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Neural-Symbolic Processing in Business Applications: Credit Card Fraud Detection
Abstract
Neural networks are mathematical models, inspired by biological processes in the human brain and are able to give computers more “human-like” abilities. Perhaps by examining the way in which the biological brain operates, at both the large-scale and the lower level anatomical level, approaches can be devised that can embody some of these remarkable abilities for use in real-world business applications. One criticism of the neural network approach by business is that they are “black boxes”; they cannot be easily understood. To open this black box an outline of neural-symbolic rule extraction is described and its application to fraud-detection is given. Current practice is to build a Fraud Management System (FMS) based on rules created by fraud experts which is an expensive and time-consuming task and fails to address the problem where the data and relationships change over time. By using a neural network to learn to detect fraud and then extracting its’ knowledge, a new approach is presented.
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