The Credit Card Anti-fraud Detection Model in the Context of Dynamic Integration Selection Algorithm

Jiajian Zheng; Le Yang; Duan Xin; Miao Tian

doi:10.54097/a5jafgdv

Authors

Jiajian Zheng
Le Yang
Duan Xin
Miao Tian

DOI:

https://doi.org/10.54097/a5jafgdv

Keywords:

Selection Algorithm, Artificial Intelligence, Anti-fraud, Credit Card Anomaly

Abstract

Because of its close relationship with information technology, every change in the field of information technology will have an important impact on the means, time and space distribution of the final fraud of the credit card network. The rapid development and popularization of artificial intelligence (AI) software based on Deepfake technology has greatly promoted the intelligent transformation of credit card network fraud. Through the use of advanced hardware and software equipment, criminals have iterated on their own fraud tools, from the use of traditional phone calls and text messages to the use of the latest AI software. Therefore, for the problem of credit card fraud detection with missing data set labels and highly unbalanced category distribution, A credit card approval anomaly detection model DES-HBOS (Dynamic Ensemble Selection based on Histogram-based Outlier Score) is proposed. Firstly, the unsupervised anomaly detection algorithm is used to construct the false label of the training set customer. Then, the customer capability area to be measured is determined, and the classifier performance is evaluated according to Pearson correlation coefficient. Finally, a set of optimal classifiers is selected to integrate the test customers. Experiments on real credit card customer data sets show that DES-HBOS has a higher Recall and can identify more fraudulent customers than other 6 classical anomaly detection models. Comparative experiments were carried out on 4 unbalanced data sets, and the experimental results showed that DES-HBOS had stronger anomaly detection ability than HBOS.

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References

URCOVSKY J, CRANITZER M, ZIECLER K, et al. Sequence Classification for Credit-Card Fraud DetectionlJl. Expert Systems with Applications, 2018, 100: 234 245.

FIORE U, DE SANTIS A, PERLA F, et al. Using Cenerative Adversarial Netorks for lmproving Classification Effectiveness inCredit Card Fraud Detection J . Information Sciences, 2019, 479: 448-455.

SMITI A. A Critical Overview 0f Outlier Detection Methods Jl. Computer Science Review, 2020, 38: 100306.Van Capelleveen C, Poel M, Mueller R M, et al. Outlier Detection In Healthcare Fraud: A Case Study in the Medicaid DentalDomainl.Jl. International Journal of Accounting Information Systems, 2016, 21: 18-31.

PORWAL U, MUKUND S. Credit Card Fraud Detection in e-Commerce: An Outlier Detection ApproachlJ . arXiv preprintar Xiv: 1811.02196.2018.

Chang Che, Bo Liu, Shulin Li, Jiaxin Huang, and Hao Hu. Deep learning for precise robot position prediction in logistics. Journal of Theory and Practice of Engineering Science, 3 (10): 36–41, 2023.DOI: 10.1021/acs.jctc.3c00031.

Hao Hu, Shulin Li, Jiaxin Huang, Bo Liu, and Change Che. Casting product image data for quality inspection with xception and data augmentation. Journal of Theory and Practice of Engineering Science, 3(10):42–46, 2023. https://doi. org/10. 53469/jtpes.2023.03(10).06.

Chang Che, Qunwei Lin, Xinyu Zhao, Jiaxin Huang, and Liqiang Yu. 2023. Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation. In Proceedings of the 2023 6th International Conference on Big Data Technologies (ICBDT '23). Association for Computing Machinery, New York, NY, USA, 414–418. https://doi. org/10. 1145/3627377.3627442.

Lin, Q., Che, C., Hu, H., Zhao, X., & Li, S. (2023). A Comprehensive Study on Early Alzheimer’s Disease Detection through Advanced Machine Learning Techniques on MRI Data. Academic Journal of Science and Technology, 8(1), 281–285.DOI: 10.1111/jgs.18617.

Che, C., Hu, H., Zhao, X., Li, S., & Lin, Q. (2023). Advancing Cancer Document Classification with R andom Forest. Academic Journal of Science and Technology, 8(1), 278–280. https:// doi.org/10.54097/ajst.v8i1.14333.

S. Tianbo, H. Weijun, C. Jiangfeng, L. Weijia, Y. Quan and H. Kun, "Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition," 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE), Guangzhou, China, 2023, pp. 834-837, doi: 10.1109/ICCECE58074.2023.10135464.

Y. Wang, K. Yang, W. Wan, Y. Zhang and Q. Liu, "Energy-Efficient Data and Energy Integrated Management Strategy for IoT Devices Based on RF Energy Harvesting," in IEEE Internet of Things Journal, vol. 8, no. 17, pp. 13640-13651, 1 Sept.1, 2021, doi: 10.1109/JIOT.2021.3068040.

Wang, Y, Yang, K, Wan, W, Mei, H. Adaptive energy saving algorithms for Internet of Things devices integrating end and edge strategies. Trans Emerging Tel Tech. 2021; 32: e4122. DOI: https://doi.org/10.1002/ett.4122.

Xu, J., Pan, L., Zeng, Q., Sun, W., & Wan, W. Based on TPUGRAPHS Predicting Model Runtimes Using Graph Neural Networks. https://api.semanticscholar.org/Corpus.

Yao, J., Zou, Y., Du, S., Wu, H., & Yuan, B. Progress in the Application of Artificial Intelligence in Ultrasound Diagnosis of Breast Cancer. DOI:https://api.semanticscholar.org/Corpus.

Zhou Y, Chen S, Wu Y, Li L, Lou Q, Chen Y, Xu S. Multi-clinical index classifier combined with AI algorithm model to predict the prognosis of gallbladder cancer. Front Oncol. 2023 May 10;13:1171837. DOI: 10.3389/fonc.2023.1171837. PMID: 37234992; PMCID: PMC10206143.

Li L, Xu C, Wu W, et al. Zero-resource knowledge-grounded dialogue generation[J]. Advances in Neural Information Processing Systems, 2020, 33: 8475-8485. DOI: https:// doi.org/ 10.48550/arXiv.2008.12918.