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@article{195101,
        author = {Medishetty Satvika and Ravula Soumya and Thoutam Khushi and Kalyani Priyanka},
        title = {AI-Powered Insurance Claim Fraud Detection Automobiles (Car) Accidents},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {7419-7424},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195101},
        abstract = {Insurance fraud results in a huge financial loss for insurance companies every year, which creates a need for developing an efficient system using advanced technologies to identify insurance fraud effectively. This leads to a huge financial loss for insurance firms. The system works by assessing insurance claims based on various parameters such as age of claimants, amount of claim, and time of incident to calculate a score that is required to assess the level of fraud. Random Forest models are efficient in detecting fraudulent insurance claims using supervised learning algorithms with high accuracy. The insurance fraud detection system is developed using React.js to create a dynamic user interface, Node.js/Flask to create efficient backends, and MySQL to manage data efficiently in a full-stack architecture. The experimental results show that using ML models, accuracy in insurance fraud detection is increased and automated decisions in claim processing are enhanced.},
        keywords = {Standard in the field include insurance fraud detection, ML models, Random Forest, supervised classification, and claim risk evaluation methodologies},
        month = {March},
        }