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The application and assessment of co...

Lin, Ruonan.

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The application and assessment of consumer credit scoring models in measuring consumer loan issuing risk of commercial banks in China.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	The application and assessment of consumer credit scoring models in measuring consumer loan issuing risk of commercial banks in China./
Author:	Lin, Ruonan.
Description:	100 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
Contained By:	Dissertation Abstracts International74-10B(E).
Subject:	Applied Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3568517
ISBN:	9781303230875

The application and assessment of consumer credit scoring models in measuring consumer loan issuing risk of commercial banks in China.
Lin, Ruonan.

The application and assessment of consumer credit scoring models in measuring consumer loan issuing risk of commercial banks in China. - 100 p.

Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.

Thesis (Ph.D.)--State University of New York at Stony Brook, 2013.

With the impressive growth of economy, various consumer credit businesses have been expanding in China rapidly during the past two decades. However, due to the low level of risk management methods and relatively laggard techniques being currently used by China's commercial banks, and because there was no mature credit risk control system based on credit scoring models in China, the further development of consumer credit business is seriously hindered. In order to solve this problem, consumer credit scoring, which is a predictive modeling method widely used to predict the probability of customer default, should be used when the commercial banks making decisions in granting credit to individuals. During the last 5 years, China has devoted to building the world's largest personal credit database and established its own personal credit scoring system. But this system still falls behind those system in developed countries such as the U.S.A and needs to be improved. Several scholars in China have tried applying traditional consumer credit scoring techniques to the data in China, among which they found logistic regression was the most robust and neural network had the highest prediction accuracy. In recent years, three new and more advanced credit scoring methods: Support Vector Machine (SVM), Random Forest (RF) and Partial Least Squares Regression (PLSR) came into use in the industry in the U.S.A and have been proved to have higher prediction accuracy and stability.

ISBN: 9781303230875Subjects--Topical Terms:

1669109
Applied Mathematics.

The application and assessment of consumer credit scoring models in measuring consumer loan issuing risk of commercial banks in China.
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035 $a (MiAaPQ)AAI3568517
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100 1 $a Lin, Ruonan. $3 2099723
245 1 4 $a The application and assessment of consumer credit scoring models in measuring consumer loan issuing risk of commercial banks in China.
300 $a 100 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
500 $a Adviser: Jin Wang.
502 $a Thesis (Ph.D.)--State University of New York at Stony Brook, 2013.
520 $a With the impressive growth of economy, various consumer credit businesses have been expanding in China rapidly during the past two decades. However, due to the low level of risk management methods and relatively laggard techniques being currently used by China's commercial banks, and because there was no mature credit risk control system based on credit scoring models in China, the further development of consumer credit business is seriously hindered. In order to solve this problem, consumer credit scoring, which is a predictive modeling method widely used to predict the probability of customer default, should be used when the commercial banks making decisions in granting credit to individuals. During the last 5 years, China has devoted to building the world's largest personal credit database and established its own personal credit scoring system. But this system still falls behind those system in developed countries such as the U.S.A and needs to be improved. Several scholars in China have tried applying traditional consumer credit scoring techniques to the data in China, among which they found logistic regression was the most robust and neural network had the highest prediction accuracy. In recent years, three new and more advanced credit scoring methods: Support Vector Machine (SVM), Random Forest (RF) and Partial Least Squares Regression (PLSR) came into use in the industry in the U.S.A and have been proved to have higher prediction accuracy and stability.
520 $a In this dissertation, all the three latest modeling techniques have been introduced and applied to a dataset obtained from one commercial bank in China. This dataset with Chinese characteristic variables and large proportion of missing values has been carefully analyzed and handled. For the purpose of comparison, three typical and traditional models: logistic regression, classification tree and multi-layer-perceptron neural network models have also been built with the same dataset. Each of the six models has been assessed and tested respectively. A comparison based on various criteria: classification error rate, Kolmogorov Smirnov (KS) test statistic and Area Under Curve (AUC) value has been made between six models. The result of comparison has shown that the Random Forest model outperformed other five models no matter which criterion was applied. Thus Random Forest is a method recommended to be implemented by China's commercial banks in the future.
590 $a School code: 0771.
650 4 $a Applied Mathematics. $3 1669109
650 4 $a Statistics. $3 517247
650 4 $a Economics, Finance. $3 626650
690 $a 0364
690 $a 0463
690 $a 0508
710 2 $a State University of New York at Stony Brook. $b Applied Mathematics and Statistics. $3 1684041
773 0 $t Dissertation Abstracts International $g 74-10B(E).
790 $a 0771
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3568517