Author
Abstract
The convergence of Artificial Intelligence, Machine Learning, and Big Data with cloud technology is fundamentally reshaping the insurance industry landscape. These technologies drive automation, predictive insights, and personalized customer experiences—essential factors for success in modern insurance markets. Cloud-based platforms enable insurers to harness these capabilities through scalable architectures that support sophisticated analytics workflows. This technical examination explores how these technologies are reengineering core insurance operations, from underwriting algorithms to claims processing systems, with particular focus on Guidewire's enterprise implementations. The integration transforms underwriting by enhancing risk prediction accuracy, enabling dynamic pricing models that adjust premiums based on real-time behavioral data. Claims processing benefits from extensive automation while maintaining accuracy, significantly reducing settlement times and improving customer satisfaction. Fraud detection capabilities expand substantially through anomaly detection techniques, including isolation forests, autoencoder neural networks, and graph analysis to identify organized fraud rings. The technical foundation supporting these advancements includes distributed computing frameworks, containerization, API-first architectures, and microservices design patterns that collectively enable the processing of massive heterogeneous data volumes while maintaining regulatory compliance.
Suggested Citation
Chandra Prakash Kathroju, 2025.
"Transforming Insurance: The Technical Convergence of AI, ML, and Big Data in Cloud Platforms,"
Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 7(3), pages 946-956, April.
Handle:
RePEc:alk:jcsts0:v:7:y:2025:i:3:id:9697
DOI: 10.32996/jcsts.2025.7.3.106
Download full text from publisher
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:alk:jcsts0:v:7:y:2025:i:3:id:9697. 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: Al-Kindi Center for Research and Development (email available below). General contact details of provider: https://al-kindipublisher.com/index.php/jcsts .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.