{"title":"机器学习驱动的银行消费金融贷款决策","authors":"Xiaoning Wang, Yi Tang, A. Quaranta","doi":"10.4018/ijisscm.348337","DOIUrl":null,"url":null,"abstract":"This paper investigates the bank lending decision process for internet consumer finance using machine learning. It focuses on microloans and compares Logistic Regression and GBDT models for credit risk assessment. Variables are filtered and recoded via Information Value and WoE methods to enhance discrimination between defaulting and performing users. Experimental results utilizing these models predict credit risk and optimize using AUC values. Additionally, it develops a fixed-effect regression model to explore how bank-specific factors affect systemic risk, revealing that larger banks reduce risk, while higher returns, non-performing loans, and equity volatility elevate it, with inconclusive effects from leverage ratio.","PeriodicalId":44506,"journal":{"name":"International Journal of Information Systems and Supply Chain Management","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning-Driven Lending Decisions in Bank Consumer Finance\",\"authors\":\"Xiaoning Wang, Yi Tang, A. Quaranta\",\"doi\":\"10.4018/ijisscm.348337\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper investigates the bank lending decision process for internet consumer finance using machine learning. It focuses on microloans and compares Logistic Regression and GBDT models for credit risk assessment. Variables are filtered and recoded via Information Value and WoE methods to enhance discrimination between defaulting and performing users. Experimental results utilizing these models predict credit risk and optimize using AUC values. Additionally, it develops a fixed-effect regression model to explore how bank-specific factors affect systemic risk, revealing that larger banks reduce risk, while higher returns, non-performing loans, and equity volatility elevate it, with inconclusive effects from leverage ratio.\",\"PeriodicalId\":44506,\"journal\":{\"name\":\"International Journal of Information Systems and Supply Chain Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2024-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Information Systems and Supply Chain Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijisscm.348337\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Information Systems and Supply Chain Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijisscm.348337","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MANAGEMENT","Score":null,"Total":0}
Machine Learning-Driven Lending Decisions in Bank Consumer Finance
This paper investigates the bank lending decision process for internet consumer finance using machine learning. It focuses on microloans and compares Logistic Regression and GBDT models for credit risk assessment. Variables are filtered and recoded via Information Value and WoE methods to enhance discrimination between defaulting and performing users. Experimental results utilizing these models predict credit risk and optimize using AUC values. Additionally, it develops a fixed-effect regression model to explore how bank-specific factors affect systemic risk, revealing that larger banks reduce risk, while higher returns, non-performing loans, and equity volatility elevate it, with inconclusive effects from leverage ratio.
期刊介绍:
The International Journal of Information Systems and Supply Chain Management (IJISSCM) provides a practical and comprehensive forum for exchanging novel research ideas or down-to-earth practices which bridge the latest information technology and supply chain management. IJISSCM encourages submissions on how various information systems improve supply chain management, as well as how the advancement of supply chain management tools affects the information systems growth. The aim of this journal is to bring together the expertise of people who have worked with supply chain management across the world for people in the field of information systems.