An analytical assessment of credit card fraud detection techniques: Supervised, Unsupervised, and Reinforcement Learning
Objective. Bank card fraud is an increasingly serious problem for individuals, businesses and financial institutions. There is a need for effective fraud detection measures to protect consumers and businesses from financial losses. Method. information-theoretical analysis of methods for detecting fr...
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| Format: | Article |
| Language: | Russian |
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Dagestan State Technical University
2025-01-01
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| Series: | Вестник Дагестанского государственного технического университета: Технические науки |
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| Online Access: | https://vestnik.dgtu.ru/jour/article/view/1614 |
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| _version_ | 1849250149005524992 |
|---|---|
| author | Abdourahman Djamal Djama |
| author_facet | Abdourahman Djamal Djama |
| author_sort | Abdourahman Djamal Djama |
| collection | DOAJ |
| description | Objective. Bank card fraud is an increasingly serious problem for individuals, businesses and financial institutions. There is a need for effective fraud detection measures to protect consumers and businesses from financial losses. Method. information-theoretical analysis of methods for detecting fraud with bank cards, machine learning algorithms in improving the accuracy of fraud detection. Result. An analytical evaluation of fraud detection methods is provided, covering different learning approaches: supervised, unsupervised and reinforcement learning. Conclusion. The choice of a fraud detection method should be based on an understanding of the available data, the specific requirements of the application domain and the trade-offs between methods in terms of performance, adaptability and computational complexity. |
| format | Article |
| id | doaj-art-49e3ca410cc54ee48cd4405c9bd62dc3 |
| institution | Kabale University |
| issn | 2073-6185 2542-095X |
| language | Russian |
| publishDate | 2025-01-01 |
| publisher | Dagestan State Technical University |
| record_format | Article |
| series | Вестник Дагестанского государственного технического университета: Технические науки |
| spelling | doaj-art-49e3ca410cc54ee48cd4405c9bd62dc32025-08-20T03:57:21ZrusDagestan State Technical UniversityВестник Дагестанского государственного технического университета: Технические науки2073-61852542-095X2025-01-01514233210.21822/2073-6185-2024-51-4-23-32927An analytical assessment of credit card fraud detection techniques: Supervised, Unsupervised, and Reinforcement LearningAbdourahman Djamal Djama0Financial University under the Government of the Russian FederationObjective. Bank card fraud is an increasingly serious problem for individuals, businesses and financial institutions. There is a need for effective fraud detection measures to protect consumers and businesses from financial losses. Method. information-theoretical analysis of methods for detecting fraud with bank cards, machine learning algorithms in improving the accuracy of fraud detection. Result. An analytical evaluation of fraud detection methods is provided, covering different learning approaches: supervised, unsupervised and reinforcement learning. Conclusion. The choice of a fraud detection method should be based on an understanding of the available data, the specific requirements of the application domain and the trade-offs between methods in terms of performance, adaptability and computational complexity.https://vestnik.dgtu.ru/jour/article/view/1614fraudbank cardsmachine learningsupervised learningunsupervised learningreinforcement learningimbalanced dataset |
| spellingShingle | Abdourahman Djamal Djama An analytical assessment of credit card fraud detection techniques: Supervised, Unsupervised, and Reinforcement Learning Вестник Дагестанского государственного технического университета: Технические науки fraud bank cards machine learning supervised learning unsupervised learning reinforcement learning imbalanced dataset |
| title | An analytical assessment of credit card fraud detection techniques: Supervised, Unsupervised, and Reinforcement Learning |
| title_full | An analytical assessment of credit card fraud detection techniques: Supervised, Unsupervised, and Reinforcement Learning |
| title_fullStr | An analytical assessment of credit card fraud detection techniques: Supervised, Unsupervised, and Reinforcement Learning |
| title_full_unstemmed | An analytical assessment of credit card fraud detection techniques: Supervised, Unsupervised, and Reinforcement Learning |
| title_short | An analytical assessment of credit card fraud detection techniques: Supervised, Unsupervised, and Reinforcement Learning |
| title_sort | analytical assessment of credit card fraud detection techniques supervised unsupervised and reinforcement learning |
| topic | fraud bank cards machine learning supervised learning unsupervised learning reinforcement learning imbalanced dataset |
| url | https://vestnik.dgtu.ru/jour/article/view/1614 |
| work_keys_str_mv | AT abdourahmandjamaldjama ananalyticalassessmentofcreditcardfrauddetectiontechniquessupervisedunsupervisedandreinforcementlearning AT abdourahmandjamaldjama analyticalassessmentofcreditcardfrauddetectiontechniquessupervisedunsupervisedandreinforcementlearning |