Leveraging Mixture of Experts and Deep Learning-Based Data Rebalancing to Improve Credit Fraud Detection
Credit card fraud detection is a critical challenge in the financial sector due to the rapidly evolving tactics of fraudsters and the significant class imbalance betweenegitimate and fraudulent transactions. Traditional models, while effective to some extent, often suffer from high false positive ra...
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MDPI AG
2024-11-01
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| author | Zeyuan Yang Yixuan Wang Haokun Shi Qiang Qiu |
| author_facet | Zeyuan Yang Yixuan Wang Haokun Shi Qiang Qiu |
| author_sort | Zeyuan Yang |
| collection | DOAJ |
| description | Credit card fraud detection is a critical challenge in the financial sector due to the rapidly evolving tactics of fraudsters and the significant class imbalance betweenegitimate and fraudulent transactions. Traditional models, while effective to some extent, often suffer from high false positive rates and fail to generalize well to emerging fraud patterns. In this paper, we propose a novel approach that integrates a Mixture of Experts (MoE) model with a Deep Neural Network-based Synthetic Minority Over-sampling Technique (DNN-SMOTE) to enhance fraud detection performance. The MoE modeleverages multiple specialized expert networks, each trained to detect specific types of fraud, while the DNN-SMOTE generates high-quality synthetic samples to address the class imbalance. Our experimental results on a publicly available dataset demonstrate that the proposed method achieves a classification accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.93</mn><mo>%</mo></mrow></semantics></math></inline-formula>, a true positive rate of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>84.69</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and a true negative rate of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.95</mn><mo>%</mo></mrow></semantics></math></inline-formula>. The Matthews Correlation Coefficient (MCC) of 0.7883 further highlights the model’s balanced performance in detecting fraudulent transactions. These results underscore the effectiveness of combining MoE with DNN-SMOTE, offering a robust solution for real-world credit card fraud detection scenarios. |
| format | Article |
| id | doaj-art-9705da76d0c54a43a025e508915469e3 |
| institution | Kabale University |
| issn | 2504-2289 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Big Data and Cognitive Computing |
| spelling | doaj-art-9705da76d0c54a43a025e508915469e32024-11-26T17:51:11ZengMDPI AGBig Data and Cognitive Computing2504-22892024-11-0181115110.3390/bdcc8110151Leveraging Mixture of Experts and Deep Learning-Based Data Rebalancing to Improve Credit Fraud DetectionZeyuan Yang0Yixuan Wang1Haokun Shi2Qiang Qiu3College of Economics and Management, Nanjing Forestry University, Nanjing 210037, ChinaDepartment of Computer Science, New York University, New York, NY 10012, USASchool of Computer Science, University of Sheffield, Sheffield S1 4DP, UKCollege of Economics and Management, Nanjing Forestry University, Nanjing 210037, ChinaCredit card fraud detection is a critical challenge in the financial sector due to the rapidly evolving tactics of fraudsters and the significant class imbalance betweenegitimate and fraudulent transactions. Traditional models, while effective to some extent, often suffer from high false positive rates and fail to generalize well to emerging fraud patterns. In this paper, we propose a novel approach that integrates a Mixture of Experts (MoE) model with a Deep Neural Network-based Synthetic Minority Over-sampling Technique (DNN-SMOTE) to enhance fraud detection performance. The MoE modeleverages multiple specialized expert networks, each trained to detect specific types of fraud, while the DNN-SMOTE generates high-quality synthetic samples to address the class imbalance. Our experimental results on a publicly available dataset demonstrate that the proposed method achieves a classification accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.93</mn><mo>%</mo></mrow></semantics></math></inline-formula>, a true positive rate of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>84.69</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and a true negative rate of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.95</mn><mo>%</mo></mrow></semantics></math></inline-formula>. The Matthews Correlation Coefficient (MCC) of 0.7883 further highlights the model’s balanced performance in detecting fraudulent transactions. These results underscore the effectiveness of combining MoE with DNN-SMOTE, offering a robust solution for real-world credit card fraud detection scenarios.https://www.mdpi.com/2504-2289/8/11/151credit card fraud detectionfinancial securitymixture of expertsensembleearningsynthetic data generation |
| spellingShingle | Zeyuan Yang Yixuan Wang Haokun Shi Qiang Qiu Leveraging Mixture of Experts and Deep Learning-Based Data Rebalancing to Improve Credit Fraud Detection Big Data and Cognitive Computing credit card fraud detection financial security mixture of experts ensembleearning synthetic data generation |
| title | Leveraging Mixture of Experts and Deep Learning-Based Data Rebalancing to Improve Credit Fraud Detection |
| title_full | Leveraging Mixture of Experts and Deep Learning-Based Data Rebalancing to Improve Credit Fraud Detection |
| title_fullStr | Leveraging Mixture of Experts and Deep Learning-Based Data Rebalancing to Improve Credit Fraud Detection |
| title_full_unstemmed | Leveraging Mixture of Experts and Deep Learning-Based Data Rebalancing to Improve Credit Fraud Detection |
| title_short | Leveraging Mixture of Experts and Deep Learning-Based Data Rebalancing to Improve Credit Fraud Detection |
| title_sort | leveraging mixture of experts and deep learning based data rebalancing to improve credit fraud detection |
| topic | credit card fraud detection financial security mixture of experts ensembleearning synthetic data generation |
| url | https://www.mdpi.com/2504-2289/8/11/151 |
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