Neural Network Rule Extraction to Detect Credit Card Fraud - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2011

Neural Network Rule Extraction to Detect Credit Card Fraud

Nick F. Ryman-Tubb
  • Fonction : Auteur
  • PersonId : 1014125
Paul Krause
  • Fonction : Auteur
  • PersonId : 1014126

Résumé

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.
Fichier principal
Vignette du fichier
978-3-642-23957-1_12_Chapter.pdf (241.78 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01571371 , version 1 (02-08-2017)

Licence

Paternité

Identifiants

Citer

Nick F. Ryman-Tubb, Paul Krause. Neural Network Rule Extraction to Detect Credit Card Fraud. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.101-110, ⟨10.1007/978-3-642-23957-1_12⟩. ⟨hal-01571371⟩
138 Consultations
283 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More