Appling Data Mining Technique in Building Credit Scording Model for Consumer Loan Appliaction
Date Issued
2006
Date
2006
Author(s)
Chang, Ching-Kuang
DOI
zh-TW
Abstract
The growth in consumer credit outstanding over the last 2 years is truly spectacular. The bad debt issue generated by credit card and cash card business has not only affect the stability of financial system, but also the foundation of our society. It is pretty sure that the Consumer Loan business will become the next victim of non-performing loan, because the balance will be transferred from card business to consumer loan due to the lower interest rate. To reduce the potential default rate, lenders have to establish a mechanism to discriminate between good and bad customers.
The goal of this study is building a Credit Scoring Model, by applying the advanced Data Mining Technology – Decision Tree algorithms, for Consumer Loan Application. The best model was obtained by evaluating both the accuracy rate and AUC value using the testing data which is 30% of the original sample. The result of this research indicate that
1. Implementing credit scoring model can not only reduce the credit risk, but also increase the overall profit of financial institutions.
2. Rigorous filtering rules are essential to complement the insufficient part of model itself.
3. Scoring Model and loan officer’s verification should not be neglected.
4. Impersonal rules to discriminate between good and bad customers can be produced by Data Mining techniques.
5. The ultimate goal of consumer loan should focus on the profit rather than the loan amount.
6. Consumer loan can be a profitable business to banks, if the credit scoring model can be implemented in a right manner.
Subjects
資料探勘
信用評分
決策樹
Data Mining
Credit Scoring
Decision Tree
Type
other
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