[AFR97]
Emin Aleskerov, Bernd Freisleben, Bharat Rao. CARDWATCH: A Neural Network Based Database Mining System for Credit Card Fraud Detection. In: Proceedings of Computa- tional Intelligence for Financial Engineering (CIFEr), S. 220--226, 1997.
[ATW97]
Suhaya Abu-Hakima, Mansour Toloo, Tony White. A Multi-Agent Systems Approach for Fraud Detection in Personal Communication Systems. In: [Faw97], 1997.
[CFPS99]
Philip K. Chan, Wei Fan, Andreas L. Prodromidis, Salvatore J. Stolfo. Distributed Data Mining in Credit Card Fraud Detection. In: IEEE Intelligent Systems, Bd. 14, Nr. 6, S. 67--74, 1999.
[DC98]
J. R. Dorronsoro, C. Santa Cruz. Discrimination of overlapping data and credit card fraud detection. Technischer Bericht, Department of Computer Engineering, Universidad de Madrid, 1998.
[Faw97]
Tom Fawcett.
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Technical Report WS-97-07, Juli 1997, AAAI-Press.
[FP97a]
Tom Fawcett and Foster Provost.
Adaptive Fraud Detection.
Data Mining and Knowledge Discovery, vol. 1, no. 3, p. {291-316}. 1997.
[FP97b]
Tom Fawcett, Foster Provost.
Combining Data Mining and Machine Learning for Effective Fraud Detection. In: [Faw97].
[Gos97]
Phil Gosset. Fraud Detection Concepts: Final Report. ASPeCT -- Project, November 1997. See [ASPeCT].
[GH99]
Phil Gossett, Mark Hyland. Classification, Detection and Prosecution of Fraud on Mobile Networks. Proceedings of ACTS Mobile Summit, Sorrento, Italy, Juni 1999.
[GR94]
Sushmito Ghosh, Douglas L. Reilly. Credit Card Fraud Detection with a Neural-Network. In: Proceedings of the 27th Hawaii International Conference on Information Systems, S. 621-- 630, 1994.
[HDA98]
Mark Hyland, Jos Dumortier, Diana Alonso Blas. Legal Aspects of Fraud Detection. ASPeCT-Project.
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[HS08]
Constantinos S. Hilas, Paris As. Mastorocostas.
An Application of Supervised and Unsupervised Learning Approaches to Telecommunications Fraud Detection.
Knowledge-Based Systems, 21, pp 721 – 726, 2008. doi:10.1016/j.knosys.2008.03.026.
[HS09]
Constantinos S. Hilas, Paris As. Mastorocostas.
Designing an expert system for fraud detection in a private telecommunications network.
An Application of Supervised and Unsupervised Learning Approaches to Telecommunications Fraud Detection.
Expert Systems with Applications. 2009. doi: 10.1016/j.eswa.2009.03.031.
[HS05]
Constantinos S. Hilas, John N. Sahalos.
User profiling for fraud detection in telecommunication networks.
In: 5th International Conference on Technology and Automation, Thessaloniki, Greece, October 2005. pp 382-387.
[HS06]
Constantinos S. Hilas, John N. Sahalos.
Testing the fraud detection ability of different user profiles by means of FFNN classifiers.
In: Collias St. et al ed.. Lecture Notes in Computer Science, vol. 4132, Part II, 2006. pp 872-883.
[HS07]
Constantinos S. Hilas, John N. Sahalos.
An application of decision trees for rule extraction towards telecommunications fraud detection.
In: B. Apolloni et al. (Eds.): KES 2007/ WIRN 2007, Lecture Notes in Artificial Intelligence, vol. 4693, Part II, Springer. 2007, pp. 1112–1121.
[KKN99]
Daniel A. Keim, Eleftherios E. Koutsofios, Stephen C. North. Visual Exploration of Large Telecommunication Data Sets. In: User Interfaces to Data Intensive Systems, S. 12-- 20, 1999.
[MP96]
Yves Moreau, Bart Preneel. Definition of Fraud Detection Concepts. ASPeCT -- Project, August 1996. See [ASPeCT].
[OTA95]
U. S. Congress, Office of Technology Assessment. Information
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[PF97]
Foster Provost, Tom Fawcett. Analysis and Visualization of Classifier
Performance: Comparison under Imprecise Class and Cost
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[SFLPC97]
Salvatore J. Stolfo, David W. Fan, Wenke Lee, Andreas L. Prodromidis, Philip K. Chan. Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results. In: [Faw97].
[Stö97]
Christof Störmann. Fraud Management Tool: Evaluation Report. ASPeCT -
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