{"title":"信用卡违约还款预测模型研究","authors":"","doi":"10.1016/j.jfds.2024.100136","DOIUrl":null,"url":null,"abstract":"<div><p>This study compares the predictive ability of various machine learning models for credit card default repayment within different prediction frameworks, using data from a commercial bank in China. Firstly, utilizing different tree models, we explore the impact on post-default repayment of different factors. Next, a split-sample time series prediction is carried out with two neural network algorithms, BPNN and ELM. The outcomes indicate that, ELM yields a significantly superior prediction performance compared to the BPNN model. Thirdly, the predictive performances of ten machine learning models are compared using full-sample data. The findings demonstrate that XGBoost and ELM models have superior predictive performances in full-sample analyses. Fourthly, this study employs the EMD data decomposition technique to examine the predictive ability of the XGBoost and ELM models in various frequency data. The results indicate that the predictive efficacy may differ depending on the frequency and repayment period after default. The findings are valuable for commercial banks in developing a framework and selecting a methodology to address the challenge of predicting credit card default payments.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918824000217/pdfft?md5=21435a376ab3e2fef9741931c14d8cf4&pid=1-s2.0-S2405918824000217-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Research on credit card default repayment prediction model\",\"authors\":\"\",\"doi\":\"10.1016/j.jfds.2024.100136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study compares the predictive ability of various machine learning models for credit card default repayment within different prediction frameworks, using data from a commercial bank in China. Firstly, utilizing different tree models, we explore the impact on post-default repayment of different factors. Next, a split-sample time series prediction is carried out with two neural network algorithms, BPNN and ELM. The outcomes indicate that, ELM yields a significantly superior prediction performance compared to the BPNN model. Thirdly, the predictive performances of ten machine learning models are compared using full-sample data. The findings demonstrate that XGBoost and ELM models have superior predictive performances in full-sample analyses. Fourthly, this study employs the EMD data decomposition technique to examine the predictive ability of the XGBoost and ELM models in various frequency data. The results indicate that the predictive efficacy may differ depending on the frequency and repayment period after default. The findings are valuable for commercial banks in developing a framework and selecting a methodology to address the challenge of predicting credit card default payments.</p></div>\",\"PeriodicalId\":36340,\"journal\":{\"name\":\"Journal of Finance and Data Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2405918824000217/pdfft?md5=21435a376ab3e2fef9741931c14d8cf4&pid=1-s2.0-S2405918824000217-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Finance and Data Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2405918824000217\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Finance and Data Science","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405918824000217","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0
摘要
本研究利用中国一家商业银行的数据,比较了各种机器学习模型在不同预测框架下对信用卡违约还款的预测能力。首先,利用不同的树模型,我们探讨了不同因素对违约后还款的影响。其次,利用 BPNN 和 ELM 两种神经网络算法进行分样本时间序列预测。结果表明,ELM 的预测性能明显优于 BPNN 模型。第三,使用全样本数据比较了十种机器学习模型的预测性能。结果表明,在全样本分析中,XGBoost 和 ELM 模型具有更优越的预测性能。第四,本研究采用 EMD 数据分解技术来检验 XGBoost 和 ELM 模型在各种频率数据中的预测能力。结果表明,违约频率和违约后的还款期不同,预测效果也可能不同。研究结果对商业银行制定框架和选择方法以应对信用卡违约还款预测挑战很有价值。
Research on credit card default repayment prediction model
This study compares the predictive ability of various machine learning models for credit card default repayment within different prediction frameworks, using data from a commercial bank in China. Firstly, utilizing different tree models, we explore the impact on post-default repayment of different factors. Next, a split-sample time series prediction is carried out with two neural network algorithms, BPNN and ELM. The outcomes indicate that, ELM yields a significantly superior prediction performance compared to the BPNN model. Thirdly, the predictive performances of ten machine learning models are compared using full-sample data. The findings demonstrate that XGBoost and ELM models have superior predictive performances in full-sample analyses. Fourthly, this study employs the EMD data decomposition technique to examine the predictive ability of the XGBoost and ELM models in various frequency data. The results indicate that the predictive efficacy may differ depending on the frequency and repayment period after default. The findings are valuable for commercial banks in developing a framework and selecting a methodology to address the challenge of predicting credit card default payments.