Utilizing digital footprint analysis for end-to-end risk-based authentication in medical billing systems

Healthcare systems face escalating challenges in securing sensitive financial and patient data due to sophisticated fraud tactics and unauthorized access. This study presents a dynamic risk-based authentication (RBA) framework that leverages digital footprint analysis, including behavior monitoring, device recognition, and location-based anomaly detection, to strengthen security. The framework utilizes machine learning models, Isolation Forest for outlier detection, and recurrent neural networks for sequential behavior analysis, to assess risk in real-time, dynamically adjusting authentication requirements based on risk profiles. Privacy-preserving technologies such as homomorphic encryption and federated learning are integrated to comply with HIPAA and GDPR standards, ensuring secure data handling without centralization. Findings show that the proposed RBA framework effectively reduces false positives, improves detection accuracy, and provides a scalable solution for securing medical billing systems. This adaptive approach supports both user experience and stringent privacy compliance, laying the groundwork for more resilient healthcare data security systems. Future studies could extend this framework by incorporating blockchain to enhance data transparency and auditability across transactions.