Utilizing Machine Learning for Financial Management in Healthcare

Abstract

In healthcare, financial management is an effective tool that ensures that the cost of healthcare is efficient, fraud free and resource allocation is optimally made. In this research we discuss the usage of machine learning (ML) algorithms in financial decision making in healthcare, especially with regard to risk assessment, fraud detection, cost prediction, and claims management in insurance. Real world financial data is used to implement and evaluate four ML models: Linear Regression, Random Forest, KMeans Clustering and Support Vector Machines (SVM). The results show that in healthcare cost prediction, Linear Regression’s technique was able to achieve 89.6% accuracy in cost prediction and so very likely to be accurate enough for precise expenditure forecasting if the distribution of the cost remains very similar to this. Traditional rule-based systems were outperformed by Random Forest in detecting fraudulent claims with a 94.3% accuracy. Financial transactions were successfully grouped by K-Means Clustering with a silhouette score of 0.78 as well as improving risk analysis, while SVM performed well with an accuracy of 87.5% improving the process of approving insurance claims and reducing delays. The proposed approach significantly improves accuracy than existing financial models in terms of prediction, minimizes financial risks, and is more efficient in operation. Further by pursuing the study of data privacy issues and regulatory challenges, this work takes care of ethical ML implementation. To enhance financial security of healthcare further, future work should be carried on scalability, federated learning, and AI driven financial automation.