Financial Fraud Detection in Healthcare Usingmachine Learning and Deep Learning Techniques

In the healthcare sector, financial fraud detection plays a critical role in safeguarding public funds and preserving the quality of healthcare services.The intricacy and volume of contemporary fraudulent schemes can outweigh the capabilities of traditional approaches.In an effort to enhance fraud detection, this study investigates the application of deep learning (DL) and machine learning (ML) techniques.Thus work shows notable gains in identifying fraudulent activity by utilizing algorithms such as logistic regression, decision trees, support vector machines, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) and analyzing massive datasets.Particularly, the Decision Tree Classifier's 99.9% accuracy rate demonstrated the ability of ML models to reliably discern between cases that are fraudulent and those that are not.This study highlights how sophisticated ML and DL methods can improve the accuracy and efficacy of fraud detection systems in the healthcare industry, which will ultimately lead to a more sustainable and egalitarian healthcare system.