本文選題：壽險(xiǎn)代理人 + 激勵(lì)方式�。� 參考：《湖南大學(xué)》2014年碩士論文

【摘要】：壽險(xiǎn)作為保險(xiǎn)行業(yè)的重要分支，是目前數(shù)據(jù)挖掘商業(yè)應(yīng)用的熱點(diǎn)領(lǐng)域。利用數(shù)據(jù)挖掘技術(shù)對(duì)壽險(xiǎn)數(shù)據(jù)進(jìn)行分析挖掘具有重要的現(xiàn)實(shí)意義。隨著壽險(xiǎn)市場(chǎng)的開(kāi)放、外資公司的介入，競(jìng)爭(zhēng)日趨灼熱化。壽險(xiǎn)保險(xiǎn)公司普遍缺乏對(duì)代理人激勵(lì)系統(tǒng)、活動(dòng)以及措施的信息反饋和效果分析。因此，通過(guò)數(shù)據(jù)挖掘方法對(duì)壽險(xiǎn)代理人激勵(lì)事件、激勵(lì)反饋等信息進(jìn)行科學(xué)的分析研究，是提升壽險(xiǎn)公司競(jìng)爭(zhēng)力的重要途徑。現(xiàn)階段壽險(xiǎn)公司對(duì)壽險(xiǎn)代理人激勵(lì)方式的選擇上存在盲目性和不及時(shí)性，，同時(shí)對(duì)各項(xiàng)激勵(lì)決策的收益分析不夠充分。本文運(yùn)用多種數(shù)據(jù)挖掘方法與人壽保險(xiǎn)公司激勵(lì)方式相結(jié)合，解決了激勵(lì)理論與員工激勵(lì)決策結(jié)合的問(wèn)題，并對(duì)壽險(xiǎn)代理人激勵(lì)收益進(jìn)行詳細(xì)評(píng)估。本文重點(diǎn)對(duì)決策樹(shù)和聚類(lèi)算法展開(kāi)研究，主要工作概括如下： 激勵(lì)方式?jīng)Q策時(shí)需要考慮的因素眾多，如果只依據(jù)簡(jiǎn)單的人為經(jīng)驗(yàn)進(jìn)行決策將導(dǎo)致片面化，而通過(guò)精算分析過(guò)程繁瑣并耗費(fèi)大量時(shí)間。因此，本文提出基于決策樹(shù)的壽險(xiǎn)代理人激勵(lì)方式?jīng)Q策模型，對(duì)于壽險(xiǎn)公司代理人數(shù)據(jù)進(jìn)行周密分析處理，根據(jù)設(shè)計(jì)的激勵(lì)事件提取方法提取出每個(gè)代理人的激勵(lì)事件，利用C4.5和Random Tree決策樹(shù)預(yù)測(cè)模型，并評(píng)價(jià)分析兩種決策樹(shù)方法在壽險(xiǎn)代理人數(shù)據(jù)環(huán)境下性能的差異，以得到每個(gè)代理人在自身?xiàng)l件下激勵(lì)方式的最優(yōu)決策策略。同時(shí)進(jìn)行案例實(shí)證分析，利用該模型進(jìn)行預(yù)測(cè)和檢驗(yàn)，與實(shí)際精算決策結(jié)果作對(duì)比，本文方法決策F-Measure可達(dá)86.6%。 基于激勵(lì)方式的決策結(jié)果，本文構(gòu)建了壽險(xiǎn)代理激勵(lì)方式績(jī)效指標(biāo)的聚類(lèi)分析指標(biāo)體系，選擇相關(guān)指標(biāo)數(shù)據(jù)，進(jìn)而對(duì)各個(gè)聚類(lèi)下激勵(lì)方式分布情況進(jìn)行分析探討。通過(guò)K-Means聚類(lèi)和Hierarchical聚類(lèi)方法，對(duì)比分析它們?cè)趬垭U(xiǎn)公司績(jī)效分類(lèi)下的結(jié)果，從而得到當(dāng)前環(huán)境下優(yōu)質(zhì)壽險(xiǎn)分公司的激勵(lì)方式最優(yōu)比例分配方案。經(jīng)案例分析證明，本文方法可為壽險(xiǎn)公司調(diào)整各項(xiàng)激勵(lì)方式所占比例提供有效參考。
[Abstract]:As an important branch of insurance industry, life insurance is a hot field of data mining commercial application. It is of great practical significance to analyze and mine life insurance data by using data mining technology. With the opening of the life insurance market and the intervention of foreign companies, the competition is becoming more and more hot. Life insurance companies generally lack information feedback and effect analysis on agent incentive systems, activities and measures. Therefore, it is an important way to improve the competitiveness of life insurance companies to scientifically analyze and study the information of life insurance agents' incentive events and incentive feedback through data mining methods. At present, there is blindness and intimeliness in the choice of life insurance agent's incentive mode in life insurance company, and at the same time, the income analysis of every incentive decision is not enough. In this paper, a combination of multiple data mining methods and life insurance incentive methods is used to solve the problem of the combination of incentive theory and employee incentive decision, and to evaluate the incentive income of life insurance agents in detail. This paper focuses on the decision tree and clustering algorithm, the main work is summarized as follows: There are many factors that need to be considered in the decision of incentive mode. If the decision is based on simple human experience, it will lead to one-sided, and the actuarial analysis process is cumbersome and takes a lot of time. Therefore, this paper puts forward a decision model of life insurance agent incentive mode based on decision tree. The data of life insurance company agent is carefully analyzed and processed, and the incentive events of each agent are extracted according to the designed incentive event extraction method. Using C4.5 and Random Tree decision tree prediction model, and evaluating and analyzing the difference of performance of two decision tree methods in the data environment of life insurance agent, we can get the optimal decision strategy of each agent's incentive mode under their own condition. At the same time, the empirical case analysis is carried out, and the model is used to predict and test, and compared with the actual actuarial decision results, the F-Measure of this method can reach 86.6. Based on the decision result of incentive mode, this paper constructs the cluster analysis index system of life insurance agent incentive mode performance index, selects the relevant index data, and then analyzes the distribution of incentive mode under each cluster. By means of K-Means clustering and Hierarchical clustering, the results of performance classification of life insurance companies are compared and analyzed, and the optimal incentive scheme of premium life insurance branches is obtained in the current environment. Case study shows that this method can provide an effective reference for life insurance companies to adjust the proportion of incentives.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F842.62;TP311.13

