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    EXL's AI on AWS Cuts Medical Claims Review Time Significantly

    EXL's new AI-powered Medical IDP solution revolutionizes the insurance claims process, transforming the review of complex medical records into an automated, efficient workflow. Discover how this technology enhances accuracy and expedites claim settlements.

    aws.amazon.com•September 21, 2026•2 min read

    Key Facts

    • EXL's AI reduces claims review time from days to hours, enhancing operational efficiency significantly.
    • Fine-tuned Insurance LLM outperforms general models, providing a competitive edge in claims processing.
    • Automated workflows cut costs, potentially increasing profit margins for insurers by reducing manual labor.
    • Integration with AWS ensures scalability, allowing EXL to handle fluctuating claim volumes effectively.
    • Improved accuracy and compliance mitigate regulatory risks, safeguarding insurers against costly penalties.

    Summary

    Summary

    EXL deployed an AI-powered Medical Intelligent Document Processing (IDP) solution on AWS to streamline the review of medical claims. The solution addressed the challenges of complex and voluminous medical records, reducing review time from days to hours while maintaining high accuracy. This transformation significantly improved operational efficiency for insurance claims adjusters.

    Background

    EXL is a data analytics, AI, and digital solutions provider with over 50,000 professionals globally, serving Fortune 500 organizations across various industries, including insurance and healthcare. Prior to the deployment of the AI solution, insurance claims adjusters spent over 100 minutes per case manually reviewing extensive medical records, which were often several hundred pages long and filled with complex, unstructured information.

    Challenge

    The primary challenge was the slow and inconsistent manual review process of medical records, which are critical for insurance claims adjudication and life underwriting. The complexity of these records, filled with specialized clinical terminology and disparate data points, led to delayed claim settlements, accuracy issues, and increased operational costs.

    Solution

    EXL implemented a two-part AI solution hosted on AWS, combining Xtrakto.AI for document processing and the EXL Insurance LLM for domain intelligence. The solution automates the ingestion, classification, extraction, and summarization of medical documents. It utilizes advanced technologies such as computer vision and natural language processing, enabling claims adjusters to access actionable insights quickly. The architecture includes various AWS services, ensuring secure and efficient processing.

    Results

    The implementation of the EXL Medical IDP solution reduced the medical record review time from days to hours. This significant decrease in processing time allowed claims adjusters to handle more cases efficiently, improving overall productivity and operational throughput.

    Key Insights

    Organizations facing similar challenges with manual document processing can benefit from integrating AI solutions that automate data extraction and summarization. Leveraging domain-specific models can enhance accuracy and efficiency in specialized workflows.

    Customer Testimonial

    No direct quotes are available from the source material.

    Entities Mentioned

    Companies

    EXL
    Amazon

    Products

    EXL Medical IDP
    Xtrakto.AI
    EXL Insurance LLM

    Technologies

    Artificial Intelligence
    Natural Language Processing
    Computer Vision
    Optical Character Recognition
    Machine Learning

    Organizations

    Fortune 500

    Key Concepts

    Medical claims review
    Intelligent document processing
    Large language models
    Data extraction
    AI in healthcare
    Document classification
    Automated summarization
    Regulatory compliance

    Definitions

    Intelligent Document Processing (IDP)
    A technology that uses AI to automate the extraction and processing of information from documents.
    Large Language Models (LLMs)
    AI models that are trained on vast amounts of text data to understand and generate human-like text.
    Natural Language Processing (NLP)
    A field of AI that focuses on the interaction between computers and humans through natural language.
    Optical Character Recognition (OCR)
    A technology that converts different types of documents, such as scanned paper documents or PDFs, into editable and searchable data.
    Parameter-Efficient Fine-Tuning (PEFT)
    A method of adapting a pre-trained model to a specific task with minimal adjustments to its parameters.

    Use Cases

    • →Automating medical claims review
    • →Generating clinical summaries
    • →Extracting structured data from medical documents
    • →Validating medical codes
    • →Improving operational efficiency in healthcare
    • →Enhancing patient care management

    Frequently Asked Questions

    What is the EXL Medical IDP solution?

    The EXL Medical IDP solution is an AI-powered system designed to automate the review of medical claims, significantly reducing the time required for claims adjusters to process medical records.

    How does the solution improve the claims review process?

    By utilizing intelligent document processing and large language models, the solution extracts and summarizes critical medical information, allowing adjusters to make quicker and more accurate decisions.

    What technologies are used in the EXL Medical IDP solution?

    The solution employs various technologies, including artificial intelligence, natural language processing, computer vision, and optical character recognition, all hosted on AWS.

    What are the benefits of using AI in medical claims processing?

    AI enhances efficiency by automating data extraction and validation, reducing manual effort, improving accuracy, and enabling faster claim settlements, which ultimately leads to better customer experiences.

    How does the solution ensure compliance with healthcare regulations?

    The solution incorporates responsible AI controls, including data de-identification and source-level traceability, to ensure that all outputs comply with healthcare regulations like HIPAA.

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