sensitive information within documents can ensure security because of cloud computing, access controls, and encryption features. Lastly, your employees can access information remotely at any time.

4.2 Impact

Businesses processing a high volume of documents can invest in cloud-based IDP solutions without significant upfront investments in IT infrastructure. They can cut costs while enjoying benefits like high security, accessibility, and agility. 

4.3 Example

An accounting firm extracting data from invoices can choose an IDP platform with cloud computing without managing on-premise servers. The IDP software stores the extracted data on cloud servers and ensures security with role-based access controls and encryption. 

5. Advanced analytical capabilities in IDP

Advanced analytical capabilities in IDP

5.1 Trend

Beyond basic data extraction, NLP and ML-based IDP platforms analyze large datasets and perform sentiment analysis, entity recognition, and language translation to derive insights. This NLP-driven information extraction solution helps businesses maximize profitability and growth. 

It provides insights using two different analysis methods: 

  • Predictive analysis: IDP's Predictive analysis identifies future trends with the available data, helping businesses make informed decisions. ML algorithms effectively recognize patterns and trends in the extracted data, unlocking insights to reduce enterprises’ losses. 
  • Prescriptive analysis: Prescriptive analysis goes one step further by collecting data from various sources and analyzing it using ML algorithms to provide insights about all possible outcomes. Additionally, it predicts the impact and effect of these outcomes, ultimately guiding businesses to choose the ideal solution. 

5.2 Impact

Investing in ML and NLP-based IDP solutions helps businesses unlock hidden insights, identify patterns, and make data-driven decisions. Analyzing customer data helps to deliver customized services and reduce customer churn rates for retail companies.

5.3 Example

Retailers can use advanced IDP solutions and analyze social media comments, website browsing history, customer interactions, and buying patterns to decode insights such as the most loved products and improvements required. 

This helps identify future trends, understand customer preferences and needs, and forecast demand so businesses can optimize inventory management and offer tailored services to customers. 

For instance, Amazon uses data analytics to personalize product recommendations as “People also bought” and “Frequently bought together” options. 

Moreover, analyzing sales and product performance provides insights into best-selling and under-performing products. They can use this data to tailor marketing strategies as “Bestsellers” and also optimize product placement based on factors such as seasonality, price, and demand. 

6. Generative AI and IDP

Generative AI and IDP

6.1 Trend

Generative AI can analyze large volumes of data, extract the most crucial information, and create summaries for employees to ensure data at a glance. It helps IDP providers create new conversational tools for their solutions. These tools allow users to interact with their documents, search for specific terms, and extract particular data points.

6.2 Impact

You can train the IDP tool with large volumes of documents, and it can learn and adapt to the data format, type, and layout and understand its context. Generate reports, contracts, and customer communications from IDP with pre-populated data, saving time and improving efficiency. 

6.3 Example

Assume a health insurance agency receives thousands of customer applications for health insurance policies. IDP for insurance businesses assess customer data such as demographics, income, lifestyle, patient records, medical history, genetic disorders, pre-existing health conditions, and risk levels. 

After extracting the vital information, the IDP platform, powered with generative AI, would create customized health insurance policies catering to the customers’ needs and preferences. This provides benefits such as time savings for underwriters. They can review and share the new policy document with the customer.

Additionally, you can deliver personalized insurance product recommendations to clients and significantly improve customer satisfaction. Lastly, it also mitigates risks for insurance agencies by determining the correct coverage amount for the policy based on the extracted data. 

Conclusion: Streamline business operations with Intelligent Document Processing

IDP aids businesses in automating end-to-end document processing workflows, streamlining business operations, saving time and costs, and improving efficiency. For all this, you must choose the right IDP platform to meet your business needs.

With Docsumo, you can capture data automatically from structured, semi-structured, and unstructured documents such as invoices, contracts, bank statements, W-2 Forms, and patient health records. 

Docsumo’s IDP effortlessly learns and adapts to different formats and templates to extract data with a 99% accuracy rate and reduce operational costs by 65-70%. Docsumo’s pre-trained APIs can flag duplicate entries, missing values, and incorrect fields, reducing errors and identifying business frauds. 

Sign up for a free trial of Docsumo and start extracting data from your structured, semi-structured, and unstructured documents.  

FAQs on Top 6 Intelligent Document Processing Trends 2024

1. What is the future of intelligent document processing? 

While generative AI is being used in intelligent document processing, going forward, we’ll see AI models getting smarter as they train on larger datasets to help with decision-making. They will automate key processes across business and IT for growth and cost reduction. 

2. How accurate is intelligent document processing? 

Data captured using IDP platforms is more accurate than manual extraction, as no human intervention is involved. Moreover, the best IDP tools have advanced validation processes to ensure a high accuracy rate of over 99%. 

3. What is Generative AI for intelligent document processing? 

Integrating GenAI in IDP solutions has improved document processing capabilities, enabling better understanding, interpretation, and advancement in general content generation.

This has led to more accurate text extraction, improved pattern recognition, and better adaptation to changing formats and languages. 

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Written by
Ritu John

Ritu is a seasoned writer and digital content creator with a passion for exploring the intersection of innovation and human experience. As a writer, her work spans various domains, making content relatable and understandable for a wide audience.

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