本文選題：大額和可疑交易 + CHAID算法��； 參考：《湖南大學(xué)》2014年碩士論文

【摘要】：隨著金融業(yè)的蓬勃發(fā)展，我國金融業(yè)面臨越來越多的安全風(fēng)險。其中，，將非法收入以各種形式注入金融體系的洗錢犯罪活動也在快速增長，給國家和社會帶來了巨大危害。為了規(guī)避此類風(fēng)險，確保我國金融業(yè)健康持續(xù)發(fā)展，建立健全的中國反洗錢監(jiān)控體系也顯得日益重要。從目前掌握的案例來看，洗錢犯罪活動主要表現(xiàn)為犯罪分子將非法收入通過銀行和保險公司等金融機(jī)構(gòu)，并利用其產(chǎn)品的復(fù)雜性將其轉(zhuǎn)化為合法收入。 目前，我國保險業(yè)反洗錢監(jiān)控手段停留在依據(jù)法律規(guī)定設(shè)定篩選條件，但是保險企業(yè)自身對大額和可疑交易的篩選并無創(chuàng)新。本文著眼于我國保險業(yè)反洗錢監(jiān)控的應(yīng)用研究，根據(jù)既定的法律法規(guī)及保險公司的反洗錢經(jīng)驗數(shù)據(jù)，在現(xiàn)有的數(shù)據(jù)挖掘技術(shù)研究基礎(chǔ)上，重點對決策樹主要算法展開研究，本論文的主要成果概括如下： 利用決策樹CHAID算法、CART算法和QUEST算法對大額和可疑交易的相關(guān)關(guān)系進(jìn)行分析，以獲得影響大額和可以交易識別結(jié)果的因素。然后，在此基礎(chǔ)上，通過設(shè)立錯分成本對模型進(jìn)行進(jìn)一步分析，使得模型結(jié)果擬合度提升，最終根據(jù)模型結(jié)果使得保險企業(yè)能獲得更加具體的篩選條件。實驗數(shù)據(jù)表明，CHAID算法、CART算法和QUEST算法的模型擬合度基本持平，但是在大額和可疑交易識別的準(zhǔn)確率上均未能達(dá)到理想水平。因此，本文在原有基礎(chǔ)上進(jìn)一步優(yōu)化模型，設(shè)立錯分成本，重新使用CHAID算法擬合模型，準(zhǔn)確率大幅提高，具體表現(xiàn)為將大額和可疑交易的對象錯分為良好信用客戶的概率低于15%。 在大額和可疑交易的篩選自變量范圍中添加投保與退保天數(shù)間隔及繳費(fèi)模式，并將之應(yīng)用到現(xiàn)行保險業(yè)反洗錢監(jiān)控系統(tǒng)中。分析了兩種篩選情況的結(jié)果差異，證明了改進(jìn)后的篩選條件能提高篩選的覆蓋率和準(zhǔn)確率，最后以此給出了對于保險業(yè)反洗錢系統(tǒng)政策上和技術(shù)上的建議,具有一定的現(xiàn)實意義。
[Abstract]:With the vigorous development of financial industry, China's financial industry is facing more and more security risks. Among them, the crime of injecting illegal income into the financial system in various forms is also growing rapidly, which brings great harm to the country and society. In order to avoid such risks and ensure the healthy and sustainable development of China's financial industry, it is increasingly important to establish a sound anti-money laundering monitoring system in China. According to the current cases, the criminal activities of money laundering mainly show that the criminals turn the illegal income through banks and insurance companies, and use the complexity of their products to convert it into legal income. At present, China's insurance industry anti-money laundering monitoring means stay in accordance with the provisions of the law to set screening conditions, but the insurance companies themselves to large and suspicious transactions screening has not been innovative. This paper focuses on the application research of anti-money laundering monitoring in insurance industry in China. According to the established laws and regulations and the anti-money laundering experience data of insurance companies, and on the basis of the existing data mining technology, this paper focuses on the research on the main algorithms of decision tree. The main achievements of this paper are summarized as follows: the correlation between large amount and suspicious transactions is analyzed by using decision tree algorithm cart and request algorithm to obtain the factors that affect the results of large amount and transaction identification. On this basis, the model is further analyzed by setting up a misdivisional cost, so that the model result fit is improved, and finally the insurance company can obtain more specific screening conditions according to the model results. The experimental data show that the model fitting degree of cart algorithm and quest algorithm is basically the same, but the accuracy of large amount and suspicious transaction recognition is not up to the ideal level. Therefore, this paper further optimizes the model on the basis of the original, sets up the misdivision cost, reuses the Chaid algorithm to fit the model, and the accuracy is greatly improved. The concrete performance is that the probability of classifying the objects of large amount and suspicious transaction into good credit customers is lower than 15%. In the range of screening independent variables of large and suspicious transactions, the interval of days between insurance and withdrawal and the mode of payment are added, and applied to the current insurance anti-money laundering monitoring system. The differences between the two screening conditions are analyzed, and it is proved that the improved screening conditions can improve the coverage and accuracy of the screening. Finally, the policy and technical suggestions for the insurance anti-money laundering system are given. Has certain realistic significance.
【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】：TP277;F842.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李曉明;論保險資金運(yùn)用于股票市場的監(jiān)管[J];保險研究;2000年02期

2 楊勝剛;王鵬;;基于數(shù)據(jù)挖掘技術(shù)的人民幣反洗錢系統(tǒng)設(shè)計[J];財經(jīng)理論與實踐;2005年06期

3 湯俊;;國際反洗錢智能數(shù)據(jù)分析技術(shù)綜述[J];華南金融電腦;2008年08期

4 馬曉麗;;數(shù)據(jù)挖掘技術(shù)在反洗錢工作中的應(yīng)用現(xiàn)狀及深化應(yīng)用建議[J];經(jīng)濟(jì)研究導(dǎo)刊;2012年12期

5 譚德彬;鄔潤龍;陳藻;;利用數(shù)據(jù)挖掘技術(shù)支持反洗錢系統(tǒng)[J];金融電子化;2003年09期

6 邱兆祥;朱寶明;;反洗錢的外部性與激勵機(jī)制的建立[J];金融理論與實踐;2007年01期

7 朱寶明;我國銀行業(yè)反洗錢的成本與收益分析——從博奕論的視角[J];金融研究;2004年04期

8 楊勝剛,何靖;反洗錢領(lǐng)域大額與可疑信息報告制度的經(jīng)濟(jì)學(xué)分析[J];金融研究;2004年10期

9 陳云開;盧正鼎;李瑞軒;李玉華;;分布式異構(gòu)計算環(huán)境下的洗錢偵測系統(tǒng)體系結(jié)構(gòu)——基于數(shù)據(jù)挖掘技術(shù)[J];計算機(jī)工程與應(yīng)用;2006年29期

10 欒麗華,吉根林;決策樹分類技術(shù)研究[J];計算機(jī)工程;2004年09期

本文編號：2103652