基于客户价值加权机器学习模型的商业银行客户流失预测

IF 0.3 4区经济学 Q4 Economics, Econometrics and Finance

Journal of Credit Risk Pub Date : 2021-01-01 DOI:10.21314/jcr.2021.011

Zongxiao Wu, Zhiyong Li

引用次数: 1

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Customer churn prediction for commercial banks using customer-value-weighted machine learning models

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来源期刊

Journal of Credit Risk BUSINESS, FINANCE-

CiteScore

0.90

自引率

0.00%

发文量

期刊介绍： With the re-writing of the Basel accords in international banking and their ensuing application, interest in credit risk has never been greater. The Journal of Credit Risk focuses on the measurement and management of credit risk, the valuation and hedging of credit products, and aims to promote a greater understanding in the area of credit risk theory and practice. The Journal of Credit Risk considers submissions in the form of research papers and technical papers, on topics including, but not limited to: Modelling and management of portfolio credit risk Recent advances in parameterizing credit risk models: default probability estimation, copulas and credit risk correlation, recoveries and loss given default, collateral valuation, loss distributions and extreme events Pricing and hedging of credit derivatives Structured credit products and securitizations e.g. collateralized debt obligations, synthetic securitizations, credit baskets, etc. Measuring managing and hedging counterparty credit risk Credit risk transfer techniques Liquidity risk and extreme credit events Regulatory issues, such as Basel II, internal ratings systems, credit-scoring techniques and credit risk capital adequacy.