Application of Deep Learning in Financial Credit Card Fraud Detection

Qiaozhi Bao; Kuo Wei; Jiahao Xu; Wei Jiang

doi:10.5281/zenodo.10960092

Authors

Qiaozhi Bao North Carolina State University
Kuo Wei Independent Research
Jiahao Xu University of Southern California
Wei Jiang Xidian University

DOI:

https://doi.org/10.5281/zenodo.10960092

References:

38

Keywords:

Credit Card Fraud Detection, BERT Model, Imbalanced Dataset, Deep Learning, Data Preprocessing

Abstract

Credit cards play an important role in our daily life, and the emergence of Internet finance makes credit card payment face more fraud risks. Therefore, it is of great significance to build an efficient credit card fraud detection model and continuously improve the fraud detection accuracy for improving the market system, promoting the healthy development of economy, maintaining the stability of national economy and ensuring financial security.This paper proposes a BERT model for credit card fraud detection to address the challenges posed by imbalanced and high-dimensional datasets. Leveraging BERT's pre-training to capture semantic similarity, the model enhances fraud detection accuracy. Through extensive data preprocessing and model training, the proposed approach achieves a remarkable 99.95% accuracy in detecting fraudulent transactions. The study underscores the importance of leveraging advanced deep learning techniques like BERT to combat evolving fraud tactics in the internet finance industry.

Author Biographies

Qiaozhi Bao, North Carolina State University

Statistics, North Carolina State University, NC, USA

Kuo Wei, Independent Research

Computer Science, Individual Contributor, Shenzhen, China

Jiahao Xu, University of Southern California

Master of Science in Financial Engineering, University of Southern California, CA, USA.

Wei Jiang, Xidian University

Computer Science, Xidian University, Xian, China.

References

Xu, Z., Gong, Y., Zhou, Y., Bao, Q., & Qian, W. (2024). Enhancing Kubernetes Automated Scheduling with Deep Learning and Reinforcement Techniques for Large-Scale Cloud Computing Optimization. arXiv preprint arXiv:2403.07905.

Gong, Y., Huang, J., Liu, B., Xu, J., Wu, B., & Zhang, Y. (2024). Dynamic Resource Allocation for Virtual Machine Migration Optimization using Machine Learning. arXiv preprint arXiv:2403.13619.

Che, Chang, et al. "Intelligent Robotic Control System Based on Computer Vision Technology." arXiv preprint arXiv:2404.01116 (2024).

Xu, X., Xu, Z., Ling, Z., Jin, Z., & Du, S. (2024). Comprehensive Implementation of TextCNN for Enhanced Collaboration between Natural Language Processing and System Recommendation. arXiv preprint arXiv:2403.09718.

Liu, Bo, et al. "Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms." arXiv preprint arXiv:2312.12872 (2023).

Wang, Y., Bao, Q., Wang, J., Su, G., & Xu, X. (2024). Cloud Computing for Large-Scale Resource Computation and Storage in Machine Learning. Journal of Theory and Practice of Engineering Science, 4(03).

Xu, J., Jiang, Y., Yuan, B., Li, S., & Song, T. (2023, November). Automated Scoring of Clinical Patient Notes using Advanced NLP and Pseudo Labeling. In 2023 5th International Conference on Artificial Intelligence and Computer Applications (ICAICA) (pp. 384-388). IEEE.

Xu, Z., Yuan, J., Yu, L., Wang, G., & Zhu, M. (2024). Machine Learning-Based Traffic Flow Prediction and Intelligent Traffic Management. International Journal of Computer Science and Information Technology, 2(1), 18-27.

Wu, X.Z. and Zhou, Z.H., 2017, July. A unified view of multi-label performance measures. In international conference on machine learning (pp. 3780-3788). PMLR.

Zhu, Mengran, et al. "THE APPLICATION OF DEEP LEARNING IN FINANCIAL PAYMENT SECURITY AND THE CHALLENGE OF GENERATING ADVERSARIAL NETWORK MODELS." The 8th International scientific and practical conference “Priority areas of research in the scientific activity of teachers”(February 27–March 01, 2024) Zagreb, Croatia. International Science Group. 2024. 298 p.. 2024.

Che, Chang, et al. "Deep learning for precise robot position prediction in logistics." Journal of Theory and Practice of Engineering Science 3.10 (2023): 36-41.

Hu, Hao, et al. "Casting product image data for quality inspection with xception and data augmentation." Journal of Theory and Practice of Engineering Science 3.10 (2023): 42-46.

Yu, Liqiang, et al. "Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method." arXiv preprint arXiv:2401.06782 (2024).

Huang, Jiaxin, et al. "Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling." arXiv preprint arXiv:2401.05433 (2024

Lin, Qunwei, et al. "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 (2023): 281-285.

Che, Chang, et al. "Advancing Cancer Document Classification with R andom Forest." Academic Journal of Science and Technology 8.1 (2023): 278-280.

