【摘要】：汽車保險獎懲系統(tǒng)是應用于續(xù)期保費階段的一種重要經(jīng)驗估費方法,其主要工作原理是依據(jù)歷史索賠信息對投保人進行保費“區(qū)別”對待:針對在上個保險年度內沒有發(fā)生索賠事件的投保人實行減免續(xù)期保費的“獎勵”,而對發(fā)生過一次或多次索賠要求的投保人在相應程度上給予增收保費的“懲罰”。本文主要圍繞基于索賠次數(shù)模型的獎懲系統(tǒng)及其工作原理展開研究討論,具體內容和研究成果如下:1.鑒于保險實踐中無索賠次數(shù)記錄出現(xiàn)的保單通常在保單組合內占有較大比重,本文采用疊加和調零后的復合分布類來擬合同質性保單的索賠次數(shù),并分別給出了參數(shù)估計的具體方法;2.獎懲系統(tǒng)等級轉移概率均由其在上一保險年度內的索賠次數(shù)所決定,利用這一工作原理,通過時間序列方法中的INAR(1)模型對下一保險年度的索賠次數(shù)進行了預報估計;3.介紹了經(jīng)典獎懲系統(tǒng)模型的基本數(shù)學理論和保費選擇方法,從建立保費等級,討論等級轉移共性法則,計算索賠發(fā)生概率并得到概率轉移矩陣,到最優(yōu)獎懲系統(tǒng),不同保費原理的選擇及保費原理下經(jīng)驗費率系數(shù)的確定等;4.考慮到只依據(jù)索賠次數(shù)分布構建的獎懲系統(tǒng)自身存在的局限性,在真實索賠次數(shù)模型的基礎上,通過設立標準索賠額基數(shù)對索賠金額進行分類,構建出一個新隨機變量——高額索賠次數(shù),同時完成高額索賠次數(shù)的分布模型假設及參數(shù)估計;5.針對我國保險公司在車險經(jīng)驗定價過程中所遇到的實際問題,提出了對現(xiàn)行獎懲制度的三大改進建議。包括將投保人違章記錄納入車險獎懲系統(tǒng);構建路徑依賴的獎懲系統(tǒng);以及利用互聯(lián)網(wǎng)信息技術搭建信息共享平臺,從而預防高風險投保人出現(xiàn)逃避繳納溢價保費的行為。
[Abstract]:The automobile insurance reward and punishment system is an important empirical method used in the renewal premium stage. Its main working principle is to treat the policyholder's premium "differently" according to the historical claim information: "reward" for the policy holder who did not have a claim event in the last insurance year. Policy-holders who have made one or more claims are punished to a certain extent. This paper mainly focuses on the reward and punishment system based on the number of claims model and its working principle. The specific contents and research results are as follows: 1. In view of the fact that the number of claims recorded in the insurance practice usually occupies a large proportion in the policy portfolio, this paper uses the composite distribution class after superposition and zero adjustment to fit the claim number of the homogeneous policy. The methods of parameter estimation are given respectively. 2. The probability of grade transfer of reward and punishment system is determined by the number of claims in the last insurance year. Using this working principle, the number of claims in the next insurance year is forecasted by the INAR (1) model in the time series method; 3. This paper introduces the basic mathematical theory of the classical reward and punishment system model and the method of premium selection, from establishing the premium grade, discussing the general law of grade transfer, calculating the probability of claim occurrence and obtaining the probability transfer matrix, to the optimal reward and punishment system. The choice of different premium principle and the determination of experience rate coefficient under premium principle; 4. Considering the limitations of the reward and punishment system based only on the distribution of the number of claims, and on the basis of the true number of claims model, the amount of the claim is classified by establishing a standard base of claim amount, A new random variable, the high number of claims, is constructed, and the distribution model hypothesis and parameter estimation of the high number of claims are completed at the same time. 5. In view of the practical problems encountered by Chinese insurance companies in the process of car insurance experience pricing, three suggestions for improving the current reward and punishment system are put forward. Including the policy holder violation records into the vehicle insurance reward and punishment system; build a path dependent reward and punishment system; and use Internet information technology to build information sharing platform to prevent high-risk policyholders from paying premium premiums.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F842.634

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本文編號：2429463