The integrity and financial stability of healthcare systems around the world are seriously threatened by healthcare fraud. It includes dishonest tactics including upcoding treatments to more costly alternatives, invoicing for services that were never provided, and falsifying patient diagnoses. These dishonest practices compromise the standard and reliability of patient care in addition to causing large financial losses. Conventional machine learning models and rule-based techniques are two examples of traditional fraud detection systems that frequently fail to adequately handle this issue. Low accuracy and a high frequency of false positives and false negatives are the results of their limited adaptability, reliance on manually set rules, and incapacity to manage the complexity of real-world data. This research suggests a novel Deep Attention Network (DAN)-based fraud detection approach to get around these restrictions. The model analyses complicated and high-dimensional healthcare claim data by utilizing sophisticated deep learning approaches, including attention processes. By doing this, it improves the accuracy and dependability of fraud detection by learning to recognize and rank the most pertinent characteristics linked to fraudulent activity. This clever technology can lessen the workload associated with manual intervention and adjust to changing fraud strategies. The ultimate goal of the suggested Deep Attention Network is to offer a precise and scalable solution that improves the security, effectiveness, and transparency of healthcare systems.