research-article

Neural fraud detection in credit card operations

Authors:

J. R. Dorronsoro,

F. Ginel,

C. Sgnchez,

C. S. CruzAuthors Info & Claims

IEEE Transactions on Neural Networks, Volume 8, Issue 4

Pages 827 - 834

https://doi.org/10.1109/72.595879

Published: 01 July 1997 Publication History

Abstract

This paper presents an online system for fraud detection of credit card operations based on a neural classifier. Since it is installed in a transactional hub for operation distribution, and not on a card-issuing institution, it acts solely on the information of the operation to be rated and of its immediate previous history, and not on historic databases of past cardholder activities. Among the main characteristics of credit card traffic are the great imbalance between proper and fraudulent operations, and a great degree of mixing between both. To ensure proper model construction, a nonlinear version of Fisher's discriminant analysis, which adequately separates a good proportion of fraudulent operations away from other closer to normal traffic, has been used. The system is fully operational and currently handles more than 12 million operations per year with very satisfactory results

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Paldino GLebichot BLe Borgne YSiblini WOblé FBoracchi GBontempi G(2024)The role of diversity and ensemble learning in credit card fraud detectionAdvances in Data Analysis and Classification10.1007/s11634-022-00515-518:1(193-217)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s11634-022-00515-5
Singh AGupta SVarshney TVerma AGoel LYadav A(2022)A Hybrid Machine Learning Approach for Credit Card Fraud DetectionInternational Journal of Information Technology Project Management10.4018/IJITPM.31342013:3(1-13)Online publication date: 4-Nov-2022
https://dl.acm.org/doi/10.4018/IJITPM.313420
Kanika Singla J(2022)A novel framework for online transaction fraud detection system based on deep neural networkJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21261643:1(927-937)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/JIFS-212616
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Neural fraud detection in credit card operations
1. Computing methodologies

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cover image IEEE Transactions on Neural Networks

IEEE Transactions on Neural Networks Volume 8, Issue 4

July 1997

150 pages

ISSN:1045-9227

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IEEE Press

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Published: 01 July 1997

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Paldino GLebichot BLe Borgne YSiblini WOblé FBoracchi GBontempi G(2024)The role of diversity and ensemble learning in credit card fraud detectionAdvances in Data Analysis and Classification10.1007/s11634-022-00515-518:1(193-217)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s11634-022-00515-5
Singh AGupta SVarshney TVerma AGoel LYadav A(2022)A Hybrid Machine Learning Approach for Credit Card Fraud DetectionInternational Journal of Information Technology Project Management10.4018/IJITPM.31342013:3(1-13)Online publication date: 4-Nov-2022
https://dl.acm.org/doi/10.4018/IJITPM.313420
Kanika Singla J(2022)A novel framework for online transaction fraud detection system based on deep neural networkJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21261643:1(927-937)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/JIFS-212616
Hong ZYang HWang HLyu WYang YWang GLiu YWang YZhang DRenz MSarwat M(2022)FastAddrProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3560999(1-10)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3557915.3560999
Chen BLi JLu XSha CWang XZhang J(2021)Incorporating Network Structure with Node Information for Semi-supervised Anomaly Detection on Attributed GraphsWeb Information Systems Engineering – WISE 202110.1007/978-3-030-90888-1_20(242-257)Online publication date: 26-Oct-2021
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Arora VLeekha RLee KKataria A(2020)Facilitating User Authorization from Imbalanced Data Logs of Credit Cards Using Artificial IntelligenceMobile Information Systems10.1155/2020/88852692020Online publication date: 30-Oct-2020
https://dl.acm.org/doi/10.1155/2020/8885269
Daliri SLoyola-González O(2020)Using Harmony Search Algorithm in Neural Networks to Improve Fraud Detection in Banking SystemComputational Intelligence and Neuroscience10.1155/2020/65034592020Online publication date: 8-Feb-2020
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Zhang YYang WSun WYe KChen MXu C(2019)The Constrained GAN with Hybrid Encoding in Predicting Financial BehaviorArtificial Intelligence and Mobile Services – AIMS 201910.1007/978-3-030-23367-9_2(13-27)Online publication date: 25-Jun-2019
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Abakarim YLahby MAttioui ABerrado ABakkoury ZBouchon-Meunier BRamdani M(2018)An Efficient Real Time Model For Credit Card Fraud Detection Based On Deep LearningProceedings of the 12th International Conference on Intelligent Systems: Theories and Applications10.1145/3289402.3289530(1-7)Online publication date: 24-Oct-2018
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Weller TChampin PGandon FMédini LLalmas MIpeirotis P(2018)Compromised Account Detection Based on Clickstream DataCompanion Proceedings of the The Web Conference 201810.1145/3184558.3186569(819-823)Online publication date: 23-Apr-2018
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Research on Credit Card Fraud Detection Model Based on Distance Sum

Credit card fraud detection based on self-paced ensemble neural network

Fraud detection in credit card transaction using neural networks

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