The Credit Default Prediction Based on Ensemble Learning－The Case of Credit Card Customers
| Autor: | Chen, Ching-Yi, 陳靜怡 |
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| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 Credit risk is the risk of default on a debt that may arise from a borrower or counterparty failing to make required payment, which has been the main source of risk in most financial institutions. The purpose of this research is to construct an ensemble-learning-based credit risk model, especially based on Blending and Stacking approaches, for credit card default payment prediction. Financial institutions can take countermeasures to avoid losses due to existing customers with default payments, with the help of default alerts provided by our model. We also benchmark the performance of ensemble models against their base classifiers. This paper uses payment data in October, 2005, from an important bank in Taiwan and the targets are existing credit card holders of the bank. Our customer data include the amount of bill statement and previous payment, the past monthly payment records, and personal information etc. In addition to data preprocessing and feature engineering, we conduct Synthetic Minority Oversampling Technique (SMOTE) to deal with our imbalanced data. We use three evaluation metrics that are applicable to credit risk management in practice, such as Type II error, F_1-score, and the value of area under ROC curve, to evaluate the performance of these classification models. The results show that the classification model built based on Stacking approach outperforms base classifiers and Blending approach. The experimental evaluation also shows that ensemble learning has the potential to improve overall classification performance effectively under the premise of the base classifiers generated with high diversity and local accuracy. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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