Credit Card Fraud Detection Using Hybrid Proximal Policy Optimization and Artificial Bee Colony Optimization with Mutual Learning.
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| Title: | Credit Card Fraud Detection Using Hybrid Proximal Policy Optimization and Artificial Bee Colony Optimization with Mutual Learning. |
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| Authors: | Yuanyuan Zhang1 zhangyy4935@163.com |
| Source: | Informatica (03505596). Mar2026, Vol. 50 Issue 1, p3-23. 21p. |
| Subjects: | Credit card fraud, Bees algorithm, Reinforcement learning, Online marketplaces, Artificial neural networks |
| Abstract (English): | The surge in e-commerce has intensified credit cardfraud, resulting in massive global losses and creating an urgent need for stronger detection systems. This research presents an advanced model for detecting credit cardfraud to address challenges often overlooked, such as class imbalance and sensitivity to initial parameter settings. Our model leverages an artificial neural network (ANN) to extract feature vectors necessary for accurate fraud detection. We utilize proximal policy optimization (PPO) to address class imbalance during training of the ANN. PPO improves the treatment of minority classes by assigning higher rewards for correct predictions and more substantial penalties for errors. This approach leads to more balanced learning. Additionally, our model incorporates a mutual learning-based artificial bee colony (ML-ABC) algorithm for efficiently pre-training the parameters of the ANN. Experiments on the Universite Libre de Bruxelles credit card dataset show that the proposed approach achieves 90.197% accuracy and an F-measure of 91.287%. It outperforms the best existing method by about 3%. These results highlight the robustness of the model and its potential for real-world e-commerce fraud detection. [ABSTRACT FROM AUTHOR] |
| Abstract (Slovenian): | Raziskava predstavlja napreden model za zaznavanje goljufij s kreditnimi karticami, ki izboljšuje natančnost in presega obstoječe metode. [ABSTRACT FROM AUTHOR] |
| Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194608713 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Credit Card Fraud Detection Using Hybrid Proximal Policy Optimization and Artificial Bee Colony Optimization with Mutual Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yuanyuan+Zhang%22">Yuanyuan Zhang</searchLink><relatesTo>1</relatesTo><i> zhangyy4935@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Informatica+%2803505596%29%22">Informatica (03505596)</searchLink>. Mar2026, Vol. 50 Issue 1, p3-23. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Credit+card+fraud%22">Credit card fraud</searchLink><br /><searchLink fieldCode="DE" term="%22Bees+algorithm%22">Bees algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Online+marketplaces%22">Online marketplaces</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: The surge in e-commerce has intensified credit cardfraud, resulting in massive global losses and creating an urgent need for stronger detection systems. This research presents an advanced model for detecting credit cardfraud to address challenges often overlooked, such as class imbalance and sensitivity to initial parameter settings. Our model leverages an artificial neural network (ANN) to extract feature vectors necessary for accurate fraud detection. We utilize proximal policy optimization (PPO) to address class imbalance during training of the ANN. PPO improves the treatment of minority classes by assigning higher rewards for correct predictions and more substantial penalties for errors. This approach leads to more balanced learning. Additionally, our model incorporates a mutual learning-based artificial bee colony (ML-ABC) algorithm for efficiently pre-training the parameters of the ANN. Experiments on the Universite Libre de Bruxelles credit card dataset show that the proposed approach achieves 90.197% accuracy and an F-measure of 91.287%. It outperforms the best existing method by about 3%. These results highlight the robustness of the model and its potential for real-world e-commerce fraud detection. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Slovenian) Group: Ab Data: Raziskava predstavlja napreden model za zaznavanje goljufij s kreditnimi karticami, ki izboljšuje natančnost in presega obstoječe metode. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194608713 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.31449/inf.v50i1.8099 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 3 Subjects: – SubjectFull: Credit card fraud Type: general – SubjectFull: Bees algorithm Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Online marketplaces Type: general – SubjectFull: Artificial neural networks Type: general Titles: – TitleFull: Credit Card Fraud Detection Using Hybrid Proximal Policy Optimization and Artificial Bee Colony Optimization with Mutual Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yuanyuan Zhang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 03505596 Numbering: – Type: volume Value: 50 – Type: issue Value: 1 Titles: – TitleFull: Informatica (03505596) Type: main |
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