Combining Market and Accounting-Based Models for Credit Scoring Using a Classification Scheme Based on Support Vector Machines

2012 ◽  
Author(s):  
Dimitrios Niklis ◽  
Michael Doumpos ◽  
C. Zopounidis
Keyword(s):  
Support Vector Machines ◽  
Classification Scheme ◽  
Credit Scoring ◽  
Support Vector ◽  
Vector Machines

CREDIT SCORING MODELS WITH AUC MAXIMIZATION BASED ON WEIGHTED SVM

2009 ◽  
Vol 08 (04) ◽  
pp. 677-696 ◽  
Author(s):  
LIGANG ZHOU ◽  
KIN KEUNG LAI ◽  
JEROME YEN
Keyword(s):  
Support Vector Machines ◽  
Quantitative Methods ◽  
Credit Scoring ◽  
Feature Weighting ◽  
Computational Time ◽  
Support Vector ◽  
Operating Characteristics ◽  
Auc Maximization ◽  
Vector Machines ◽  
Rbf Kernel

Credit scoring models are very important tools for financial institutions to make credit granting decisions. In the last few decades, many quantitative methods have been used for the development of credit scoring models with focus on maximizing classification accuracy. This paper proposes the credit scoring models with the area under receiver operating characteristics curve (AUC) maximization based on the new emerged support vector machines (SVM) techniques. Three main SVM models with different features weighted strategies are discussed. The weighted SVM credit scoring models are tested using 10-fold cross validation with two real world data sets and the experimental results are compared with other six traditional methods including linear regression, logistic regression, k nearest neighbor, decision tree, and neural network. Results demonstrate that weighted 2-norm SVM with radial basis function (RBF) kernel function and t-test feature weighting strategy has the overall better performance with very narrow margin than other SVM models. However, it also consumes more computational time. In considering the balance of performance and time, least squares support vector machines (LSSVM) with RBF kernel maybe a better choice for large scale credit scoring applications.

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