Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sehajpal, Sabina; Kaur, Ravneet; Singh, Ajay; Bhatnagar, Mukul Title A name given to the resource Leveraging Big Data and AI for Optimizing Health Insurance Claims and Risk Assessment in Healthcare Financing Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1464 LNNS;pp.405-413 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-7520-3_34" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-7520-3_34</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020243239?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020243239?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sehajpal S., Chandigarh University, Punjab, Chandigarh, India; Kaur R., Chandigarh University, Punjab, Chandigarh, India; Singh A., Christ University, Karnataka, Bengaluru, India; Bhatnagar M., Graphic Era Deemed to be University, Dehradun, India Description An account of the resource This research elucidates the transformative potential of big data analytics and artificial intelligence in optimizing health insurance claims and risk assessment by employing an empirically robust framework encompassing reliability and validity metrics, Heterotrait-Monotrait Ratio (HTMT) analysis, and bootstrapping to unravel the intricate interdependencies among constructs such as AI model accuracy, claims processing efficiency, cost efficiency, data quality, fraud detection accuracy, system usability, and user trust interface, thereby advancing a comprehensive understanding of the systemic synergies that enhance predictive precision, operational scalability, and equitable resource allocation within the healthcare financing paradigm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Data interoperability; Digital transformation; Ethical AI; Healthcare equity; Operational efficiency; Predictive modeling; Stakeholder trust; System optimization Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981967519-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online