Engineering and Technology
| Open Access | Data-Driven Risk Intelligence: Harnessing Predictive Analytics, Iot And Machine Learning For Next-Generation Insurance Underwriting And Claims Processing
A. R. Sharma , Global School of Insurance Analytics, International University, IndiaAbstract
The insurance industry stands at an inflection point where traditional actuarial approaches — based on limited historical data and human judgment — are being overwhelmingly supplemented or replaced by data‑driven, automated risk‑assessment systems. This article synthesizes emerging developments from both industry and academic research to outline a comprehensive framework for how predictive analytics, Internet of Things (IoT) data streams, and machine learning algorithms together can transform underwriting, pricing, and claims management in Property & Casualty (P&C), health, and life insurance lines. Drawing on recent empirical and conceptual studies, the paper describes how real‑time sensor data and historical records can be fused to create dynamic risk profiles; how claims processing may be accelerated and fraud mitigated using AI; and how insurers can realize cost efficiencies and competitive differentiation. The paper discusses methodological considerations, operational challenges (data quality, privacy, governance), and strategic change‑management imperatives. With this integrated paradigm, insurers can shift from reactive “detect and repair” models to proactive “predict and prevent” risk management — paving the way for more accurate pricing, personalized policies, enhanced customer satisfaction and sustainable profitability.
Keywords
Predictive analytics, Machine learning, Insurance underwriting
References
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Copyright (c) 2025 A. R. Sharma
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