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Articles March 25, 2024

AI-Powered Efficiency: Exploring the Value of Intelligent Document Processing (IDP)

In today's business world, efficiency is a top priority. Companies are swamped with paperwork and digital documents full of vital, but hard-to-access data. This is driving a shift toward Intelligent Document Processing (IDP), which leverages Artificial Intelligence (AI), Machine Learning (ML), and Optical Character Recognition (OCR) to significantly streamline the processing of important documents. 

Firms often struggle with the volume of data trapped in unstructured formats, the time-consuming nature of manual data entry, and the high incidence of errors, which can lead to costly business decisions. IDP addresses these challenges head-on by automating the extraction and interpretation of data, ensuring that businesses can quickly access the information they need without sacrificing accuracy or efficiency. A key feature of IDP is its ability to evaluate the accuracy of each data element extracted. Lower scores for critical data elements can trigger a human review for quality assurance, while high scores allow documents to move through the process automatically, enhancing both speed and reliability.

IDP goes beyond traditional OCR, which only digitizes text, by using AI and ML to understand, interpret, validate, and organize document data. Imagine the challenge of handling, categorizing, and processing high volumes of detailed invoices, each with unique line items and terms. This slow, error-prone grind of data extraction can be a thing of the past thanks to tailored solutions and AI models available on all major platforms including Microsoft, Amazon, and Google. Taking a more nuanced approach to document processing ensures that businesses can maintain exacting standards of accuracy and efficiency, transforming how they manage and utilize their data.

Document Types and Levels of Complexity

IDP can process a wide range of documents, from structured to unstructured, with varying levels of complexity and length. In fact, the level of document structure directly impacts the amount of training required and the expected performance of IDP systems. 

  • Structured documents, like forms and spreadsheets, have a consistent format and predefined fields, making data extraction straightforward.
  • Semi-structured documents, such as bank statements and daily operational reports, blend structured and unstructured elements, featuring tags or markers that aid in parsing. 
  • Unstructured documents, like research papers and restaurant menus, lack a predefined data model and pose challenges for machine interpretation.

Top Advantages

The IDP advantage lies in its ease of setup, allowing for a user-friendly, performant AI solution using as few as five example documents for training. These solutions offer cost-effective processing, typically at two cents or less per page, and can be established in weeks or days. IDP automates workflows for structured and semi-structured documents, minimizing human intervention and continuously improving through a feedback loop. Seamless integrations with business systems enhance operational coherence, while human in the loop interfaces ensure quality control. Scalable to meet growing data needs, IDP provides valuable insights and ensures compliance with data protection regulations by automating data handling and minimizing exposure to sensitive information.

Case Studies

CapTech has worked with clients to improve access to business-critical information that enables data-driven decision-making using IDP.

Energy Industry Daily Operational Reports (Semi Structured):

A Forbes Global 2000 oil industry company had partial ownership in over 200 facilities managed by external parties, involving 25 vendors who delivered crucial operational performance data daily via emailed PDFs. This process generated thousands of PDFs each month, leading to expensive and labor-intensive manual data entry. To address this issue, CapTech launched a pilot project with a base model trained to extract data from three different vendor templates using five example documents each, achieving 95% accuracy in less than a week. Once approved, the implementation of an IDP solution, costing approximately $500 per month, allowed the client to avoid a manual data entry contract that would have cost them $5,000 per month.

Food and Beverage Competitive Pricing (Unstructured):

An International Airport Restaurant Group operates more than 300 restaurants at 42 locations in North America with over 7,000 food items requiring pricing. The client’s commercial team identified nearby comparable restaurants and menu items to extract, item, description, and price, which informed menu item level pricing strategies. To improve this highly manual process, CapTech established a model to extract item, description, and price, training 200 menus in varying formats and styles, resulting in over 80% accuracy. The implementation of this IDP approach allowed for more rapid and accurate data extraction and enabled constraint based optimized pricing recommendations for this group’s operations.

Future Trends

While many organizations are just getting started with the adoption of IDP and other AI capabilities, longer term we anticipate that integrating IDP with Retrieval Augmented Generation (RAG) models will significantly enhance how businesses interact with and leverage their data. This powerful combination not only streamlines the extraction of information from documents but also enriches it with deep, contextual insights by tapping into extensive internal and external knowledge bases.

Consider the realm of Know Your Customer (KYC) regulations and procedures—essential for financial institutions to prevent identity theft, financial fraud, money laundering, and terrorist financing. IDP can play a crucial role by efficiently processing and extracting vital information from customer documents, such as identification papers and financial statements. Integration with a RAG model elevates this process by incorporating additional information from the institution's own repositories and external sources. This could include a customer's financial history, public records, or relevant news articles, thus offering a more comprehensive view of the customer's profile.

Such an enriched understanding could allow financial institutions to conduct more detailed and nuanced risk assessments, enhancing compliance with KYC regulations. Moreover, the versatility of IDP and RAG models extends across industries; many sectors can benefit from more informed decision-making and operational efficiency.

Is IDP Right for Your Business?

Embarking on the IDP journey may seem daunting due to integration complexities, data privacy, and process changes. Yet, the efficiency gains and cost savings far outweigh these initial challenges. The key to success is choosing the right IDP solution and partner, starting with a focused use case, and expanding after demonstration of initial value.

We recommend a pilot approach to start your IDP strategy. Identify high-volume document processes, select the most impactful one, and create a pilot with three to five document formats to verify the model's performance while establishing integrations. A small team of three to six can manage this pilot, covering model training, testing, and results sharing for broader adoption, all within one to one and a half months. At this point, it is potentially more expensive for organizations to not explore IDP because of the cost savings potential, making it worth exploring sooner rather than later.