人工智能衍生技术在银行客户偿付能力预测中的应用——以Cart型决策树(DT)的应用为例

Karim Amzile, Rajaa Amzile
{"title":"人工智能衍生技术在银行客户偿付能力预测中的应用——以Cart型决策树(DT)的应用为例","authors":"Karim Amzile, Rajaa Amzile","doi":"10.5121/csit.2022.120503","DOIUrl":null,"url":null,"abstract":"In this study we applied the CART-type Decision Tree (DT-CART) method derived from artificial intelligence technique to the prediction of the solvency of bank customers, for this we used historical data of bank customers. However we have adopted the process of Data Mining techniques, for this purpose we started with a data preprocessing in which we clean the data and we deleted all rows with outliers or missing values as well as rows with empty columns, then we fixed the variable to be explained (dependent or Target) and we also thought to eliminate all explanatory (independent) variables that are not significant using univariate analysis as well as the correlation matrix, then we applied our CART decision tree method using the SPSS tool. After completing our process of building our model (AD-CART), we started the process of evaluating and testing the performance of our model, by which we found that the accuracy and precision of our model is 71%, so we calculated the error ratios, and we found that the error rate equal to 29%, this allowed us to conclude that our model at a fairly good level in terms of precision, predictability and very precisely in predicting the solvency of our banking customers.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"78 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Application of Techniques Derived from Artificial Intelligence to the Prediction of the Solvency of Bank Customers: Case of the Application of the Cart Type Decision Tree (DT)\",\"authors\":\"Karim Amzile, Rajaa Amzile\",\"doi\":\"10.5121/csit.2022.120503\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study we applied the CART-type Decision Tree (DT-CART) method derived from artificial intelligence technique to the prediction of the solvency of bank customers, for this we used historical data of bank customers. However we have adopted the process of Data Mining techniques, for this purpose we started with a data preprocessing in which we clean the data and we deleted all rows with outliers or missing values as well as rows with empty columns, then we fixed the variable to be explained (dependent or Target) and we also thought to eliminate all explanatory (independent) variables that are not significant using univariate analysis as well as the correlation matrix, then we applied our CART decision tree method using the SPSS tool. After completing our process of building our model (AD-CART), we started the process of evaluating and testing the performance of our model, by which we found that the accuracy and precision of our model is 71%, so we calculated the error ratios, and we found that the error rate equal to 29%, this allowed us to conclude that our model at a fairly good level in terms of precision, predictability and very precisely in predicting the solvency of our banking customers.\",\"PeriodicalId\":91205,\"journal\":{\"name\":\"Artificial intelligence and applications (Commerce, Calif.)\",\"volume\":\"78 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial intelligence and applications (Commerce, Calif.)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5121/csit.2022.120503\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial intelligence and applications (Commerce, Calif.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/csit.2022.120503","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在本研究中,我们运用人工智能技术衍生的cart型决策树(DT-CART)方法来预测银行客户的偿付能力,为此我们使用了银行客户的历史数据。然而,我们采用了数据挖掘技术的过程,为此目的,我们从数据预处理开始,在这个过程中,我们清理数据,我们删除了所有有异常值或缺失值的行,以及空列的行,然后我们固定了要解释的变量(依赖或目标),我们还认为要消除所有解释(独立)变量,使用单变量分析和相关矩阵不显著。然后使用SPSS工具应用CART决策树方法。完成后我们构建我们的模型(AD-CART)的过程,我们开始的过程评估和测试我们的模型的性能时,我们发现我们的模型的准确性和精度是71%,所以我们计算错误的比率,我们发现出错率等于29%,这使我们得出结论,我们的模型在一个相当不错的水平精度、可预见性和精确地预测我们的银行客户的偿付能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
The Application of Techniques Derived from Artificial Intelligence to the Prediction of the Solvency of Bank Customers: Case of the Application of the Cart Type Decision Tree (DT)
In this study we applied the CART-type Decision Tree (DT-CART) method derived from artificial intelligence technique to the prediction of the solvency of bank customers, for this we used historical data of bank customers. However we have adopted the process of Data Mining techniques, for this purpose we started with a data preprocessing in which we clean the data and we deleted all rows with outliers or missing values as well as rows with empty columns, then we fixed the variable to be explained (dependent or Target) and we also thought to eliminate all explanatory (independent) variables that are not significant using univariate analysis as well as the correlation matrix, then we applied our CART decision tree method using the SPSS tool. After completing our process of building our model (AD-CART), we started the process of evaluating and testing the performance of our model, by which we found that the accuracy and precision of our model is 71%, so we calculated the error ratios, and we found that the error rate equal to 29%, this allowed us to conclude that our model at a fairly good level in terms of precision, predictability and very precisely in predicting the solvency of our banking customers.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Methodology of Measurement Intellectualization based on Regularized Bayesian Approach in Uncertain Conditions Stochastic Dual Coordinate Ascent for Learning Sign Constrained Linear Predictors Data Smoothing Filling Method based on ScRNA-Seq Data Zero-Value Identification Batch-Stochastic Sub-Gradient Method for Solving Non-Smooth Convex Loss Function Problems Teaching Reading Skills More Effectively
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1