Using neural network rule extraction and decision tables for credit-risk evaluation
Using neural network rule extraction and decision tables for credit-risk evaluation
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DOI：
10.1287/mnsc.49.3.312.12739
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发表时间：
2003-03-01
影响因子：
5.4
通讯作者：
Vanthienen, J
中科院分区：
文献类型：
--
作者：
Baesens, B;Setiono, R;Vanthienen, J
信用风险评估是财务分析领域中一个极具挑战性的重要管理科学问题。文献中提出了许多分类方法来解决这个问题。特别是神经网络，由于其具有普遍的近似性质而受到了广泛的关注。然而，使用神经网络进行决策的一个主要缺点是它们缺乏解释能力。虽然他们可以达到很高的预测准确率，但他们如何做出决定背后的原因并不容易获得。在本文中，我们展示了使用神经网络规则提取技术分析三个现实生活中的信用风险数据集的结果。通过解释规则来澄清神经网络决策，这些解释规则捕获嵌入网络中的学习知识，可以帮助信用风险管理者解释为什么特定的申请人被分类。不是好就是坏。此外，我们还讨论了如何将这些规则以紧凑和直观的图形格式可视化为决策表，以方便咨询。研究表明，神经网络规则提取和决策表是一种强大的管理工具，可以帮助我们构建先进的、用户友好的信用风险评估决策支持系统。
Credit-risk evaluation is a very challenging and important management science problem in the domain of financial analysis. Many classification methods have been suggested in the literature to tackle this problem. Neural networks, especially, have received a lot of attention because of their universal approximation property. However, a major drawback associated with the use of neural networks for decision making is their lack of explanation capability. While they can achieve a high predictive accuracy rate, the reasoning behind how they reach their decisions is not readily available. In this paper, we present the results from analysing three real-life credit-risk data sets using neural network rule extraction techniques. Clarifying the neural network decisions by explanatory rules that capture the learned knowledge embedded in the networks can help the credit-risk manager in explaining why a particular applicant is classified. as either bad or good. Furthermore, we also discuss how these rules can be visualized as a decision table in a compact and intuitive graphical format that facilitates easy consultation. It is concluded that neural network rule extraction and decision tables are powerful management tools that allow us to build advanced and user-friendly decision-support systems for credit-risk evaluation.