Proactive risk intelligence: AI-driven alerting frameworks for disaster mitigation at insured locations

Balaji Chode *

Senior Cloud Architect, AI/ML Applications.
 
Review
International Journal of Science and Research Archive, 2024, 13(01), 3606-3611.
Article DOI: 10.30574/ijsra.2024.13.1.2022
Publication history: 
Received on 13 September 2024; revised on 21 October 2024; accepted on 28 October 2024
 
Abstract: 
In an increasingly disaster-prone world, commercial insurance providers face growing pressure to deliver timely, actionable intelligence to protect high-value assets. Traditional risk management systems often rely on static, manually assembled reports and fail to provide early warnings during critical events. This paper presents the design and deployment of an AI-driven alerting framework that enables proactive disaster mitigation at insured locations. The proposed system integrates real-time hazard data feeds, machine learning–based risk scoring, and customizable alert thresholds into a secure, cloud-native platform. Key components include forecasting models for natural hazards, cyber risk scoring algorithms, and a dynamic rules engine for generating location-specific advisories. The platform has been deployed across a multi-tenant insurance ecosystem, demonstrating high scalability with over 2 million API calls per day and sub-150ms response latency. Real-world outcomes include reduced incident response time, improved underwriting efficiency, and enhanced client resilience. The framework sets a new standard for intelligent risk delivery and can serve as a blueprint for broader disaster readiness initiatives across insurance and asset-intensive industries.
 
Keywords: 
AI risk scoring; Disaster alerting; Insured asset resilience; Real-time intelligence; Insurance technology
 
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0