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Transcript
Credit scoring with a data mining
approach based on support vector
machines
Cheng-Lung Huang
Mu-Chen Chen
Chieh-Jen Wang
Expert Systems with Applications Nov.2007
Introduction



The credit card industry has been growing
rapidly recently and competition in the
consumer credit market has become severe.
The credit scoring manager often evaluates
the consumer’s credit with intuitive
experience.
If manager can accurately evaluate the
applicant’s credit score by machine learning
classification systems maybe better than
intuitive experience.
Algorithm




SVM
- SVM + Grid
- SVM + Grid + F-score
- SVM + GA
Neural Network
Decision Tree
Genetic programming
Grid Search
F-Score
SVM+Grid+F-Score
Grid Search
Grid Search
SVM-GA
Dataset
Population
Scaling
Training set
Parameter
genes
Feature
genes
Phenotype of
feature genes
Selected feature subset
Training set
with selected
feature subset
Converting
genotype to
phenotype
Phenotype of
parameter genes
Testing set
Testing set
with selected
feature subset
Training SVM
classifier
Trained SVM
classifier
Classfication accuracy for
testing set
Fitness evaluation
Genetic
operatation
No
Termination
are satisfied?
Yes
Optimized (C,  ) and
feature subset
Data Set
SVM Result Summary
Australian Result
German Result
Importance of features for
Australian
Importance of features for
German
Conclusion



It is evident that the SVM-based model is very
competitive to BPN and GP in terms of classification
accuracy.
Compared with GP and BPN, SVM-based credit
scoring model can achieve identical classificatory
accuracy.
The SVM-based approach credit scoring model can
properly classify the applications as either accepted
or rejected, thereby minimizing the creditors’ risk
and translating considerably into future savings.