Detection using Graph Learning, Ensemble Models, and Explainable AI
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Abstract
Healthcare fraud detection is a critical study topic that identifies fraudulent claims, unusual billing activity, and financial abnormalities in healthcare systems. Graph learning, ensemble-based methods, and explainable AI (XAI) are all evaluated in this paper as potential ways to enhance fraud detection. There are a number of ways to increase prediction performance: ensemble approaches use several learning algorithms, graph learning techniques assist find connections between patients, healthcare providers, and claims. Methods for identifying intricate patterns of fraud are covered in the paper, including ensemble tactics such as bagging, boosting, and stacking. The article delves into XAI approaches such as SHAP, LIME, feature significance, and counterfactual explanations to help readers better comprehend the model's predictions. Integration of diverse healthcare data, data quality, class inequity, privacy protection, and the number of false records is all major obstacles. The review goes beyond only looking at accuracy when evaluating models; it takes into account variables including recall, precision, AUC-ROC, PR-AUC, and F1-score. The capacity to handle data reliably, efficiently, and react to new fraud tactics is essential for practical implementation. Looking at these methods and the problems they face, this paper shows how XAI, ensemble methods, and graph learning may help build a healthcare fraud detection system that is more accurate, transparent, and reliable.
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