One of the key decisions financial institutions have to make as part of their daily operations is to decide whether or not to grant a loan to an applicant. With the emergence of large-scale data-storing facilities, huge amounts of data have been stored regarding the repayment behavior of past applicants. It is the aim of credit scoring to analyze this data and build models that distinguish good from bad payers using characteristics such as amount on savings account, marital status, purpose of loan, etc. Many classification techniques have been suggested to build credit-scoring models. Especially neural networks have in recent years received a lot of attention. However, while they are generally able to achieve a high predictive accuracy rate, the reasoning behind how they reach their decisions is not readily available, which hinders their acceptance by practitioners. Therefore, in [1], we have proposed a two-step process to open the neural network black box which involves: (1) extracting rules from the network; (2) visualizing this rule set using an intuitive graphical representation, such as decision tables or trees.
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