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Document AI: Unlocking Real-Time Intelligent Information from Unstructured Documents for Improved Decision-Making

April 12, 2022


Data is the lifeblood for enterprises today, especially when it comes to making strategic decisions in a complex business environment. But, the majority of data is unstructured and present in various formats and types, from documents and emails to images, making it difficult to categorize, segregate, and analyze.

Businesses have realized that having data just for its sake is no longer sufficient. Extracting insights and making them accessible for decision-making can help enterprises unlock hidden business value at scale. Hence, AI-based data extracting solutions like Document AI is becoming the need of the hour.

What is Document AI?

Document AI or Document Intelligence uses AI technology to collate unstructured data from various documents, structure them into readily consumable information, and generate data for analysis as and when needed.

Unlike humans, AI Document Processing can easily extract granular information or capture subtle nuances in the sources often overlooked by humans. Since the entire process of data extraction and processing completes automatically in nanoseconds, it is possible to gain real-time insight into existing processes without disrupting the workflow.

Understanding the Importance of Document AI

Enterprises deal with many company documents, PDFs, printouts, emails, messages, invoices, and others daily. Each of these documents is a powerhouse of data, carrying valuable information. But, extracting insights from them is both time and labor-intensive.

There are four key challenges enterprises face when extracting and processing data from documents:

Huge volume of documents to be processed: Document volume is posing a significant challenge for businesses. Especially when unprocessed documents hide valuable insights, the pressure to extract them from millions of documents builds upon human employees when done manually. Hence, most information extracted is often contaminated by biased judgments or incomplete because granular data remain hidden.

Variety of data formats: Adding to the volume is the variety of formats used in sharing company information. When volume can be easily handled with technology, extracting data from multiple formats becomes challenging.

The veracity of data in documents: As mentioned earlier, unstructured documents are mostly error-prone. Data entered wrongfully, valuable data skipped during manual entries, and even unreadable sections showing up in the extracted information can render the insights undependable. Such errors in certain documents like contracts can result in legal or compliance issues. Manual verifying every document is not feasible in terms of time, hindering on-time data availability.

The velocity at which documents need processing: Companies need to access real-time data during decision-making instantly. However, mapping and processing large volumes of documents in a short span is challenging for the human resources involved. Businesses need faster time to value, but the processing speed of existing disjointed solutions doesn’t solve this challenge.

How can Document AI Help Overcome the Challenges of Volume, Variety, Veracity, and Velocity?

Businesses need document processing automation to successfully unlock business value from enterprise documents when needed, irrespective of document complexity or domain specificity. Only Document AI-powered by NLP, Computer Vision, Deep Learning, and Machine Learning can easily overcome the challenges mentioned above and convert unstructured data into a structured format using content classification, entity extraction, and advanced searching.

Here are a few use cases of Document AI:

Document volumes in the financial services industry

KYC is an integral part of every banking service, which involves manually extracting data from documents in multiple formats and originating from multiple sources. This process is highly effort-intensive when we consider millions of documents processed at about the same time. Hence, the cycle time for onboarding new account owners increases. With its supervised and unsupervised learning capabilities, Document AI can help banks streamline the document and effort-intensive KYC process. Further, this technology helps banks to realize faster turnaround times and increased compliance.

Document variety for healthcare payers

Healthcare insurers receive thousands of claims requests every day. Requests are usually in different documents, images, or PDFs. And images are usually of poor and inconsistent quality. Processing these claim requests involve too much labor. It is both time and resource-intensive and often leaves behind a trail of human error. Document AI digitizes claim request documents by extracting required data and processing images for quality enhancement using vision capabilities.

Document veracity for auditing firms

Similarly, accounting firms handle bulk financial documents to assess the financial soundness of companies. It is a part of due diligence. However, the documents are processed manually, and extracted content is circled, validated against sources of truth, and audited/unaudited documents. Document AI uses computer vision, NLP, and intent-based entity extraction capabilities to automate the extraction process and highlight discrepancies between submitted documents and sources of truth. Automation accelerates activities such as generating comfort letters for companies under scrutiny.

Velocity in document processing for procurement teams

Procurement contract management is resource-intensive, especially for large organizations. With thousands of new contracts added every month, a historic load of over thousands of contracts takes time to process. Unfortunately, globally executed contracts like LIBOR come in multiple types and formats and include critical terms and clauses related to local regulations and third-party suppliers. Extracting such critical clauses manually and comparing the language with standard templates is an inefficient approach translating into more risks. Document AI’s unsupervised clustering extract terms and clauses and determine the potential risks of their contracts and suppliers quickly and accurately. Hence, contract cycle time is highly reduced, and negotiation of minimal operational risk is improved.

The Future of Document AI

Companies are increasingly looking to unlock hidden value from documents and have intelligent information at their fingertips in near real-time. However, the legacy system of manual document processing can never reflect an accurate picture of the processes, not to mention the errors that remain behind. But, with the help of Document AI, such needs are easily met, irrespective of ever-evolving document types and volumes.

However, adopting new technology should not mean upending your existing systems from the ground up every time. The focus should be more on finding solutions that can seamlessly plug and play into existing enterprise systems.

EdgeVerve’s Document AI solutions XtractEdge Platform and XtractEdge Contract Analysis, help businesses unlock the value hidden in complex documents and streamline decision-making.

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