Publication
Abstract
This research delves into the comprehensive methodology of employing machine learning in the domain of fraud detection, outlining the critical steps from data collection to continuous learning. It emphasizes the importance of adhering to data protection regulations during the data collection phase and the significance of preprocessing in preparing the data for analysis. The study explores various machine learning models, including supervised and unsupervised learning techniques, and evaluates their performance using metrics such as accuracy and AUC-ROC. It highlights the necessity of continuous learning to adapt to evolving fraud tactics and the challenges of integrating machine learning models into existing fraud detection systems. Ultimately, this research underscores the transformative potential of machine learning in enhancing the accuracy and efficiency of fraud detection, safeguarding financial transactions, and protecting consumers from fraudulent activities.
Keywords
Fraud Detection , Data Compliance , Machine Learning , Manasgement System .
Metadata
Subjects: Financial Risk Management
Cite This Article
APA Style
Cheng, X. (2024). Machine learning-driven fraud detection: management, compliance, and integration. Academic Journal of Sociology and Management, 2(6), 8-13. https://doi.org/10.5281/zenodo.14064121
Acknowledgments
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.
FUNDING
INSTITUTIONAL REVIEW BOARD STATEMENT
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
INFORMED CONSENT STATEMENT
CONFLICT OF INTEREST
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
AUTHOR CONTRIBUTIONS
References
1.
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.
2.
Liu, B., Yu, L., Che, C., Lin, Q., Hu, H., & Zhao, X. (2024). Integration and performance analysis of artificial intelligence and computer vision based on deep learning algorithms. Applied and Computational Engineering, 64, 36-41.
3.
Huang, Z., Zheng, H., Li, C., & Che, C. (2024). Application of machine learning-based k-means clustering for financial fraud detection. Academic Journal of Science and Technology, 10(1), 33-39.
4.
Che, C., Liu, B., Li, S., Huang, J., & Hu, H. (2023). Deep learning for precise robot position prediction in logistics. Journal of Theory and Practice of Engineering Science, 3(10), 36-41.
5.
Che, C., Lin, Q., Zhao, X., Huang, J., & Yu, L. (2023, September). Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation. In Proceedings of the 2023 6th International Conference on Big Data Technologies (pp. 414-418).
6.
Huang, Z., Che, C., Zheng, H., & Li, C. (2024). Research on Generative Artificial Intelligence for Virtual Financial Robo-Advisor. Academic Journal of Science and Technology, 10(1), 74-80.
7.
Yu, L., Liu, B., Lin, Q., Zhao, X., & Che, C. (2024). Similarity matching for patent documents using ensemble bert-related model and novel text processing method. Journal of Advances in Information Technology, 15(3).
8.
Hu, H., Li, S., Huang, J., Liu, B., & Che, C. (2023). Casting product image data for quality inspection with xception and data augmentation. Journal of Theory and Practice of Engineering Science, 3(10), 42-46.
9.
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.
10.
Huang, J., Zhao, X., Che, C., Lin, Q., & Liu, B. (2023, October). Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling. In 2023 International Conference on Information Network and Computer Communications (INCC) (pp. 8-12). IEEE.
11.
Che, C., Zheng, H., Huang, Z., Jiang, W., & Liu, B. (2024). Intelligent robotic control system based on computer vision technology. Applied and Computational Engineering, 64, 142-147.
12.
Che, C., Huang, Z., Li, C., Zheng, H., & Tian, X. (2024). Integrating generative AI into financial market prediction for improved decision making. Applied and Computational Engineering, 64, 155-161.
13.
Yu, L., Li, C., Gao, L., Liu, B., & Che, C. (2024, March). Stochastic analysis of touch-tone frequency recognition in two-way radio systems for dialed telephone number identification. In 2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE) (pp. 1565-1572). IEEE.
14.
Li, C., Zheng, H., Sun, Y., Wang, C., Yu, L., Chang, C., ... & Liu, B. (2024). Enhancing multi-hop knowledge graph reasoning through reward shaping techniques. arXiv preprint arXiv:2403.05801.
15.
Che, C., Li, C., & Huang, Z. (2024). The Integration of Generative Artificial Intelligence and Computer Vision in Industrial Robotic Arms. International Journal of Computer Science and Information Technology, 2(3), 1-9.
16.
Liu, H., Wang, C., Zhan, X., Zheng, H., & Che, C. (2024). Enhancing 3D Object Detection by Using Neural Network with Self-adaptive Thresholding. arXiv preprint arXiv:2405.07479.
17.
Che, C., & Tian, J. (2024). Game Theory: Concepts, Applications, and Insights from Operations Research. Journal of Computer Technology and Applied Mathematics, 1(4), 53-59.
18.
Che, C., & Tian, J. (2024). Analyzing patterns in Airbnb listing prices and their classification in London through geospatial distribution analysis. Advances in Engineering Innovation, 12, 53-59.
19.
Che, C., & Tian, J. (2024). Maximum flow and minimum cost flow theory to solve the evacuation planning. Advances in Engineering Innovation, 12, 60-64.
20.
Cheng, X., Liu, T., Su, G., Che, C., Zhu, C., Liu, K., ... & Hu, X. (2024). Smart Navigation System for Parking Assignment at Large Events: Incorporating Heterogeneous Driver Characteristics. arXiv preprint arXiv:2410.18983.
PUBLISHER'S NOTE
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
This work is licensed under a Creative
Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
