Building fire insurance premium rate based on quantitative risk assessment

Abstract

[Objective] Fire is a serious threat to public life and property safety. Insurance is an effective means to deal with fire risk, and accurately determining the premium rate of buildings according to the fire risk is a concern of the insurance industry. Currently, the premium rate is mainly based on the fire frequency and loss expectation from the insurance statistics, and adjustments are based on building risk assessment results. The adjustment scheme can be divided into two types. One is the rate floating model, which gives the floating range of the premium rate based on the risk level, but the floating proportion is fairly subjective. The other is the rate calculation model, which establishes the quantitative risk assessment method to calculate the specific premium rate. However, comprehensively reflecting the hazardous in the buildings as well as the uncertainty of losses with the current risk assessment method is difficult. Thus, the premium rate is relatively rough. [Methods] A quantitative model for building fire insurance premium rates is constructed in this paper. First, the Bayesian network method is used to calculate the building fire probability considering the influences of various risk sources. The specific factors affecting ignition were comprehensively analyzed from the aspects of humans, things, and environments. Therefore, 14 factors were selected to construct the Bayesian network of building ignition, based on which the probability of building fire can be calculated rather quantitatively and objectively. Second, the Latin hypercube sampling (LHS) is used to stratify the burn rate in different fire stages from ignition, growth, and development to spread with certain distributions to reflect the staging and random characteristics of fire losses. Thus, the final loss distribution, including the expected value, standard deviation, probability density function, and cumulative probability density function, can be acquired accurately. [Results] Therefore, the quantitative and dynamic risk assessment of building fire is realized, and the rate calculation model is used to compute the rate based on the result. Fifteen households were selected to calculate their premium rates based on the quantitative assessment of building fire risk, including ignition probability and loss distribution, and the premium rates are compared with the rate in the insurance market. [Conclusions] Results show that the proposed premium rate determination model can effectively reflect the differentiated level of fire risk and ensure the fairness of insurance. The premise of the building fire insurance premium rate model in this paper is that the insurance company covers all the fire risks of the building and disregards the case of deductible due to the retainment of fire risk by the insured. In addition, the foreign statistics were adopted, and the normal loss distribution at each stage after the ignition was assumed due to the lack of domestic data. Deductibles can be considered in further research to construct premium rate models, and accurate data can be acquired to obtain results consistent with the building fire risk level in China.