本文選題：反洗錢系統(tǒng) + 風(fēng)險等級模型�。� 參考：《電子科技大學(xué)》2014年碩士論文

【摘要】：全球經(jīng)濟一體化進程的快速發(fā)展使得金融領(lǐng)域的洗錢行為日益猖獗,極大地影響了經(jīng)濟的正常運行。世界各國都在積極制定相關(guān)法律法規(guī),并要求各個金融機構(gòu)針對洗錢行為開發(fā)相應(yīng)的反洗錢系統(tǒng)。本文在這種背景下,以實際開發(fā)的銀行反洗錢系統(tǒng)為依據(jù),研究了反洗錢的相關(guān)模式,并將反洗錢風(fēng)險等級模型研究成果應(yīng)用于反洗錢系統(tǒng)。本文的研究內(nèi)容著重于數(shù)據(jù)挖掘技術(shù)在反洗錢系統(tǒng)中的應(yīng)用這一課題,提出了一種洗錢風(fēng)險等級模型。洗錢風(fēng)險等級模型使用數(shù)據(jù)挖掘技術(shù)中的歸納分類技術(shù),本文設(shè)計了一種基于決策樹歸納和規(guī)則歸納的組合分類器,洗錢風(fēng)險等級模型利用構(gòu)建的組合分類器對銀行客戶信息屬性進行洗錢風(fēng)險等級類型預(yù)測分類,分別將客戶洗錢風(fēng)險等級分為高風(fēng)險、中等風(fēng)險和低風(fēng)險三種等級。同時針對洗錢風(fēng)險等級模型建立的組合分類器進行相應(yīng)的樹剪枝優(yōu)化和Adaboost提升優(yōu)化。本文對洗錢風(fēng)險等級模型進行實驗評估,使用客戶信息驗證集數(shù)據(jù)對洗錢風(fēng)險等級模型進行測試,選用合理的評估度量標(biāo)準(zhǔn)比較各種基分類器以及組合分類器的性能,實驗結(jié)果表明該組合分類器與單個分類器模型比較,具有較高的預(yù)測分類準(zhǔn)確性。本文分析了銀行現(xiàn)有反洗錢系統(tǒng)的結(jié)構(gòu)與各個功能組件之間的關(guān)系,分析了SMBC-AML反洗錢系統(tǒng)的功能,目前反洗錢系統(tǒng)中存在的問題,介紹了按照國家反洗錢法的規(guī)定對大額、可疑、重點可疑等客戶的識別,進而將洗錢風(fēng)險等級模型以模塊化的設(shè)計方式整合進當(dāng)前的銀行反洗錢系統(tǒng)中。實踐表明該洗錢風(fēng)險等級模型具有良好的應(yīng)用前景。
[Abstract]:With the rapid development of global economic integration, money laundering in financial field is increasingly rampant, which greatly affects the normal operation of economy. Countries all over the world are actively making relevant laws and regulations, and all financial institutions are required to develop the corresponding anti-money laundering system. Under this background, based on the bank anti-money laundering system developed in practice, this paper studies the relevant models of anti-money laundering, and applies the research results of anti-money laundering risk level model to the anti-money laundering system. This paper focuses on the application of data mining technology in anti-money laundering system and proposes a risk level model for money laundering. In this paper, a combined classifier based on decision tree induction and rule induction is designed. The money laundering risk rating model uses the combined classifier to predict and classify the bank customer information attributes into three categories: high risk, medium risk and low risk. At the same time, the combined classifier based on money laundering risk grade model is optimized by tree pruning and Adaboost upgrading. This paper carries on the experimental evaluation to the money laundering risk grade model, uses the customer information verification set data to carry on the test to the money laundering risk grade model, selects the reasonable appraisal metric to compare the performance of various base classifier and the combination classifier. The experimental results show that the combined classifier is more accurate than the single classifier model. This paper analyzes the relationship between the structure of the existing anti-money laundering system of the bank and each functional component, analyzes the function of the SMBC-AML anti-money laundering system, the problems existing in the current anti-money laundering system, and introduces the large amount of money in accordance with the provisions of the National Anti-Money-Laundering Law. The identification of suspicious and key suspicious customers, and then the risk level model of money laundering is integrated into the current bank anti-money laundering system by modularized design. Practice shows that the model has a good application prospect.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F832.2;TP311.13

【共引文獻】

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1 費笑松;王玉屏;邵帥;;構(gòu)建我國商業(yè)銀行可疑交易報告體系的探討[J];金融縱橫;2014年11期

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1 司麗娟;論網(wǎng)絡(luò)洗錢犯罪[D];廣西大學(xué);2014年

2 劉小九;跨國洗錢犯罪法律對策研究[D];新疆大學(xué);2014年

3 秦立;M銀行反洗錢自主監(jiān)測系統(tǒng)的設(shè)計與實現(xiàn)[D];湖南大學(xué);2014年

4 唐帥;某基層央行反洗錢監(jiān)測與管理系統(tǒng)的設(shè)計與實現(xiàn)[D];湖南大學(xué);2014年

5 范慈家;中國與東盟地區(qū)的反洗錢合作機制研究[D];上海師范大學(xué);2015年

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本文編號：1797925