A QUANTITATIVE ANALYSIS OF HEALTHCARE FRAUD AND UTILIZATION OF AI FOR MITIGATION

Abstract

Healthcare fraud is an emerging and prevalent problem that threatens the reputability of the healthcare system, leading to significant financial charges and disrupting the patient's care. Conventional fraud prevention techniques include manual audits and rule-based systems, which are no longer adequate in the contempt of sophisticated fraud schemes. The advent of advanced technologies like Artificial Intelligence contributes to new opportunities to confront healthcare fraud more effectively. AI-powered solutions include voice biometrics and scrutinizing distinctive identifiers like patterning voice to detect fraudulent activities with greater efficiency and accuracy in contrast to conventional methods. By leveraging Machine Learning algorithms, these systems could incessantly detect fraud patterns and curtail the risk of false positives, improving the overall effectiveness of fraud detection. The research has attempted to exemplify AI implementation in providing accessibility and availability for reliance aid in the healthcare system by gauging its effectiveness in fraud detection. The research presents a comprehensive quantitative scrutiny of AI facilitation over the healthcare system's security threats for mitigation. Since building large-scale labelled Medicare datasets, a data-centric approach empowers healthcare providers to reduce paperwork and time-consuming settlements for policyholders. The present research outcome has proven that applying AI-based fraud mitigation strategies could significantly influence the healthcare industry through quantitative analysis. Hence, by enhancing and automating fraud detection capabilities, healthcare organizations could maintain their capital resources, protect patient data, and retain public reliance. Moreover, the proposed results highlight AI's potential to transmit the prospect of healthcare fraud prevention, facilitating a more efficient and secure healthcare system.