The problem of credit-risk evaluation is a very challenging and important financial analysis problem. Recently, researchers have found that neural networks perform very well for this complex and unstructured problem when compared to more traditional statistical approaches. 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 analyzing two real life credit-risk evaluation 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 human experts in explaining why a particular decision is made. Furthermore, we also discuss how these rules can be visualized as a decision table in a compact and intuitive graphical format. Hence, extracting rules from trained neural networks and representing these rules as a decision table may offer a viable and valuable alternative for building credit-risk evaluation expert systems.