INTENSIFYING THE LEGITIMATE AND MEDICAL CLAIMS USING SOPHISTICATED NLP TECHNIQUES
Keywords:
Natural Language Processing, Medical Claims, Insurance Fraud Detection, BERT, BioBERT, Named Entity Recognition, Semantic Analysis, Claim Validation, Healthcare AIAbstract
Efficient and accurate processing of medical and insurance claims remains a critical challenge in the healthcare sector. This research presents a Natural Language Processing (NLP)-based approach to enhance the identification and validation of legitimate medical claims. By utilizing advanced techniques such as Named Entity Recognition (NER), transformer-based language models (e.g., BERT, BioBERT), and semantic analysis, the system can extract relevant medical information, assess claim legitimacy, and reduce processing delays. A credibility scoring mechanism, trained on real and synthetic data, further strengthens decision-making by ranking claims based on medical coherence and historical trends. Experimental results show improved accuracy and speed over traditional methods, highlighting the potential for AI-driven claim adjudication in real-world applications.
