Predictive Modeling and Intelligent Underwriting for Unusual Disasters Through Multi-Modal Big Data Analysis

Shubo Lei; Chenyang Ruan; Junyao Chu

doi:10.54097/7psbtj60

Authors

Shubo Lei
Chenyang Ruan
Junyao Chu

DOI:

https://doi.org/10.54097/7psbtj60

Keywords:

Neural Network, Prediction Model, Big Data, ARIMA Model.

Abstract

This study presents an innovative approach to insurance underwriting by integrating big data analytics and meteorological data to anticipate catastrophic events, thereby enhancing risk assessment in the insurance industry. Against the backdrop of escalating climate volatility and the growing impact of natural disasters on insurance claims, the research aims to develop a predictive model that leverages the power of ARIMA for forecasting potential future losses. The significance of this study lies in its potential to provide insurers with strategic tools to mitigate disaster-related risks, optimize underwriting decisions, and ultimately, protect against financial instability caused by unforeseen catastrophes. The model includes comprehensive system design, data crawling from various sources, warehouse design for efficient data storage, and visualization for intuitive understanding of risk patterns. Technologies employed in this study span across Python for data manipulation, Hadoop and Spark for big data processing, MySQL for database management, and Vue with Echarts for dynamic data visualization. This integrated system not only offers a robust framework for predicting disaster outcomes but also paves the way for more informed and proactive insurance strategies in the face of climate change and its associated challenges.

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