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The validation of analytic hierarchy process (AHP) scoring model

机译:层次分析评分模型的验证

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摘要

Credit scoring model is a key element of the automated loan approval system. The developed model is required to be validated under the internal rating-based approach of the BASEL framework. There are several techniques available for validating statistical models. These include KS statistics, C statistics, and HL statistics. When the bank cannot build a statistical model either because it does not have high quality data or it decides to rely on the experts' judgement for certain types of loans, the validation of their credit rating system can be problematic. This paper proposes the use of AHP technique to develop credit scoring model based on experts' judgement. Since the consistency ratio or the geometric consistency index under AHP technique can test only the expert's consistency in their priority settings at the time the model is built, this paper proposes the actual decision validation of the AHP based on the concept of KS statistics. The lower and cut-off scores can then be computed using the bank's acceptable inconsistency rate.
机译:信用评分模型是自动贷款批准系统的关键要素。开发的模型需要在BASEL框架的基于内部评级的方法下进行验证。有几种可用于验证统计模型的技术。这些包括KS统计信息,C统计信息和HL统计信息。当银行由于没有高质量的数据而无法建立统计模型,或者决定依靠专家的判断来确定某些类型的贷款时,其信用评级系统的验证可能会出现问题。提出基于专家判断的层次分析法发展信用评分模型。由于AHP技术下的一致性比率或几何一致性指标只能在模型建立时测试专家在其优先级设置中的一致性,因此本文提出了基于KS统计概念的AHP实际决策验证。然后可以使用银行可接受的不一致率来计算下限和下限分数。

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