%0 Conference Proceedings
%T Neural Network Rule Extraction to Detect Credit Card Fraud
%+ University of Surrey (UNIS)
%A Ryman-Tubb, Nick, F.
%A Krause, Paul
%Z Part 4: Financial and Management Applications of AI
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI)
%C Corfu, Greece
%Y Lazaros Iliadis
%Y Chrisina Jayne
%I Springer
%3 Engineering Applications of Neural Networks
%V AICT-363
%N Part I
%P 101-110
%8 2011-09-15
%D 2011
%R 10.1007/978-3-642-23957-1_12
%K fraud detection
%K credit card
%K neural applications
%K rule extraction
%K neuro-symbolic
%K data mining
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-01571371/document
%2 https://inria.hal.science/hal-01571371/file/978-3-642-23957-1_12_Chapter.pdf
%L hal-01571371
%U https://inria.hal.science/hal-01571371
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-363
%~ IFIP-EANN