Welcome to the InfoSci Platform
Neural-Symbolic Processing in Business Applications: Credit Card Fraud Detection

Neural-Symbolic Processing in Business Applications: Credit Card Fraud Detection

Nick F. Ryman-Tubb
Copyright: © 2011 |Pages: 45
ISBN13: 9781609600211|ISBN10: 1609600215|EISBN13: 9781609600235
DOI: 10.4018/978-1-60960-021-1.ch012
Cite Chapter Cite Chapter

MLA

Ryman-Tubb, Nick F. "Neural-Symbolic Processing in Business Applications: Credit Card Fraud Detection." Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications, edited by Eduardo Alonso and Esther Mondragón, IGI Global, 2011, pp. 270-314. https://doi.org/10.4018/978-1-60960-021-1.ch012

APA

Ryman-Tubb, N. F. (2011). Neural-Symbolic Processing in Business Applications: Credit Card Fraud Detection. In E. Alonso & E. Mondragón (Eds.), Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications (pp. 270-314). IGI Global. https://doi.org/10.4018/978-1-60960-021-1.ch012

Chicago

Ryman-Tubb, Nick F. "Neural-Symbolic Processing in Business Applications: Credit Card Fraud Detection." In Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications, edited by Eduardo Alonso and Esther Mondragón, 270-314. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-021-1.ch012

Export Reference

Mendeley
Favorite

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.