An intelligent approach for anomaly detection in credit card data using bat optimization algorithm
As technology advances, many people are utilising credit cards to purchase their necessities, and the number of credit card scams is increasing tremendously. However, illegal card transactions have been on the rise, costing financial institutions millions of dollars each year. The development of ef...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Asociación Española para la Inteligencia Artificial
2023-09-01
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| Series: | Inteligencia Artificial |
| Subjects: | |
| Online Access: | https://journal.iberamia.org/index.php/intartif/article/view/940 |
| _version_ | 1797672209750163456 |
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| author | Haseena Sikkandar Saroja S Suseandhiran N Manikandan B |
| author_facet | Haseena Sikkandar Saroja S Suseandhiran N Manikandan B |
| author_sort | Haseena Sikkandar |
| collection | DOAJ |
| description |
As technology advances, many people are utilising credit cards to purchase their necessities, and the number of credit card scams is increasing tremendously. However, illegal card transactions have been on the rise, costing financial institutions millions of dollars each year. The development of efficient fraud detection techniques is critical in reducing these deficits, but it is difficult due to the extremely unbalanced nature of most credit card datasets. As compared to conventional fraud detection methods, the proposed method will help in automatically detecting the fraud, identifying hidden correlations in data and reduced time for verification process. This is achieved by selecting relevant and unique features by using Bat Optimization Algorithm (BOA). Next, balancing is performed in the highly imbalanced credit card fraud dataset using Synthetic Minority over-sampling technique (SMOTE). Then finally the CNN model for anomaly detection in credit card data is built using full center loss function to improve fraud detection performance and stability. The proposed model is tested with Kaggle dataset and yields around 99% accuracy.
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| first_indexed | 2024-03-11T21:26:43Z |
| format | Article |
| id | doaj.art-439288d6e5e44218b4ce2f080941b9d2 |
| institution | Directory Open Access Journal |
| issn | 1137-3601 1988-3064 |
| language | English |
| last_indexed | 2024-03-11T21:26:43Z |
| publishDate | 2023-09-01 |
| publisher | Asociación Española para la Inteligencia Artificial |
| record_format | Article |
| series | Inteligencia Artificial |
| spelling | doaj.art-439288d6e5e44218b4ce2f080941b9d22023-09-27T22:02:59ZengAsociación Española para la Inteligencia ArtificialInteligencia Artificial1137-36011988-30642023-09-01267210.4114/intartif.vol26iss72pp202-222An intelligent approach for anomaly detection in credit card data using bat optimization algorithmHaseena Sikkandar0Saroja S1Suseandhiran N2Manikandan B3Mepco schlenk engineering college, IndiaNational Institute of Technology, Trichy, IndiaMepco schlenk engineering college, IndiaMepco schlenk engineering college, India As technology advances, many people are utilising credit cards to purchase their necessities, and the number of credit card scams is increasing tremendously. However, illegal card transactions have been on the rise, costing financial institutions millions of dollars each year. The development of efficient fraud detection techniques is critical in reducing these deficits, but it is difficult due to the extremely unbalanced nature of most credit card datasets. As compared to conventional fraud detection methods, the proposed method will help in automatically detecting the fraud, identifying hidden correlations in data and reduced time for verification process. This is achieved by selecting relevant and unique features by using Bat Optimization Algorithm (BOA). Next, balancing is performed in the highly imbalanced credit card fraud dataset using Synthetic Minority over-sampling technique (SMOTE). Then finally the CNN model for anomaly detection in credit card data is built using full center loss function to improve fraud detection performance and stability. The proposed model is tested with Kaggle dataset and yields around 99% accuracy. https://journal.iberamia.org/index.php/intartif/article/view/940Credit card anomaly detection, imbalanced data, feature selection, optimization, neural network, loss function |
| spellingShingle | Haseena Sikkandar Saroja S Suseandhiran N Manikandan B An intelligent approach for anomaly detection in credit card data using bat optimization algorithm Inteligencia Artificial Credit card anomaly detection, imbalanced data, feature selection, optimization, neural network, loss function |
| title | An intelligent approach for anomaly detection in credit card data using bat optimization algorithm |
| title_full | An intelligent approach for anomaly detection in credit card data using bat optimization algorithm |
| title_fullStr | An intelligent approach for anomaly detection in credit card data using bat optimization algorithm |
| title_full_unstemmed | An intelligent approach for anomaly detection in credit card data using bat optimization algorithm |
| title_short | An intelligent approach for anomaly detection in credit card data using bat optimization algorithm |
| title_sort | intelligent approach for anomaly detection in credit card data using bat optimization algorithm |
| topic | Credit card anomaly detection, imbalanced data, feature selection, optimization, neural network, loss function |
| url | https://journal.iberamia.org/index.php/intartif/article/view/940 |
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