Towards a Comprehensible and Accurate Credit Management Model: Application of Four Computational Intelligence Methodologies

A. Tsakonas, N. Ampazis, G. Dounias
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引用次数: 3

Abstract

The paper presents methods for classification of applicants into different categories of credit risk using four different computational intelligence techniques. The selected methodologies involved in the rule-based categorization task are (1) feedforward neural networks trained with second order methods (2) inductive machine learning, (3) hierarchical decision trees produced by grammar-guided genetic programming and (4) fuzzy rule based systems produced by grammar-guided genetic programming. The data used are both numerical and linguistic in nature and they represent a real-world problem, that of deciding whether a loan should be granted or not, in respect to financial details of customers applying for that loan, to a specific private EU bank. We examine the proposed classification models with a sample of enterprises that applied for a loan, each of which is described by financial decision variables (ratios), and classified to one of the four predetermined classes. Attention is given to the comprehensibility and the ease of use for the acquired decision models. Results show that the application of the proposed methods can make the classification task easier and - in some cases - may minimize significantly the amount of required credit data. We consider that these methodologies may also give the chance for the extraction of a comprehensible credit management model or even the incorporation of a related decision support system in banking
迈向一个可理解和准确的信用管理模型:四种计算智能方法的应用
本文介绍了使用四种不同的计算智能技术将申请人分类为不同类别的信用风险的方法。基于规则的分类任务所涉及的方法有:(1)用二阶方法训练的前馈神经网络;(2)归纳机器学习;(3)由语法引导遗传规划产生的分层决策树;(4)由语法引导遗传规划产生的基于模糊规则的系统。所使用的数据在本质上是数字和语言的,它们代表了一个现实世界的问题,即决定是否应该授予贷款,关于申请该贷款的客户的财务细节,向特定的私人欧盟银行。我们用申请贷款的企业样本来检验提出的分类模型,每个企业都由财务决策变量(比率)描述,并分类到四个预定类别中的一个。注意对获得的决策模型的可理解性和易用性。结果表明,应用所提出的方法可以使分类任务更容易,并且在某些情况下可以显着减少所需的信用数据量。我们认为,这些方法也可能为提取可理解的信贷管理模型提供机会,甚至可以将相关的决策支持系统纳入银行业
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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