Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{171735,
        author = {Bharat Hegde and Praveen N J and Pratanu Dolui and Deepak Khimavath B B and Shreya P},
        title = {Insurance Risk Prediction Using Quantum Computing and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {841-846},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171735},
        abstract = {Integrating quantum computing in insurance risk forecasting. Address the computational challenges of large dice sets by combining Quantum Support Vector Machines (QSVM) and advanced quantum algorithms with advanced machine learning models. This hybrid structure thus improves the accuracy and efficiency of insurance risk assessment. Dramatically increases the scalability and reliability of underwriting and claims assessment. We have developed a web-based insurance management system using Flask to streamline auto and health insurance workflows. This secure and easy-to-use platform manages insurance information. Calculate insurance premium and manage policy prices With session-based authentication and batch processing The system guarantees information security and operational efficiency. Integrating quantum risk prediction models will help reduce errors and improve service delivery. Its aim is to revolutionize insurance operations. increase credibility Scalability and user satisfaction},
        keywords = {Quantum Computing, Insurance Risk Prediction, Quantum Support Vector Machines (QSVM), Hybrid Quantum-Classical Framework, Risk Assessment},
        month = {January},
        }