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

【摘要】：壽險作為保險行業(yè)的重要分支，是目前數(shù)據(jù)挖掘商業(yè)應(yīng)用的熱點領(lǐng)域。利用數(shù)據(jù)挖掘技術(shù)對壽險數(shù)據(jù)進行分析挖掘具有重要的現(xiàn)實意義。隨著壽險市場的開放、外資公司的介入，競爭日趨灼熱化。壽險保險公司普遍缺乏對代理人激勵系統(tǒng)、活動以及措施的信息反饋和效果分析。因此，通過數(shù)據(jù)挖掘方法對壽險代理人激勵事件、激勵反饋等信息進行科學(xué)的分析研究，是提升壽險公司競爭力的重要途徑�，F(xiàn)階段壽險公司對壽險代理人激勵方式的選擇上存在盲目性和不及時性，，同時對各項激勵決策的收益分析不夠充分。本文運用多種數(shù)據(jù)挖掘方法與人壽保險公司激勵方式相結(jié)合，解決了激勵理論與員工激勵決策結(jié)合的問題，并對壽險代理人激勵收益進行詳細評估。本文重點對決策樹和聚類算法展開研究，主要工作概括如下： 激勵方式?jīng)Q策時需要考慮的因素眾多，如果只依據(jù)簡單的人為經(jīng)驗進行決策將導(dǎo)致片面化，而通過精算分析過程繁瑣并耗費大量時間。因此，本文提出基于決策樹的壽險代理人激勵方式?jīng)Q策模型，對于壽險公司代理人數(shù)據(jù)進行周密分析處理，根據(jù)設(shè)計的激勵事件提取方法提取出每個代理人的激勵事件，利用C4.5和Random Tree決策樹預(yù)測模型，并評價分析兩種決策樹方法在壽險代理人數(shù)據(jù)環(huán)境下性能的差異，以得到每個代理人在自身條件下激勵方式的最優(yōu)決策策略。同時進行案例實證分析，利用該模型進行預(yù)測和檢驗，與實際精算決策結(jié)果作對比，本文方法決策F-Measure可達86.6%。 基于激勵方式的決策結(jié)果，本文構(gòu)建了壽險代理激勵方式績效指標(biāo)的聚類分析指標(biāo)體系，選擇相關(guān)指標(biāo)數(shù)據(jù)，進而對各個聚類下激勵方式分布情況進行分析探討。通過K-Means聚類和Hierarchical聚類方法，對比分析它們在壽險公司績效分類下的結(jié)果，從而得到當(dāng)前環(huán)境下優(yōu)質(zhì)壽險分公司的激勵方式最優(yōu)比例分配方案。經(jīng)案例分析證明，本文方法可為壽險公司調(diào)整各項激勵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)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F842.62;TP311.13

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