本文選題：未決賠款準(zhǔn)備金　切入點(diǎn)：案均賠款法　出處：《山東科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：未決賠款準(zhǔn)備金是非壽險準(zhǔn)備金中最重要的一部分,因此對未決賠款準(zhǔn)備金的評估顯得尤為重要。對于非壽險公司來說,由于某些準(zhǔn)備金評估周期較長的特征,通常某事故年的最后一進(jìn)展年的索賠額是未知的。因此,在公司對未決賠款準(zhǔn)備金進(jìn)行評估時,要考慮很多的影響因素,針對不同的數(shù)據(jù)特征也要選取不同的評估方法。事實(shí)上,在實(shí)際的保險案例中,非壽險公司在錄入真實(shí)索賠數(shù)據(jù)時,可能會出現(xiàn)某些人為的輸入錯誤,譬如小數(shù)點(diǎn)前移或者后移一位、少輸入或多輸入一位數(shù)等等,這都會導(dǎo)致最終索賠額的巨大變化。另外,還有一些非人為行為(如自然災(zāi)害、信用欺詐、經(jīng)濟(jì)危機(jī)等)所導(dǎo)致的索賠數(shù)據(jù)突然變大或者變小也是時有發(fā)生的,這些也會很大的影響準(zhǔn)備金評估的結(jié)果,因此對未決賠款準(zhǔn)備金評估方法的穩(wěn)健性進(jìn)行研究很有意義。本文首先介紹了非壽險實(shí)務(wù)中未決賠款準(zhǔn)備金評估的常用確定性模型與方法,從這些模型的結(jié)構(gòu)、假設(shè)條件和數(shù)據(jù)基礎(chǔ)入手,全面分析了模型的特點(diǎn)。其次,以確定性方法中的案均賠款法為例,引入離群值,運(yùn)用殘差箱線圖法和兩點(diǎn)法對相關(guān)索賠數(shù)據(jù)進(jìn)行離群值檢驗,然后針對離群值提出了一種穩(wěn)健的案均賠款法,對進(jìn)展因子和結(jié)案率數(shù)據(jù)的選取方式加以修正,并通過實(shí)證證明,穩(wěn)健的案均賠款法可以對離群值進(jìn)行有效的識別和調(diào)整,最終索賠額估計結(jié)果也比傳統(tǒng)方法更加平穩(wěn)。在此基礎(chǔ)上由未決賠款準(zhǔn)備金評估的確定性方法過渡到隨機(jī)性方法中,首先介紹實(shí)務(wù)中常用的一些隨機(jī)性模型,并分析各方法的優(yōu)缺點(diǎn)。其次,通過在準(zhǔn)備金評估的雙廣義線性模型中引入離群值,考慮離群值對未決賠款準(zhǔn)備金評估的影響,基于擬似然估計方法,提出了一種穩(wěn)健的估計值方程,來對離群值進(jìn)行識別和調(diào)整,使得模型在處理存在離群值的數(shù)據(jù)時,可以得到更加穩(wěn)健的評估結(jié)果。進(jìn)而,更深層次的對雙廣義線性模型進(jìn)行研究,調(diào)整賠付損失的進(jìn)展模式,改善雙廣義線性模型的線性預(yù)估量,將不易量化的內(nèi)部因素和外部因素引入到模型中,以此拓寬模型的適用范圍,并改善模型的預(yù)測效果。最后,利用R軟件對所提出的模型給出算例分析,得到相關(guān)結(jié)論。
[Abstract]:The outstanding claim reserve is the most important part of the non-life insurance reserve, so the evaluation of the pending claim reserve is particularly important. Usually the amount claimed in the last year of an accident year is unknown. Therefore, a number of factors need to be taken into account when companies assess outstanding claims reserves, In fact, in actual insurance cases, when non-life insurance companies enter true claim data, there may be some artificial input errors. If the decimal point moves forward or backward by one decimal place, by entering less or more digits, and so on, this can lead to a huge change in the amount of the final claim. In addition, there are also some non-human acts (such as natural disasters, credit fraud, etc.). The sudden increase or decrease in claim data caused by the economic crisis, etc., also happens from time to time, which can also have a significant impact on the outcome of reserve assessment, Therefore, it is very meaningful to study the robustness of the evaluation methods of pending claim reserve. Firstly, this paper introduces the commonly used deterministic models and methods in non-life insurance practice, from the structure of these models, Based on the hypothesis and data basis, the characteristics of the model are analyzed. Secondly, taking the case average compensation method in the deterministic method as an example, the outlier value is introduced, and the outlier values of the related claim data are tested by the residual box diagram method and the two-point method. Then, a robust average compensation method is proposed for outliers, which modifies the selection of progress factors and closure rate data, and proves that the robust average compensation method can effectively identify and adjust outliers' outliers. The result of final claim estimation is also more stable than the traditional method. On this basis, the deterministic method of evaluation of pending claim reserve is transferred to randomness method. Firstly, some stochastic models commonly used in practice are introduced. The advantages and disadvantages of each method are analyzed. Secondly, by introducing outliers into the bilinear model of reserve evaluation, considering the influence of outliers on the evaluation of pending compensation reserve, the quasi-likelihood estimation method is used. In this paper, a robust estimation equation is proposed to identify and adjust outliers, so that the model can obtain more robust evaluation results when dealing with data with outliers. The further research on the bilinear model, the adjustment of the progress model of compensation loss, the improvement of the linear predictor of the bilevel linear model, the introduction of internal and external factors which are not easy to quantify into the model. In order to widen the scope of application of the model and improve the prediction results of the model. Finally, an example analysis of the proposed model is given by using R software, and the relevant conclusions are obtained.
【學(xué)位授予單位】：山東科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F224;F840

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