Automated Insurance Claim Classification Using Natural Language Processing Techniques
Keywords:
Insurance claims, NLP, Transformer embeddings, Machine learning, Deep learning, Text preprocessing, Feature extraction, Automated classification, Fraud detectionAbstract
Automated insurance claim classification has become essential for improving operational efficiency and accuracy in the insurance industry. Traditional manual methods of processing claims are time-consuming, inconsistent, and prone to human error. This research proposes a comprehensive framework utilizing Natural Language Processing (NLP) techniques to classify insurance claims effectively. The proposed methodology integrates systematic text preprocessing, contextual feature representation using transformer-based embeddings, and supervised classification models. Experimental evaluation compares traditional machine learning models, recurrent neural networks, and transformer-based classifiers across standard performance metrics including accuracy, precision, recall, and F1-score. Results indicate that transformer-based architectures significantly outperform other models, providing superior contextual understanding and handling complex claim narratives. The study also addresses class imbalance, model explainability, and deployment considerations, ensuring applicability in real-world insurance workflows. The proposed framework demonstrates scalability, robustness, and alignment with regulatory requirements, establishing a strong foundation for future advancements in AI-driven insurance automation.
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References
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