Deductible Decision-Making of Contractors’ All Risk Insurance Using Evolutionary Support Vector Machines Inference Model (ESIM)

• Main Authors: Yi-hong liao, 廖義宏
• Other Authors: none
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/21706271641799411278

碩士 === 國立臺灣科技大學 === 營建工程系 === 96 === To reduce the high risk of a construction project, an insurance program, especially the Contractors’ All risk Insurance (CAR), is a widely applied risk transfer mechanism in the construction industry. The contractor’s insurance decision processes contain evaluating the expectation loss to balance the loss retention capacity, and achieve the optimal decision of deductible. The optimal decision of deductible impacts not only the compensation and risk transference, but also the premium cost. This paper, in view of building contractors, identifies risk factors impacting the project during construction to set up loss prediction models, to evaluate the expectation loss, and to provide the decision criterions. The objective of this research is providing the criterions of the optimal decision of insurance deductible to support the building contractor to determine the strategy of CAR. The chance of loss for a building project includes the loss frequency and the loss severity. This study focuses on improving the methodology used in the previous research to evaluate the chance of loss. Through papers review and field experts interview, the loss attributes of a building construction project were identified. The objective factors significantly describe the loss attributes were also selected as the input variables of ESIM (Evolutionary Support Vector Machine Inference Model). Using ESIM, the loss prediction model was developed to predict the loss frequency and the loss severity. As a result, a combination of the efficient frontier curve of deductibles with the indifference curve of the risk vs. insurance cost, and a criterion function of the optimal decision of insurance deductible were developed.