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首页> 外文期刊>International journal of risk assessment and management >An empirical study on customer risk management in banking industry: applying k-means and RFM methods (evidence from two Iranian private banks)
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An empirical study on customer risk management in banking industry: applying k-means and RFM methods (evidence from two Iranian private banks)

机译：银行业客户风险管理的实证研究：应用k均值和RFM方法（来自两家伊朗私人银行的证据）

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This paper aims to study customer risk management in the banking industry. For this purpose, notions and backgrounds of customer relationship management (CRM), risk and risk management, classifying and clustering methods as well as multiple criteria decision-making methods (MCDM) have been studied. Since, to manage customer credit risks, recognising and classifying them is critical, therefore 150 legal customers and 100 general customers from two private banks in Iran have been selected. K-means algorithm has been proposed for clustering both general and legal customers, moreover a WRFM model has been applied to classify general customers based on customer loyalty properties. A technique for order preference by similarity to ideal solution (TOPSIS) has been used for prioritising general customers based on loyalty properties of the RFM model. On the other hand in order to calculate the relative importance coefficient or weight of loyally properties in the WRFM method, the pairwise comparison matrix based on the analytical hierarchy process (AHP) has been applied.
机译：本文旨在研究银行业的客户风险管理。为此，研究了客户关系管理（CRM），风险和风险管理，分类和聚类方法以及多准则决策方法（MCDM）的概念和背景。由于要管理客户信用风险，识别和分类风险至关重要，因此，已从伊朗的两家私人银行中选择了150名合法客户和100名一般客户。提出了K-means算法来对普通客户和合法客户进行聚类，此外，已采用WRFM模型根据客户忠诚度属性对普通客户进行分类。通过类似于理想解决方案（TOPSIS）的订单偏好技术已被用于根据RFM模型的忠诚度对一般客户进行优先排序。另一方面，为了在WRFM方法中计算忠诚度的相对重要性系数或权重，已应用了基于层次分析法（AHP）的成对比较矩阵。

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