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. © 2011 International Federation for Information Processing.
CITATION STYLE
Ryman-Tubb, N. F., & Krause, P. (2011). Neural network rule extraction to detect credit card fraud. In IFIP Advances in Information and Communication Technology (Vol. 363 AICT, pp. 101–110). https://doi.org/10.1007/978-3-642-23957-1_12
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