Leveraging Machine Learning, Cloud Computing, and Artificial Intelligence for Fraud Detection and Prevention in Insurance: A Scalable Approach to Data-Driven Insights

Abstract

This paper aims to establish an understanding of how developments in technology have affected insurance fraud detection and control. This paper discusses the applicability of combining ML, cloud environment and AI to build flexible and effective fraud discovery systems. The existing strategies for fraud detection and prevention may have a weakness with the amount, variety and real-time nature of data. This paper proposes a detailed framework to improve the effectiveness of fraud detection with the help of ML algorithms for accurate prediction models, AI for decision automation support, and cloud computing for future expansion. It will be clear from the above results that enhanced detection accuracy, operations efficiency and compliance to set legal standards have been attained. This research work’s objective is to present recommendations for insurers interested in preventing fraud while keeping the antidote affordable and easily soluble in large volumes.