The DEA Analysis In Decision-making Of Reinsurance

Insurance is a special finance trade of managing risk. As the development ofsociety and economy, technology and science are applied abroad, the degree of loss ofmatter and casualty enlarged in an accident. Any insurance dare not bear the risk ofmint and it is not allowed by wardship. To keep stability and enlarge the scale ofmanaging, reinsurance becomes the necessity choice, the basic problem of reinsuranceis to deal with premium and keep retention. In theory, it can be answered by utility.but utility function is subjective and can't define easily, so to apply the means ofutility in practice of decision-making of reinsurance is difficulty. Data envelopment analysis (DEA) is a power technique of measuring the relativeefficiency of a set of decision-making units (DMUs) with the same inputs and outputs.This technique has been widely applied in measuring the optimal property, but in theactivity of economy, inputs and outputs is disturbed by error and fluctuate andperiodicity, so the stochastic data variations in inputs and outputs may be incorporatedinto the DEA framework for evaluating the efficiency. In this paper, the development of Data Envelopment Analysis (DEA) theories,DEA methods and DEA applications are discussed, the basic DEA model DEAefficiency theories are analyzed. Based on divided and conquer, the define ofevaluation of stochastic DMU from another aspect is presented and given a newmethod of solving, the process and algorithm of many input and output stochasticDMU provided with common trend is discussed in main. Based on principle ofmean-variance, this paper firstly advances this definition of the relative -stableefficiency of stochastic DMUS and presents the model DEA of the relative-stableefficiency measurement and analyses the relative-stable efficiency, and gives thecalculation of it. The idea of data envelopment analysis is applied intodecision-making of reinsurance, implying the choice project and the forecasting therelative efficiency of reinsurance and analyzing the feasibility of algorithm.