IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v11y2025i1id963.html

The Future of Insurance Technology: Leveraging AI for Transformation in Property and Casualty

Author

Listed:
  • Naveen Kondeti

Abstract

This article explores the transformative impact of Artificial Intelligence (AI) on the property and casualty (P&C) insurance sector, focusing on key technological advancements reshaping core insurance operations. This article examines how AI-driven solutions are revolutionizing traditional underwriting processes, enhancing claims management efficiency, and strengthening fraud detection capabilities. Through an analysis of cloud-based platforms like Guidewire, it investigates the integration challenges and opportunities in implementing AI solutions within existing insurance infrastructure. It highlights how machine learning models, computer vision, and predictive analytics are enabling insurers to achieve more accurate risk assessment, automated claims processing, and sophisticated fraud prevention. It also suggests that the successful adoption of AI technologies in P&C insurance not only improves operational efficiency but also enables insurers to deliver more personalized products and enhanced customer experiences. The article concludes by examining emerging trends and providing strategic recommendations for insurers navigating this technological transformation, emphasizing the critical balance between innovation and regulatory compliance in the evolving insurance landscape.

Suggested Citation

  • Naveen Kondeti, 2025. "The Future of Insurance Technology: Leveraging AI for Transformation in Property and Casualty," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(1), pages 2919-2926, February.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i1:id:963
    DOI: 10.32628/CSEIT251112295
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251112295
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT251112295
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/home/article/download/CSEIT251112295/CSEIT251112295
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/CSEIT251112295?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jbh:ijsrcs:v11:y2025:i1:id:963. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.