Applying Support Vector Machines to Predictar Loan Defaults
Date Issued
2009
Date
2009
Author(s)
Chiang, Yi-Chien
Abstract
The recent financial crises have called for better credit evaluation. Traditionally,ar loans constitute an important portion of credit lending for an economy.etermining the default probability of a car loan is a major task for consumeranking and automobile sales companies. On the one hand, the lender will suffer aoss if a granted car loan eventually defaults; on the other hand, the lender will lose aotential gain if a good loan prospect is mistakenly rejected.revious studies have developed models for credit evaluation. These modelsnclude regression analysis, expert systems, and data mining techniques. Using datarom an automobile company, this study applies support vector machines to buildlassifiers for car loan default prediction.y adopting the grid search approach to adjust kernel parameters and using theilter method and wrapper method to select features, this study find that a classifierith just 5 features possess the best classification power. The classification accuracyate is 77.43%. By comparison, a step-wise logistic regression model has alassification accuracy rate of 75.70%. The results show that (1) support vectorachines have better classification power than logistic regression, and (2)onsidering more factors does not necessary result in better classification.
Subjects
Support Vector Machines
SVMs
logistic regression
car loan
default
information quality
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