Efficiently integrating MapReduce-based computing into a Hurricane Loss Projection model

Abstract

Homeowner insurance is a critical issue for Floridians because of the periodic threat hurricanes pose to Florida. Providing fairness into the rate-making policy process, the state of Florida has developed the Florida Public Hurricane Loss Model (FPHLM), an open, public hurricane risk model to assess the risk of wind damage to insured residential properties. For each input property portfolio, the FPHLM processes a large amount of data to provide expected losses over tens of thousand of years of simulation, for which computational efficiency is of paramount importance. This paper presents our work in integrating the atmospheric component into the FPHLM using MapReduce, which resulted in a highly efficient computing platform for generating stochastic hurricane events on a cluster of computers. The experimental results demonstrate the feasibility of utilizing MapReduce for risk modeling components.