CLOUD-POWERED PREDICTIVE ANALYTICS IN INSURANCE: ADVANCING RISK ASSESSMENT THROUGH AI INTEGRATION

This article examines the transformative impact of predictive analytics, powered by cloud computing and artificial intelligence (AI), on risk assessment practices in the insurance industry. Through a comprehensive analysis of data from multiple insurance providers, we investigate how these technologies enhance the accuracy of risk predictions, improve customer segmentation, and enable proactive claims management. Our findings demonstrate a significant improvement in underwriting precision, with a 20% reduction in fraudulent claims and a 15% increase in customer retention rates among early adopters. However, the integration of these advanced analytics poses challenges, particularly in data privacy, legacy system compatibility, and model interpretability. We propose a framework for addressing these issues, emphasizing the need for robust cybersecurity measures, flexible API solutions, and the use of explainable AI techniques such as SHAP (Shapley Additive Explanations). The article concludes by outlining future trends, including the integration of Internet of Things (IoT) data and the development of more sophisticated AI models, providing strategic recommendations for insurers to leverage these technologies effectively. This article contributes to the growing body of literature on digital transformation in insurance, offering insights for both practitioners and researchers in the field of actuarial science and data analytics.