||Credit card fraud is a major problem in the financial industry. It is responsible for billions of dollars in losses per annum globally. This work develops a methodology and resulting system prototype for fraud detection on credit card transaction data. The detection engine is based on Artificial Neural Networks (ANNs). The ANNs are tuned in three aspects by Genetic Algorithms (GAs), namely in the determination of the optimum set of input factors to the ANN, the determination of the optimum topology of the ANN, and the determination of the optimum weights connecting the ANN neurons. The purpose of this research is to determine whether any specific choice of application of one or more GAs to certain aspects of the ANN dominates other choices. Care was taken to deal with the different cost structure of false positive and false negative results. The detection engine prototype was trained on subsets of a labeled data set from a major financial institution that covers all transactions that were made in a period of 13 months on cards issued by that institution. The results of our investigations are encouraging in that GAs applied to ANNs for credit card fraud detection can improve detection engine performance as expressed through minimization of the objective function value when applied to weight optimization and input feature selection, while there are only mixed results for topology optimization.