【摘要】：信用風(fēng)險(xiǎn)是商業(yè)銀行的主要風(fēng)險(xiǎn)之一，根據(jù)麥肯錫的研究表明信用風(fēng)險(xiǎn)占總體銀行風(fēng)險(xiǎn)的六成以上，比市場(chǎng)風(fēng)險(xiǎn)和操作風(fēng)險(xiǎn)之和的三倍還多。作為影響金融行業(yè)尤其是銀行業(yè)興衰榮辱的關(guān)鍵因素，信用風(fēng)險(xiǎn)歷來(lái)是國(guó)內(nèi)外學(xué)者和政府監(jiān)管部門(mén)熱點(diǎn)研究問(wèn)題。隨著全球化金融危機(jī)的影響以及信用交易的擴(kuò)大化，商業(yè)銀行面對(duì)的信用風(fēng)險(xiǎn)呈現(xiàn)形式多樣化、操作復(fù)雜化的趨勢(shì)，從我國(guó)的實(shí)際情況來(lái)看，信用風(fēng)險(xiǎn)度量的準(zhǔn)確性與靈敏性都是制約銀行業(yè)風(fēng)險(xiǎn)管理的薄弱環(huán)節(jié)，如何界定信用風(fēng)險(xiǎn)并施以有效的風(fēng)險(xiǎn)管理措施是當(dāng)下商業(yè)銀行追求有效利潤(rùn)并保證長(zhǎng)久有效持續(xù)經(jīng)營(yíng)的重要問(wèn)題，也是保障投資者利益、社會(huì)穩(wěn)定的民生問(wèn)題。本文從商業(yè)銀行的角度出發(fā)，以信用風(fēng)險(xiǎn)度量指標(biāo)體系的建立為核心，提出基于數(shù)據(jù)挖掘技術(shù)的商業(yè)銀行中企業(yè)客戶，尤其是上市企業(yè)客戶的信用風(fēng)險(xiǎn)精準(zhǔn)度量模型，希望能夠?yàn)樯虡I(yè)銀行信用風(fēng)險(xiǎn)的度量提供一定的技術(shù)參考和方法依據(jù)。 首先，論述了商業(yè)銀行公司客戶信用風(fēng)險(xiǎn)度量的相關(guān)理論，從巴塞爾協(xié)議出發(fā)結(jié)合當(dāng)前國(guó)情對(duì)商業(yè)銀行公司客戶信用風(fēng)險(xiǎn)重新界定；介紹了信用風(fēng)險(xiǎn)精準(zhǔn)度度量的模型和方法，研究了數(shù)據(jù)挖掘技術(shù)在商業(yè)銀行中風(fēng)險(xiǎn)管理中的應(yīng)用，尤其是在信用風(fēng)險(xiǎn)度量的必要性與可行性。 其次，從指標(biāo)體系建設(shè)和模型構(gòu)建兩方面詳細(xì)介紹了商業(yè)銀行信用風(fēng)險(xiǎn)精準(zhǔn)度量的建模過(guò)程，系統(tǒng)分析了其影響因素包括財(cái)務(wù)因素、行業(yè)屬性及宏觀經(jīng)濟(jì)環(huán)境，得出了7個(gè)方面共40個(gè)指標(biāo)構(gòu)建的精準(zhǔn)度量體系，并建立了以反向逐步選擇法篩選變量、縮減指標(biāo)數(shù)據(jù)的因子分析為基礎(chǔ)的邏輯回歸模型。 最后，使用Clementine數(shù)據(jù)挖掘工具，根據(jù)選取的120個(gè)樣本進(jìn)行實(shí)證研究，利用其財(cái)務(wù)報(bào)表、行業(yè)屬性及宏觀經(jīng)濟(jì)數(shù)據(jù)，使用KMO檢驗(yàn)、Bartlett球體檢驗(yàn)的因子分析方法對(duì)指標(biāo)進(jìn)行降維，按照“CRISP-DM”數(shù)據(jù)挖掘流程建立了邏輯回歸（LOGISTIC）模型。經(jīng)過(guò)隨機(jī)樣本檢測(cè)，，模型的準(zhǔn)確性和穩(wěn)定性較好，度量結(jié)果較為理想。結(jié)果表明，數(shù)據(jù)挖掘技術(shù)在商業(yè)銀行企業(yè)客戶信用風(fēng)險(xiǎn)度量中具有較好的預(yù)測(cè)效果。
[Abstract]:Credit risk is one of the main risks of commercial banks. According to McKinsey & Company's research, credit risk accounts for more than 60% of the total bank risk, more than three times the sum of market risk and operational risk. As a key factor affecting the rise and fall of banking industry, credit risk has always been a hot research issue for domestic and foreign scholars and government regulators. With the impact of the global financial crisis and the expansion of credit transactions, commercial banks face the trend of diversified forms of credit risk and complicated operation. The accuracy and sensitivity of credit risk measurement are the weak links of banking risk management. How to define credit risk and apply effective risk management measures is an important issue for commercial banks to pursue effective profits and ensure long-term and effective continuous operation. It is also a livelihood issue to protect the interests of investors and social stability. From the point of view of commercial banks and taking the establishment of credit risk measurement index system as the core, this paper puts forward the accurate credit risk measurement model of commercial banks based on data mining technology, especially the customers of listed enterprises. We hope to provide some technical reference and method basis for commercial bank credit risk measurement. Firstly, the paper discusses the theory of customer credit risk measurement of commercial bank company, and redefines the customer credit risk of commercial bank company based on the Basel Accord and the current situation. This paper introduces the model and method of credit risk precision measurement, and studies the application of data mining technology in commercial bank risk management, especially the necessity and feasibility of credit risk measurement. Secondly, the modeling process of accurate measurement of credit risk of commercial banks is introduced in detail from two aspects of index system construction and model construction, and the influencing factors, including financial factors, industry attributes and macroeconomic environment, are systematically analyzed. An accurate measurement system of 40 indexes in 7 aspects is obtained, and a logical regression model based on factor analysis of variable selection and reduction index data is established. Finally, using Clementine data mining tools, according to the selected 120 samples for empirical research, using its financial statements, industry attributes and macroeconomic data, using KMO test, Bartlett sphere test factor analysis method to reduce the index dimension. According to the data mining flow of "CRISP-DM", the logical regression (LOGISTIC) model is established. After random sample detection, the model has good accuracy and stability, and the measurement result is ideal. The results show that the data mining technology has a good prediction effect in the measurement of customer credit risk in commercial banks.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP311.13;F832.4

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