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 曹均華,邢煒,俞自由;關(guān)于保險(xiǎn)代理人激勵(lì)機(jī)制問(wèn)題的研究[J];經(jīng)濟(jì)科學(xué);2000年01期

2 周濤;陸惠玲;;數(shù)據(jù)挖掘中聚類(lèi)算法研究進(jìn)展[J];計(jì)算機(jī)工程與應(yīng)用;2012年12期

3 吉根林,孫志揮;基于數(shù)據(jù)挖掘技術(shù)的保險(xiǎn)業(yè)務(wù)風(fēng)險(xiǎn)分析[J];計(jì)算機(jī)工程;2002年02期

4 鄭丹;王潛平;;K-means初始聚類(lèi)中心的選擇算法[J];計(jì)算機(jī)應(yīng)用;2012年08期

5 翟俊海;翟夢(mèng)堯;李勝杰;;基于相容粗糙集技術(shù)的連續(xù)值屬性決策樹(shù)歸納[J];計(jì)算機(jī)科學(xué);2012年11期

6 許亞平;;美國(guó)保險(xiǎn)市場(chǎng)發(fā)展對(duì)中國(guó)的啟示[J];經(jīng)濟(jì)研究導(dǎo)刊;2013年07期

7 張宗軍;;我國(guó)壽險(xiǎn)公司范圍經(jīng)濟(jì)效應(yīng)的實(shí)證研究[J];經(jīng)濟(jì)與管理;2013年06期

8 胡慶輝;丁立新;陸玉靖;何進(jìn)榮;;一種快速、魯棒的有限高斯混合模型聚類(lèi)算法[J];計(jì)算機(jī)科學(xué);2013年08期

9 梁鳳蘭;;優(yōu)化決策樹(shù)改進(jìn)挖掘算法仿真[J];計(jì)算機(jī)仿真;2013年11期

10 徐守坤;王薇;樂(lè)光學(xué);;IWO-Kohonen聚類(lèi)算法在IDS中的應(yīng)用[J];計(jì)算機(jī)工程;2014年01期

相關(guān)碩士學(xué)位論文 前3條

1 魏瑩;基于分解的多目標(biāo)進(jìn)化聚類(lèi)算法研究[D];西安電子科技大學(xué);2013年

2 馮正西;關(guān)聯(lián)規(guī)則的改進(jìn)與應(yīng)用研究[D];華中師范大學(xué);2013年

3 鄭文娟;基于Weka平臺(tái)的改進(jìn)模型C均值聚類(lèi)算法研究與應(yīng)用[D];太原理工大學(xué);2013年

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