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Data Mining Approaches in Personal Loan Approval |
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| รหัสดีโอไอ | |
| Creator | Kuannabha Pimcharee |
| Title | Data Mining Approaches in Personal Loan Approval |
| Contributor | Olarik Surinta |
| Publisher | Faculty of Engineering Mahasasakham University |
| Publication Year | 2565 |
| Journal Title | Engineering Access |
| Journal Vol. | 8 |
| Journal No. | 1 |
| Page no. | 15-21 |
| Keyword | Support Vector Machine, Multi-Layer Perceptron, Decision Tree, Feature Selection, Chi-square, Information Gain, Personal Loan Approval |
| URL Website | https://ph02.tci-thaijo.org/index.php/mijet/index |
| Website title | THAIJO Engineering Access |
| ISSN | 2730-4175 |
| Abstract | The approval of a bank's credit for an individual loan requires the fulfillment of several requirements, such as bank credit policy, loan amount, the purpose of the loan, and repayment ability. However, every type of credit is subject to the risk of non-repayment and non-performing loans, which affect the liquidity of the bank's operation. This research studied the application of data mining techniques to identify key factors for the loan decisions of a bank. The main objective was to compare the data mining process of personal loan approval process with and without feature selection techniques. For the experiments, the first step was to create the data mining models using three methods, including support vector machine (SVM), multi-layer perceptron (MLP), and decision tree. The results showed that the SVM method outperformed other data mining methods. Second, we experimented with feature selection techniques consisting of Chi-square and information gain. The Chi-square considered the ten factors, while information gain selected the best three factors. The experimental results showed that the Chi-square and information gain combined with the MLP method obtained an accuracy rate of 90.40% and 91.70%, respectively. Therefore, this research concluded that the SVM classifier without combining the feature selection method is the best method to use in personal credit evaluation. |
