Neural networks have represented a serious barrier-to-entry in their application in automated fraud detection due to their black box and often proprietary nature which is overcome here by combining them with symbolic rule extraction. A Sparse Oracle-based Adaptive Rule extraction algorithm is used to produce comprehensible rules from a neural network to aid the detection of credit card fraud. In this paper, a method to improve this extraction algorithm is presented along with results from a large real-world European credit card data set. Through this application it is shown that neural networks can assist in mission-critical areas of business and are an important tool in the transparent detection of fraud.