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@article{162007,
        author = {Pannala Akshaya and Akshay Deep and T.Akshay Goud and Akshaya Sadrollu and S.Akshaya and Akhil Goud and Dr.Gifta jerith},
        title = {Insurance Fraud Claim Detection Using Machine Learning },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {7},
        pages = {185-187},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162007},
        abstract = {Insurance fraud poses a significant challenge for both insurance companies and society as a whole. As fraudulent activities continue to evolve in sophistication, traditional methods of fraud detection are becoming increasingly insufficient.     Leveraging the power of machine learning (ML) techniques has emerged as a promising solution to combat fraudulent claims efficiently and effectively. This research paper aims to present an innovative framework that utilizes advanced ML algorithms for the detection and prevention of insurance fraud.
The proposed framework integrates various machine learning methodologies, including but not limited to supervised learning, unsupervised learning, and anomaly detection techniques. Data preprocessing techniques such as feature engineering, dimensionality reduction, and data balancing methods are applied to optimize the model's performance. Additionally, the utilization of ensemble learning models and deep learning architectures enhances the system's ability to identify complex fraudulent patterns within insurance claims data. Moreover, the research investigates diverse datasets encompassing different types of insurance claims, including health, auto, property, and casualty insurance. The evaluation metrics employed to assess the models' performance encompass precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) to ensure robustness and reliability.
},
        keywords = {Insurance fraud detection, Logistic Regression, Machine Learning Algorithms, Support Vector Machines, Random Forest, Decision Tree, K-Nearest Neighbours, Unsupervised learning, Supervised Learning, Detection Accuracy,  Deep Learning  Approaches.},
        month = {},
        }