A Novel Approach of Streamlining Claims Processing and Fraud Prevention in Health Insurance through Blockchain Technology
C.M. Selvamuthu, B. Lavaraju, Asha Sundaram · 2024
The increasing complexity of health insurance claims processing and the rising incidence of fraud necessitate the development of advanced systems that can streamline operations while enhancing fraud detection capabilities. This study proposes a novel blockchain-based system integrated with advanced artificial intelligence (AI) and machine learning techniques to address these challenges. The proposed model is evaluated against nine existing models, including K-Nearest Neighbors (KNN), Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine (SVM), AdaBoost, Gradient Boosting Machine (GBM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). The results demonstrate the superiority of the proposed model, which achieved an accuracy of 97.80%, significantly outperforming the best-performing existing model with an accuracy of 94.50%. Additionally, the proposed model performs well and results in precision (96.90%), recall (97.50%), and F1-score (97.20%), ensuring both high detection rates and minimal false positives. The integration of blockchain technology not only enhances the transparency and security of the claims process but also reduces processing time to 390 ms, the fastest among all models tested. This research highlights the potential of combining blockchain with AI to revolutionize the health insurance industry, offering a robust and efficient solution for claims processing and fraud prevention.