Che, Chang, et al. "Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation." Proceedings of the 2023 6th International Conference on Big Data Technologies. 2023.

Li, Chen, et al. "Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques." arXiv preprint arXiv:2403.05801 (2024).

K. Xu, X. Wang, Z. Hu and Z. Zhang, "3D Face Recognition Based on Twin Neural Network Combining Deep Map and Texture," 2019 IEEE 19th International Conference on Communication Technology (ICCT), Xi'an, China, 2019, pp. 1665-1668, doi: 10.1109/ICCT46805.2019.8947113.

Shi, Peng, Yulin Cui, Kangming Xu, Mingmei Zhang, and Lianhong Ding. 2019. "Data Consistency Theory and Case Study for Scientific Big Data" Information 10, no. 4: 137. https://doi.org/10.3390/info10040137.

Zhenghua Hu, Xianmei Wang, Kangming Xu, and Pu Dong. 2020. Real-time Target Tracking Based on PCANet-CSK Algorithm. In Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence (CSAI '19). Association for Computing Machinery, New York, NY, USA, 343–346. https://doi.org/10.1145/3374587.3374607.

Medication Recommendation System Based on Natural Language Processing for Patient Emotion Analysis. (2024). Academic Journal of Science and Technology, 10(1), 62-68. https://doi.org/10.54097/v160aa61

Li, X., Zheng, H., Chen, J., Zong, Y., & Yu, L. (2024). User Interaction Interface Design and Innovation Based on Artificial Intelligence Technology. Journal of Theory and Practice of Engineering Science, 4(03), 1-8.

Song, T., Li, X., Wang, B., & Han, L. (2024). Research on Intelligent Application Design Based on Artificial Intelligence and Adaptive Interface.

Wu, Jiang, et al. "Case Study of Next-Generation Artificial Intelligence in Medical Image Diagnosis Based on Cloud Computing." Journal of Theory and Practice of Engineering Science 4.02 (2024): 66-73.

Zhang, Y., Liu, B., Gong, Y., Huang, J., Xu, J., & Wan, W. (2024). Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management. arXiv preprint arXiv:2402.17216.Wang, Yong, et al. "Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS data." arXiv preprint arXiv:2402.15796 (2024).

Song, B., Xu, Y., & Wu, Y. (2024). ViTCN: Vision Transformer Contrastive Network For Reasoning. arXiv preprint arXiv:2403.09962.

Che, Chang, et al. "Integrating Generative AI into Financial Market Prediction for Improved Decision Making." arXiv preprint arXiv:2404.03523 (2024).

Yu, Liqiang, et al. "Stochastic Analysis of Touch-Tone Frequency Recognition in Two-Way Radio Systems for Dialed Telephone Number Identification." arXiv preprint arXiv:2403.15418 (2024).

Wang, Junliang, et al. "Research on finance Credit Risk Quantification Model Based on Machine Learning Algorithm." Academic Journal of Science and Technology 10.1 (2024): 290-298.

Wan, Weixiang, et al. "Progress in artificial intelligence applications based on the combination of self-driven sensors and deep learning." arXiv preprint arXiv:2402.09442 (2024).

W. Sun, W. Wan, L. Pan, J. Xu, and Q. Zeng, “The Integration of Large-Scale Language Models Into Intelligent Adjudication: Justification Rules and Implementation Pathways”, Journal of Industrial Engineering & Applied Science, vol. 2, no. 1, pp. 13–20, Feb. 2024.

Xu, X., Niu, H., Ji, H., Li, H., & Wang, J. (2024). AI Empowered of Advancements in Microbial and Tumor Cell Image Labeling for Enhanced Medical Insights. Journal of Theory and Practice of Engineering Science, 4(03), 21-27.

Zeng, Q., Sun, W., Xu, J., Wan, W., & Pan, L. (2024). Machine Learning-Based Medical Imaging Detection and Diagnostic Assistance. International Journal of Computer Science and Information Technology, 2(1), 36-44.

Huang, Zengyi, et al. "Research on Generative Artificial Intelligence for Virtual Financial Robo-Advisor." Academic Journal of Science and Technology 10.1 (2024): 74-80.

Huang, Zengyi, et al. "Application of Machine Learning-Based K-Means Clustering for Financial Fraud Detection." Academic Journal of Science and Technology 10.1 (2024): 33-39.

Li, H., Wang, X., Feng, Y., Qi, Y., & Tian, J. (2024). Integration Methods and Advantages of Machine Learning with Cloud Data Warehouses. International Journal of Computer Science and Information Technology, 2(1), 348-358. https://doi.org/10.62051/ijcsit.v2n1.36

Biology-based AI Predicts T-cell Receptor Antigen Binding Specificity. (2024). Academic Journal of Science and Technology, 10(1), 23-27. https://doi.org/10.54097/wy28c490