信貸審批數(shù)據(jù)建模是信貸行業(yè)的重要研究課題。隨著這一領(lǐng)域的快速發(fā)展,信用違約預(yù)測(cè)(CDP)分類(lèi)器被廣泛應(yīng)用于對(duì)客戶信用評(píng)估和大額貸款組合的審批。違約風(fēng)險(xiǎn)預(yù)測(cè)數(shù)據(jù)建模是模式識(shí)別理論背景下的二元分類(lèi)問(wèn)題,其目的是將新的觀察結(jié)果傳遞給預(yù)先定義的決策類(lèi)。信用風(fēng)險(xiǎn)決策的結(jié)果受兩個(gè)主要因素的影響:選擇準(zhǔn)確的特征選擇算法來(lái)尋找合理的指標(biāo)特征集,選擇合適的分類(lèi)模型來(lái)構(gòu)造進(jìn)行決策。財(cái)務(wù)風(fēng)險(xiǎn)管理是用于探索許多重要參數(shù)的最具研究前景的問(wèn)題之一。銀行業(yè)包括影響銀行及其利益相關(guān)者的眾多風(fēng)險(xiǎn)因素。CDP與銀行有著緊密的聯(lián)系,它是一種適用于銀行資金借貸的有效而決定性的技術(shù)。獲取積累有關(guān)債權(quán)人、監(jiān)管機(jī)構(gòu)、其他金融和非金融公司、政府等方面的數(shù)據(jù),對(duì)信用風(fēng)險(xiǎn)進(jìn)行監(jiān)管是非常重要的。同時(shí),CDP對(duì)于向客戶提供貸款的集中評(píng)估也是十分重要的。此外,CDP方法有助于將信譽(yù)好的客戶與信譽(yù)差的客戶區(qū)分開(kāi)來(lái)。這意味著,一些信貸客戶擁有良好的信用資質(zhì);相應(yīng)地,銀行可以將他們歸類(lèi)為“有償付能力的債權(quán)人”。相反,還有一些沒(méi)有良好信用資質(zhì)的客戶,因此被歸類(lèi)為“無(wú)償付能力的債權(quán)人”。然而,值得注意的是,這種直接的分類(lèi)程序可能無(wú)法提供最佳的信用風(fēng)險(xiǎn)管...

【文章頁(yè)數(shù)】：182 頁(yè)

【學(xué)位級(jí)別】：博士

【文章目錄】：
Abstract
摘要
1 Introduction
    1.1 Background
    1.2 Research Motivation
    1.3 Research Ideas and Methods
    1.4 Research Questions and Research Objectives
    1.5 Research Content and Structure Arrangement
    1.6 Relation and Differences of Main Contents
        1.6.1 Differences based on Methodology
        1.6.2 Differences based on Characteristics
    1.7 Research Contributions
2 Literature Review
    2.1 Credit Default Risk Prediction:A Theoretical Background
        2.1.1 Credit Default Definitions
        2.1.2 Five Cs Good Lending Concept
        2.1.3 Judgmental Systems Versus Credit Default Prediction Systems
        2.1.4 Risk Management
        2.1.5 Sound Default Risk Management
        2.1.6 Benefits and Criticism of Credit Default Risk Prediction
    2.2 Empirical Literature
        2.2.1 Individual Feature Selection to Default Risk Prediction Based onSupport Vector Machine
        2.2.2 Group Feature Selection to Default Risk Prediction Based on SupportVector Machine
        2.2.3 Hybrid Model for Default Risk Prediction Based on LogitSVM andLogitNeural Algorithms
3 Individual Feature Selection to Default Risk Prediction Based on Support VectorMachine
    3.1 Background
    3.2 Motivation
    3.3 Individual Feature Selection Models
        3.3.1 T-test Approach
        3.3.2 Discriminant Analysis Approach
        3.3.3 Logistic Regression Approach
        3.3.4 CHAID Decision Tree
        3.3.5 QUEST Decision Tree
    3.4 Default Risk Prediction Model
        3.4.1 Support Vector Machine
    3.5 Dataset
        3.5.1 Data Division
    3.6 Performance Measure
    3.7 Empirical Results
        3.7.1 Selecting Significant Features
        3.7.2 Discriminant Analysis
        3.7.3 Logistic Regression
        3.7.4 Decision Trees
        3.7.5 Support Vector Machines
        3.7.6 Type Ⅰ Error, Type Ⅱ Error, and EMCC
        3.7.7 Comparisons of Model's Predictability
        3.7.8 Relative Importance of Selected Features
    3.8 Summary
        3.8.1 Main Results
        3.8.2 Main Conclusion
        3.8.3 Main Characteristics
4 Group Feature Selection to Default Risk Prediction Based on Support VectorMachine
    4.1 Background
    4.2 Motivation
    4.3 'New Age' Group Feature Selectors
        4.3.1 Ridge Regression
        4.3.2 Least Angle Regression
        4.3.3 Lasso (Least Absolute Shrinkage and Selection Operator)
        4.3.4 Gradient Boosted Feature Selection
        4.3.5 Random Forest
    4.4 Default Risk Prediction Models
        4.4.1 Support Vector Machine
        4.4.2 Multilayer Perceptron
        4.4.3 Radial Basis Function
        4.4.4 Classification and Regression Tree
    4.5 Datasets
        4.5.1 Cross-Validation
    4.6 Model's Parameter
    4.7 Performance Measure
    4.8 Statistical Significance Test
    4.9 Empirical Results
        4.9.1 Significant Feature Sets
        4.9.2 Results from Different Datasets
        4.9.3 Credit Default Prediction Average Results
        4.9.4 Cost of Default Prediction Errors
        4.9.5 Verification of Feature Importance
        4.9.6 Robustness Check
    4.10 Summary
        4.10.1 Main Results
        4.10.2 Main Conclusion
        4.10.3 Main Characteristics
5 Hybrid Model for Default Risk Prediction Based on LogitSVM and LogitNeuralModels
    5.1 Background
    5.2 Motivation
    5.3 Default Risk Prediction Hybrid Models
        5.3.1 Logistic Regression
        5.3.2 Neural Network Architecture
    5.4 Datasets
        5.4.1 Training Schemes (TSs)
    5.5 Performance Measure
    5.6 Empirical Results
        5.6.1 Model Prediction
        5.6.2 Type Ⅰ and Type Ⅱ errors with their Corresponding Cost-Benefit Scores
        5.6.3 Selecting the Optimal TS Ratio
        5.6.4 The Most Contributed Feature
        5.6.5 Comparison to the Perfect Models
        5.6.6 LSVM, LNA, SVM, and BPN:A Global Comparison
    5.7 Summary
        5.7.1 Main Results
        5.7.2 Main Conclusion
        5.7.3 Main Characteristics
6 Conclusion
    6.1 Main Conclusion
    6.2 Main Findings
    6.3 Main Contributions
    6.4 Policy Implications
    6.5 Future Roadmaps
References
Appendix A
Appendix B
Publications during PhD Period
Acknowledgement
Curriculum Vitae



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