Join our FSI AI Event on March 19th, 2024 at Google Zürich

The future of document processing: What to expect in the next 5 years

The efficient processing of documents is of high importance to virtually every company in every industry, and the data from them is essential to business operations. In recent years, the technologies used to process documents have advanced rapidly. With IDC’s prediction that the amount of data worldwide will grow to 175 ZB by 2025 (of which over 80% is unstructured), technology advances will continue to be necessary to drive document processing.

In this article, we address:

  • What the status quo is in document processing,
  • What some of the emerging technologies are,
  • What the future might look like, and
  • What the challenges are and how Parashift is solving them.

The status quo of document processing

statut quo

Document processing involves the automated handling of both electronic and physical documents. This involves converting them into a machine-readable format so that they are easier to analyze, search, and store. Although advances in technology (we’ll get to that in more detail in a moment) are rapid, the challenges faced by companies in various industries remain equally daunting.

The most important parts of document processing include:

Classification: Classification allows documents to be categorized based on their content. For example, it distinguishes between purchase orders, land register extracts, invoices, and contracts.

Extraction: The extraction part allows only certain data to be extracted from a document. For example, this could be only the addresses, amounts, but also line items that are relevant for the company, instead of the entire document.

Storage: To ensure that your team can retrieve the documents or the relevant data from them at any time, it is important that the data is stored in an orderly and secure manner.

Larger companies today often have integrated automation solutions for the high-volume part of their document processing. Such Optical Character Recognition (OCR) solutions are based on templates. This makes them suitable for rigid documents whose structures do not deviate from the defined template. This means that part of the document processing can already be automated.

However, conventional OCR solutions are not built for documents with different layouts and constant changes in form and format. The template has to be adjusted and the machine reconfigured every time a document changes. This happens when companies work with different customers and partners.

But that’s not the end of the story. Legacy OCR solutions have other significant limitations, including the following:

Integration: Integration with existing ERP or DMS systems can be a complex undertaking. This not only complicates automation in document processing workflows in general, but also very specifically in data extraction.

Maintenance and updates: Traditional OCR solutions are usually not only expensive to maintain, but can also trigger high costs for updates, especially when customized to specific business requirements. This limits the ability of organizations to keep pace with technological advances and improve accuracy and efficiency.

Accuracy: Once documents are of poor quality or have handwritten notes such as signatures, OCR solutions struggle to identify data accurately (or at all). The solution extracts the data inaccurately, making intervention and manual corrections inevitable.

These limitations have a significant impact on document processing and, as a result, business operations and outcomes. For this reason, many organizations are looking for advanced document processing technologies to improve efficiency, accuracy, and security, extract valuable insights from the data, and automate workflows.

If you are interested in the evolution of document capture; here we have separately written an article about it.

Emerging technologies in document processing

Recent developments in document processing have focused on improving the accuracy and efficiency of these tasks through the use of artificial intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP). This allows deep learning models to be trained to recognize and extract specific types of data from documents with high accuracy. NLP can analyze the meaning and context of text to improve document classification.

The combination of these emerging technologies in document processing is what we mostly call Intelligent Document Processing (IDP) today. For a better overview, details and benefits of the technologies underlying modern IDP are:

1. Artificial Intelligence (AI): In document processing, AI is used to automate document classification, data extraction, and routing. In addition, AI can detect patterns and anomalies in large data sets.

The benefits of AI in document processing include:

  • Increased efficiency and accuracy
  • Lower labor costs and better scalability
  • Increased document security through automated classification
  • Improved decision making based on insights gained from documents
  • Improved compliance through automated auditing and tracking of document access and use

2. Machine Learning (ML) and Deep Learning: ML is a subset of AI, and deep learning in turn is a subset of ML. In document processing, ML and Deep Learning are used to train algorithms to recognize and classify patterns in document layouts and extract data from unstructured documents.

The benefits of ML and Deep Learning in document processing include:

  • Reduced labor costs through automation
  • Improved accuracy in document classification and data extraction from unstructured documents
  • Reduced need for manual intervention in document processing workflows
  • Improved decision making through insights derived from data sets

3. Natural Language Processing (NLP): NLP is a subset of AI in which machines are trained to understand and interpret human language. In document processing, NLP is used to classify documents based on their content, to extract meaning from documents, and to automate routing of documents based on their content.

The benefits of NLP in document processing include:

  • Lower labor costs through automation
  • Improved accuracy in document classification and content analysis
  • Improved routing of documents based on content and meaning
  • Improved decision making through insights gained from data sets

4. Computer Vision: Computer vision is an area of AI where machines are trained to interpret and understand visual data such as images and videos. In the field of document processing, computer vision is used to automate tasks in document processing workflows and for data extraction and classification.

The combination of these technologies united in one system as Intelligent Document Processing offer significant benefits to organizations: increased efficiency, better accuracy, easy scalability, cost savings, and better decision making with compliance are just a few.

For a detailed look at what Parashift does differently with Machine Learning in the document processing space, we recommend this episode of the IDP Podcast.

The future of document processing

future

The enormous progress in document processing over the last 3-5 years promises a lot for the future. Similarly at least, but probably at an even more rapid pace, the next five years are likely to be marked by significant advances in technology. Innovations in artificial intelligence and machine learning will continue to drive IDP capabilities. In addition, there are “new hype phenomena,” such as OpenAI’s ChatGPT lately, that IDP vendors will have to contend with. The future of document processing could include, but is not limited to:

Increased adoption of IDP: Generally speaking, more and more companies are recognizing the benefits of Intelligent Document Processing for their growth and successful digital transformation. This will mean increased adoption of this technology in the document processing space. This, in turn, will lead to more efficient and accurate document processing workflows, as well as increased scalability and cost savings for businesses.

