Advanced Data Analytics and Machine Learning Driven Fraud Detection and Data Loss Prevention for Automated Incident Response in the US Healthcare Corporations

Abstract

Fraud detection and data loss prevention are critical challenges facing US healthcare corporations as they strive to safeguard sensitive patient information and comply with stringent data protection regulations. The integration of advanced data analytics and machine learning has emerged as a powerful approach to enhance the efficiency and accuracy of detecting fraudulent activities and preventing data breaches. This study explores the application of machine learning-driven solutions in automating incident response for healthcare data security. The research begins by examining the current landscape of data analytics and machine learning in fraud detection, emphasizing the limitations of traditional methods. Through an extensive literature review and analysis of case studies within the US healthcare industry, the paper identifies key areas where advanced technologies can bridge existing gaps. The methods section outlines the data collection process, the machine learning algorithms implemented, and the evaluation metrics used to measure model performance. Results demonstrate the enhanced detection accuracy and prompt response capabilities of machine learning models compared to conventional techniques. The discussion delves into the implications of these findings, showcasing the transformative potential of automated incident response systems in reducing response times and mitigating data loss risks. Although promising, the study acknowledges limitations in data variability and model generalizability, suggesting avenues for further research. The paper concludes with strategic recommendations for adopting machine learning solutions in healthcare security protocols. By highlighting best practices and policy recommendations, this research aims to provide a roadmap for healthcare corporations seeking to strengthen their data protection frameworks. The insights presented underscore the pivotal role of advanced data analytics and machine learning in fortifying healthcare data security against evolving cyber threats.