How Intelligent Document Processing is Revolutionizing Business Workflows

Brian DeWyer, the CTO and Co-Founder of Reveille Software, summarizes how intelligent document processing (IDP) is changing business workflows across departments. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

The digital era has significantly evolved our ability to create and store content. Today, businesses are choking on terabits of information, which is the new gold. But just like traditional gold, it must be mined and carefully monitored to ensure adequate storage, instant access, and layered security. The need for intense monitoring has morphed into a physical process of Intelligent Data Processing (IDP).  

Emails, social media posts, Word documents, images, videos, audio files, and surveillance footage are several examples of content flooding storage devices. Although its value is immeasurable, most data remains unstructured. Although the volume of unstructured data is overwhelming, IDP helps organizations with data extraction, classification, and analysis.  

IDP turns structured and unstructured content chaos into accurate processes by using artificial intelligence (AI) and machine learning to automatically extract, categorize, and organize all that data without straining the limits of human capabilities.   

IDP Origins 

The roots of IDP go back to the early 1900s when Optical Character Recognition (OCR) was first developed. OCR began by translating text into sound for people with visual impairments. It then evolved to translate written characters into telegraph code, which evolved into digital credit card and barcode systems. The first volume capable of OCR systems appeared in the 1960s and 1970s, designed for specific applications like mail sorting based on zip codes or reading handwritten numbers. Then, OCR was used to digitize historical archives such as newspapers; when it entered the cloud, it became a tool accessible from desktops and mobile devices.  

Now, artificial intelligence, machine learning, natural language processing, and sophisticated computer vision have transformed OCR into IDP and enabled it to take over the tasks associated with document classification and data capture functions entirely. Modern IDP systems can transform unstructured data into usable formats with speed and accuracy while learning from each interaction to improve processing accuracy. In retail banking, for example, IDP streamlines the handling of customer-related documents like account opening forms and identity verifications. With IDP, banks can automatically extract data from these documents, regardless of format or quality; this speeds up decision-making and reduces errors associated with manual data entry. 

The IDP Process 

But how does IDP do it? Here’s how the process unfolds: 

1. IDP recognizes and categorizes documents, such as invoices, emails, or forms. It then facilitates using tailored rules and extraction methods for more precise and efficient data processing.  
2. Once documents are classified, the IDP extracts the needed information such as dates, names, and amounts. For example, it can extract the due date, vendor name, and total amount due from an invoice. 
3. After extraction, IDP validates data, enhancing accuracy. This process reduces manual checks, ensuring data reliability and seamless integration into existing workflows. 
4. IDP systems facilitate continuous learning through user interactions and feedback. User analytics is crucial for analyzing usage patterns and benchmarks. It helps organizations refine data processing strategies and align the system with changing business requirements. 
5. IDP systems track processing time, error rates, and throughput volumes. The insights gained help identify bottlenecks, improve workflows, and enable organizations to make data-driven decisions.  
6. Deploy focused observability and monitoring of IDP applications, processes, and systems for comprehensive IDP management. 

Behind IDP’s Technology 

There are several core technologies behind IDP. There is no ‘one size fits all’ as multiple technologies exist to support different unstructured data processing requirements. 

1. Optical Character Recognition (OCR) converts images of text from scanned documents and photos into machine-encoded text. 
2. Machine Learning (ML) enhances IDP systems by enabling them to learn from data inputs and corrections. ML algorithms can identify patterns and anomalies.  
3. Natural Language Processing (NLP) allows IDP systems to interpret the data and the semantics of information.  
4. Deep learning, a specialized ML technique, uses layers of algorithms called artificial neural networks to further enhance document processing capabilities. 
5. Generative AI can generate summaries and derive actionable insights from unstructured data, further enhancing IDP’s capabilities. 
6. Computer Vision helps recognize and interpret visual elements within documents, such as logos, stamps, and handwritten notes. 
7. Robotic Process Automation (RPA) automates repetitive tasks, such as data entry, sending notifications, and updating records, without human intervention.  

The IDP Benefits 

The following are the benefits associated with IDP: 

• IDP streamlines operations and enhances data management across sectors. Automating the extraction and processing of data streamlines workflows and cuts down on manual labor. IDP improves productivity and optimizes resource allocation across the organization.
• IDP enables rapid data processing and quick accessibility, providing faster access to data and helping organizations make swift decisions. Companies can maintain a competitive edge by staying responsive to market changes and customer needs.
• Reducing the reliance on manual document handling serves two benefits: it decreases labor costs and minimizes the occurrence of errors. Both help prevent financial losses associated with data mishandling and reduce the workload of correcting such mistakes.
• IDP can seamlessly integrate with existing systems, which allows for the automation of complex processes at an enterprise scale. This automation ensures data consistency and enhances quality across various operations, boosting reliability and compliance within the organization. 
• IDP’s efficient data processing allows quick responses to customer queries and ensures accurate request handling. This process improves customer interactions and satisfaction, which fosters loyalty and increases a business’s reputation.  
• IDP systems can adapt to increased workloads without requiring additional resources. Such scalability supports business growth, enabling companies to expand their operations while managing the larger data sets that come with scaling. 

IDP Penetration: From Banking to HR 

The insurance industry is revolutionizing claims processing by automating the extraction and analysis of data from claims forms and related documents. This has reduced processing times, boosted accuracy, expedited claim resolutions, and elevated customer satisfaction. IDP’s ability to detect patterns and anomalies has also helped insurers strengthen their fraud detection efforts. 

In banking and financial institutions, IDP accelerates customer onboarding processes. By automatically extracting data from ID proofs, application forms, and other documents, IDP speeds up the verification process, reduces errors, and improves compliance with regulatory mandates. This results in a more efficient onboarding experience that enhances customer satisfaction. 

IDP streamlines the management of shipping documents, invoices, and freight bills within logistics. It automates data capture from various forms and documents, enabling quicker invoice processing and reducing discrepancies. This efficiency improves supply chain visibility, enhances tracking accuracy, and reduces bottlenecks, resulting in more reliable and faster deliveries. 

HR departments can automate the processing of employee documents, such as legal documents, resumes, onboarding paperwork, and all the forms that come with benefit packages. This process speeds up hiring, improves records management, and ensures compliance with regulations. It also allows HR professionals more time to focus on tasks like talent management and employee engagement rather than toiling away at data entry work. 

Conclusion 

The future of IDP is auspicious and expected to become more sophisticated. Fortune Business Insights states, “The global Intelligent Document Processing market size was valued at USD 5.89 billion in 2023. The market is projected to grow from USD 7.89 billion in 2024 to USD 66.68 billion by 2032, exhibiting a CAGR of 30.6 percent during the forecast period.” 

Artificial intelligence will continue to evolve, impacting IDP’s ability to process complex documents and provide deeper insights into extraction, classification, ingestion, and validation. Because digital transformation is a process, not a goal, IDP will always be essential for automating and streamlining document processes. Five-year calculators, such as the Intelligent Data Processing Community’s calculator, can determine the return on investment (ROI) from using IDP.  

All industries, from banking to HR, need the right tools to process information accurately with as little human intervention as possible. Intelligent Document Processing (IDP) virtually eliminates the paralysis by analysis syndrome, which has hampered the operational efficiencies of document-driven processes with human errors for decades. IDP’s future as a valued business tool shines as brightly as a gold nugget in a clear-water stream. Organizations that embrace these nuggets will prosper faster than those that retain outdated data procedures and workflows.