Continued growth and investment: The IDP market is expected to continue to grow and investment in the technology is expected to increase. This will continue to drive innovation and development of IDP solutions, making them more powerful over time. In addition, investments will be needed to ensure that the AI models of the Intelligent Document Processing solution can keep pace with the complex use cases and their document quality.

Increased use of cloud-based solutions: Cloud-based IDP solutions will become more prevalent. This will allow organizations to access IDP capabilities without the need for on-premise hardware or software. This will increase the scalability and flexibility of IDP solutions and make them more accessible to small and medium-sized organizations.

Focus on document security and document analytics: As the amount of document data processed and stored continues to grow, an increased focus on document security can be expected. The same applies to document analytics and pre-fraud detection through early identification of anomalies and suspicious patterns.

Improved customer experience: Already today, Intelligent Document Processing can improve the speed and accuracy of customer-facing document processing tasks such as claims processing. These opportunities and positive impacts on the customer experience will only grow in the near future, with new use cases being added.

The combination of LLMs and IDP: Large Language Models (LLMs) like OpenAI’s ChatGPT have been on everyone’s lips for months. While the hype is obvious, LLMs also hold new opportunities and use cases when combined with Intelligent Document Processing. A variety of new use cases can be solved at the IDP level through generative AI. And again, more use cases are most certainly coming in the future for document processing.

The Parashift team is working daily on developing an API that processes all documents and returns the data without human intervention. The goal: a fully autonomous document extraction.

Challenges in document processing and how Parashift solves them

labyrint

As with any emerging technology, Intelligent Document Processing will face new challenges to consider in the coming years. Among them are the following challenges and how Parashift is solving them:

1. Data security and compliance: Document processing often involves the processing of sensitive data such as personal information. It is important to ensure that this data is protected from unauthorized access or use. As document processing technologies become more advanced and capable of extracting sensitive information, there will be increasing privacy and data protection concerns. There is a need to ensure that this data is protected from unauthorized access or use. In addition, document processing systems must be transparent and traceable.

How Parashift handles this challenge: Parashift’s modern cloud infrastructure is fully EU GDPR compliant. Customers worldwide use Parashift for their most sensitive data and documents. EU GDPR and data security are therefore two of Parashift’s most important features. The platform runs in ISO27001, ISO27017, ISO27110, ISO27018, SOC 1/2/3, PCI DSS, CSA STAR and HIPAA compliant data centers, making Parashift a secure cloud IDP provider. The cloud SaaS solution can guarantee a global service with the highest level of security and continuous software improvements.

2. Continuous improvements: It will be increasingly important in the near future for an IDP solution to learn and continuously improve as it operates. Only then will it be possible for companies to benefit from accurate data.

How Parashift handles this challenge: The core concept of the Parashift IDP platform is the proprietary Document Swarm Learning. The machine learning algorithms that drive the “swarm” train on billions of data points and are constantly learning: with every document, from every customer, and across every industry. To do this, document types are separated into individual fields, creating a global data network for unprecedented out-of-the-box capabilities. These shared learnings across document types, customers and industries ensure maximum efficiency. Document Swarm Learning enables organizations to automatically classify hundreds of document types and extract document data. This is unique in the IDP industry.

3. Simple user interfaces: Complex user interfaces are increasingly becoming a no-go. In the future, it will become more and more important that even employees with little or no programming experience can work with systems for automated document processing.

How Parashift handles this challenge: The Parashift IDP platform is built entirely on the no-code principle. This means that employees without specific know-how can work with the platform. Pre-trained and ready-to-use standard document types can be activated with one click. In addition, individual document types can be easily created. Thanks to the no-code approach, completely without the help of experts or developers.

4. Continuous development and innovation: It is imperative for IDP vendors to continuously develop and innovate their document processing solutions and provide new and better features to customers.

How Parashift handles this challenge: Parashift is committed to ongoing development to keep customers at the forefront of innovation in AI document processing. This is evident in the series of new features that Parashift recently announced and will gradually integrate into its IDP platform over the next six to 12 months. Among the new features are: fully EU GDPR compliant LLMs that leverage the previously mentioned Document Swarm Learning to provide a unique blend of radical innovation, privacy, and security (especially for banks and insurers to maintain high standards of information security and compliance). Through generative AI, for example, the summary of the document in question can be requested with one click or an interpretation of the context can be provided with one click (automatic recognition of the document type such as correspondence). In addition, Parashift opens up the integration of third-party AI APIs such as OpenAI for less compliance-heavy use cases. This allows customers to leverage a variety of AI-driven services in their existing document processing infrastructure – without having to deal with integration issues. In addition, the Parashift Marketplace will open very soon, giving enterprises access to a one-stop store for all their intelligent document processing needs. New automation capabilities such as the “If X then Y” logic engine, powerful scripting and calculation capabilities, and a developer component that enables viewer/validation/annotation capabilities in third-party applications complement the new features.

Analyst Quadrant Knowledge Solutions has positioned Parashift as a Leader in its SPARK Matrix™ Intelligent Document Processing 2022.

With the Parashift Intelligent Document Processing platform, the automation of your document processing is therefore in the best hands for both the present and the future.

Related Posts