Applications of AI in Document Management

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Pavan Nanjundaiah
Pavan Nanjundaiah
Head of Solutions

We are drowning in information, but starved for knowledge

This is a famous quote by John Naisbitt which shows the key difference between information and knowledge. Advancement in data engineering techniques and cloud computing have made it easy to generate data from multiple sources but making sense of this data and getting insights is still a huge challenge. The data volumes have now increased exponentially and along with the traditional structured data, data can now reside in different formats like unstructured social media text, log files, audio/video files, streaming sensor data etc.

Applying manual methods to process this diverse data is not only time consuming and expensive but is also prone to errors. Hence the need of the hour is to use Artificial Intelligence (AI) based automated solutions that can deliver reliable insights and also give a competitive advantage to customers. Here are few examples of how customers across industries can benefit from AI driven solutions.

Microsoft Azure based AI solution

In 2017, more than 34,000 documents related to John F Kennedy’s assassination were released. The data volume was huge, and data existed in different formats like reference documents, scanned PDF files, hand written notes and images. It would take researchers months to read through this information and hence manually reviewing this data was not the most optimal solution. Microsoft Azure team applied AI based Cognitive Search solution to extract data from these diverse sources and gained insights. Technical architecture for this use case was built using Azure Cognitive Services components like Computer Vision, Face Detection, OCR, Handwriting Recognition, Search and core Azure components like Blob Storage, Azure ML, Azure Functions and Cosmos Database. This solution also annotated text using custom CIA Cryptonyms.

Hospitals usually deal with a lot of patient data which could reside in electronic medical records (EMR), handwritten prescriptions, diagnostic reports and scanned images. AI based Azure Cognitive Search could be an ideal solution to efficiently manage patient’s medical records and create personalized treatment plan. Many downstream use cases like Digital Consultations, Virtual Nurses and Precision Medication can be built once the patient data is optimally stored.

Google Cloud Platform (GCP) based AI solution

GCP introduced Document Understanding AI (beta) in Cloud Next 19. This is a serverless platform that can automate document processing workflows by processing data stored in different formats and building relationships between them. This solution uses GCP’s vision API, AutoML, machine learning based classification, OCR to process image data and custom knowledge graph to store and visualize the results. Customers can easily integrate this solution with downstream applications like chatbot, voice assistants and traditional BI to better understand their data.

Customers who deal with Contract Management data like Mortgages are usually faced with a lot of manual tasks to ensure that the contracts are complete and accurate. This could mean processing contracts in different formats/languages, reviewing the supporting documents, ensuring that the details are accurate and complies with regulatory standards across documents. By using Document Understanding AI and integrating it with a well-designed RPA framework, customers will be able to efficiently process Mortgage applications, Contracts, Invoices/Receipts, Claims, Underwriting and Credit Reports.

Use cases from other industries

Document Management AI solution can also be applied to diverse use cases from other industries like processing claims related to damages to shipped products by e-commerce companies, handling know your customer (KYC) process in the banking industry, invoice data processing by Finance teams, fraud detection during document processing etc.

As more and more companies embrace the digitization wave, they will be faced with different variations of data/document management challenges. Based on the current trend, number of use cases are only going to increase and an AI driven solution is probably the most efficient way to solve this problem as it can reduce manual work, save cost and deliver reliable insights. This will ensure that companies can spend more time on building their business and less time on manually processing documents and data preparation.

Going back to John Naisbitt’s quote, AI and ML driven solutions are probably the only way to bridge the gap between information and knowledge.

1 Comment

  1. Pratap Daruka

    How can wevuse these for SoW and MSA management.

    I am writing here to see if this communication channel is effective or not.


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