Abstract: A system implements a method for managing claim integrity in healthcare revenue cycle management, collecting a dataset from electronic health records and payer records that contain historical medical claims with approval and denial outcomes. Machine learning engines train models using patterns in claim denial outcomes. The system receives data in real time, including medical claims awaiting submission. Machine learning engines process each claim by extracting features, generating denial probability scores and denial category classifications, and providing explainability values through SHAP analysis. For claims with a denial probability above a specified threshold, the system identifies corrective actions, automates corrections via automation agents, and recomputes the claim scores.