Ways to Fight Online Payment Fraud
Keywords:
Online payment fraud, secure paymentsAbstract
Online payment fraud is a pervasive threat in the digital age, fueled by the rapid expansion of e-commerce, mobile payments, and digital wallets. Fraudulent schemes like phishing, identity theft, and chargeback fraud impose significant financial and reputational losses on businesses and consumers. Combating this challenge requires a holistic strategy integrating cutting-edge technologies, robust security protocols, regulatory compliance, and user education. Machine learning and artificial intelligence play a transformative role in fraud detection by analyzing real-time transaction patterns and flagging suspicious activities, allowing businesses to respond proactively. Secure payment protocols, including tokenization and encryption, safeguard sensitive data during transactions, while multi-factor authentication enhances user account protection. Adherence to regulatory standards like PCI DSS establishes a security baseline, fostering a safer payment ecosystem. Equally important is empowering users with knowledge about online security, such as recognizing phishing attempts, creating strong passwords, and using secure connections for transactions. Collaboration among payment processors, financial institutions, and regulatory bodies is essential for effectively sharing threat intelligence and addressing emerging fraud tactics. Additionally, businesses must adopt multi-layered security measures that combine technological defenses with continuous monitoring and adaptive responses to evolving risks. By uniting these efforts, organizations can create a resilient payment environment that minimizes fraud risks, protects consumers, and supports trust in digital financial systems. As the digital economy grows, proactive measures to address vulnerabilities and a collective commitment from all stakeholders are crucial to mitigating the escalating risks of online payment fraud.
Downloads
References
Wei, W., Li, J., Cao, L., Ou, Y., & Chen, J. (2013). Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web, 16, 449-475.
Dal Pozzolo, A., Caelen, O., Le Borgne, Y. A., Waterschoot, S., & Bontempi, G. (2014). Learned lessons in credit card fraud detection from a practitioner perspective. Expert systems with applications, 41(10), 4915-4928.
Xuan, S., Liu, G., Li, Z., Zheng, L., Wang, S., & Jiang, C. (2018, March). Random forest for credit card fraud detection. In 2018 IEEE 15th international conference on networking, sensing and control (ICNSC) (pp. 1-6). IEEE.
Moore, T., Clayton, R., & Anderson, R. (2009). The economics of online crime. Journal of Economic Perspectives, 23(3), 3-20.
Bolton, R. J., & Hand, D. J. (2001). Unsupervised profiling methods for fraud detection. Credit scoring and credit control VII, 235-255.
Quah, J. T., & Sriganesh, M. (2008). Real-time credit card fraud detection using computational intelligence. Expert systems with applications, 35(4), 1721-1732.
Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2017). Credit card fraud detection: a realistic modeling and a novel learning strategy. IEEE transactions on neural networks and learning systems, 29(8), 3784-3797.
Bierstaker, J. L., Brody, R. G., & Pacini, C. (2006). Accountants' perceptions regarding fraud detection and prevention methods. Managerial Auditing Journal, 21(5), 520-535.
Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision support systems, 50(3), 602-613.
Bahnsen, A. C., Aouada, D., Stojanovic, A., & Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 51, 134-142.
Awoyemi, J. O., Adetunmbi, A. O., & Oluwadare, S. A. (2017, October). Credit card fraud detection using machine learning techniques: A comparative analysis. In 2017 international conference on computing networking and informatics (ICCNI) (pp. 1-9). IEEE.
Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P. E., He-Guelton, L., & Caelen, O. (2018). Sequence classification for credit-card fraud detection. Expert systems with applications, 100, 234-245.
Zareapoor, M., & Shamsolmoali, P. (2015). Application of credit card fraud detection: Based on bagging ensemble classifier. Procedia computer science, 48(2015), 679-685.
Sánchez, D., Vila, M. A., Cerda, L., & Serrano, J. M. (2009). Association rules applied to credit card fraud detection. Expert systems with applications, 36(2), 3630-3640.
Council, F. F. I. E. (2005). Authentication in an internet banking environment. Retrieved June, 28, 2006.
Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).
Gade, K. R. (2017). Integrations: ETL vs. ELT: Comparative analysis and best practices. Innovative Computer Sciences Journal, 3(1).
Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
Naresh Dulam, et al. Kubernetes Gains Traction: Orchestrating Data Workloads. Distributed Learning and Broad Applications in Scientific Research, vol. 3, May 2017, pp. 69-93
Naresh Dulam, et al. Apache Arrow: Optimizing Data Interchange in Big Data Systems. Distributed Learning and Broad Applications in Scientific Research, vol. 3, Oct. 2017, pp. 93-114
Naresh Dulam, and Venkataramana Gosukonda. Event-Driven Architectures With Apache Kafka and Kubernetes. Distributed Learning and Broad Applications in Scientific Research, vol. 3, Oct. 2017, pp. 115-36
Naresh Dulam, et al. Snowflake Vs Redshift: Which Cloud Data Warehouse Is Right for You? . Distributed Learning and Broad Applications in Scientific Research, vol. 4, Oct. 2018, pp. 221-40
Naresh Dulam, et al. Apache Iceberg: A New Table Format for Managing Data Lakes . Distributed Learning and Broad Applications in Scientific Research, vol. 4, Sept. 2018
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of research papers submitted to Distributed Learning and Broad Applications in Scientific Research retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agree to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. Scientific Research Canada disclaims any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
If you have any questions or concerns regarding these license terms, please contact us at editor@dlabi.org.
