An Ensemble Learning Based Strategy for Customer subdivision and Credit Risk Characterization
An Ensemble Learning Based Strategy for Customer subdivision and Credit Risk Characterization
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DOI：
10.17559/tv-20221220085239
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Shuaiqi Liu;Guiying Wei;WU Sen;Yiyuan SUN-
中科院分区：
文献类型：
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作者：
Shuaiqi Liu;Guiying Wei;WU Sen;Yiyuan SUN-
信贷客户细分和借款人特征是银行和贷款公司评估信贷风险和获取利润的重要工具。本文提出了一种基于异构集成学习方法的多层次违约风险评级策略。在此基础上，构建了信用客户的八细分模型。通过该模型，将信用客户细分为易错过的违约客户、风险最高客户、潜在风险客户、目标客户等8类重要客户。此外，我们描述了客户细分的风险特征，并发现了该机构现有风险评级的不足之处。最后，在10万份真实信用数据上对所探索的策略进行了验证，验证了ESM的有效性。与传统的客户细分及特征研究相比，本文提出了一种基于违约风险视角的信用客户细分新方法，通过8个客户细分更全面地描述了风险特征。
: Credit customer subdivisions and borrower characteristics are essential tools for banks and lending companies to evaluate credit risk and make profits. This study proposes a Multi-Level Default Risk Rating (MLDRR) strategy based on a heterogeneous ensemble learning method. Further, a novel Eight Subdivisions Model (ESM) of credit customers is constructed. Through the model, the credit customers are subdivided into eight important categories, such as the defaulting customers that are easily missed, the customers with the highest risk, customers with the potential risk, and target customers, etc. Moreover, we describe the risk characteristics of the customer subdivisions and find deficiencies in the agency's existing risk ratings. Finally, the explored strategies are validated on one hundred thousand real credit data, demonstrating the effectiveness of ESM. Compared with the traditional customer segmentation and characteristics research, this paper develops a new credit customer segmentation method based on the perspective of default risk and describes the risk characteristics more comprehensively through eight customer subdivisions.