AI-Driven Claims Adjudication: Optimizing Healthcare Systems with Machine Learning and Deep Neural Networks

Abstract

Healthcare claims adjudication is an important administrative process that affects the accuracy and efficiency of reimbursements in healthcare systems and prevents fraud. Growing volumes of claims, challenging billing codes, undocumented clinical records, and sophisticated fraud schemes present weaknesses in rule-based and manual adjudication. This study introduces an AI-based claims adjudication system that combines transformer-based natural language processing (NLP), ensemble machine learning, and a two-step fraud detection pipeline. The system examines the structured attributes of claims, utilization, profile of provider behavior, and contextual embeddings of clinical text to automatically perform adjudication with great accuracy and transparency. Claim classification is performed using a weighted combination of XGBoost, Random Forest, and neural network classifiers, and fraud detection is performed using a hybrid model of unsupervised anomaly detection and supervised learning. On large-scale healthcare data, experimental analysis reveals 94.7% classification accuracy, 67 percent processing time reduction, and 37 percent better fraud detection results than rule-based systems. The interpretability, regulatory compliance, computational complexity, and deployment considerations discussed in this study make the framework a scalable platform for next-generation healthcare claims processing.

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