Enhancing Security in Payment Processing through AI-Based Anomaly Detection
Keywords:
AI-based anomaly detection, Payment security, Fraud prevention, Machine learning algorithms, Cyber threatsAbstract
In the digital age, ensuring the security of payment processing systems is paramount due to the increasing prevalence of online transactions and the associated risk of cyber threats. Traditional security measures often fall short in addressing sophisticated fraud schemes, necessitating the integration of advanced technologies such as Artificial Intelligence (AI). This paper explores the role of AI-based anomaly detection in enhancing payment security. By employing machine learning algorithms and real-time data analysis, AI systems can identify unusual patterns and potential threats that conventional methods may overlook. This proactive approach not only aids in detecting fraudulent activities but also minimizes financial and reputational damage. The paper discusses how AI-based anomaly detection works, the types of anomalies it can detect, and the process of integrating AI solutions with existing payment systems. Additionally, it addresses the challenges and limitations of AI-based approaches, such as high false positive rates, data privacy concerns, and the need for continuous adaptation to evolving threats. By overcoming these challenges, businesses can leverage AI to improve fraud detection, ensure regulatory compliance, and foster customer trust. The paper concludes by highlighting future trends in AI for payment security and the importance of ongoing optimization to maintain robust protection against cybercrime.
References
Awoyemi, J. O., Adetunmbi, A. O., & Oluwadare, S. A. (2017). Credit card fraud detection using machine learning techniques: A comparative analysis. Journal of Computer Networks and Communications, 2017, 1-9.
Bauder, R. A., & Khoshgoftaar, T. M. (2018). The detection of credit card fraud and identifying gender differences in fraudulent transactions using machine learning and statistical techniques. IEEE Transactions on Cybernetics, 49(7), 2500-2513.
Bayyapu, S. (2021). Bridging the gap: Overcoming data, technological, and human roadblocks to AI-driven healthcare transformation. Journal of Management (JOM), 8(1), 7-14.
Ramachandran, K. (2021). Architecting the future: Modular designs for next-generation payment gateways. International Journal of Science and Research (IJSR), 10(6), 1821-1824.
Pookandy, J. (2022). AI-based data cleaning and management in Salesforce CRM for improving data integrity and accuracy to enhance customer insights. International Journal of Advanced Research in Engineering and Technology (IJARET), 13(5), 108-116.
Carcillo, F., Dal Pozzolo, A., Le Borgne, Y. A., Caelen, O., Mazzer, Y., & Bontempi, G. (2019). SCARFF: A scalable framework for streaming credit card fraud detection with Spark. Information Fusion, 41, 182-194.
Kolluru, V., Mungara, S., & Chintakunta, A.N. (2020). Combating misinformation with machine learning: Tools for trustworthy news consumption. Machine Learning and Applications: An International Journal (MLAIJ), 7(3/4), 28–39. https://doi.org/10.5121/mlaij.2020.7403
Kanchetti, D. (2022). Advanced anomaly detection algorithms for securing insurance data ecosystems against emerging cyber threats and fraud. International Journal of Information Technology (IJIT), 3(1), 17–35.
Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.
Bayyapu, S. (2022). Optimizing IT sourcing in healthcare: Balancing control, cost, and innovation. International Journal of Computer Applications, 3(1), 14-20.
Pookandy, J. (2021). Multi-factor authentication and identity management in cloud CRM with best practices for strengthening access controls. International Journal of Information Technology & Management Information System (IJITMIS), 12(1), 85-96.
Ramachandran, K. (2021). Concurrency conquered: Advanced techniques for optimizing system performance in high-throughput environments. European Journal of Advances in Engineering and Technology, 8(12), 69-76.
Chen, J., Li, K., Li, J., & Xu, Y. (2020). A survey on applications of artificial intelligence in fighting against COVID-19. ACM Computing Surveys (CSUR), 53(6), 1-32.
Bayyapu, S. (2023). Impact of the Internet of Medical Things (IoMT) on healthcare cybersecurity. International Journal for Innovative Engineering and Management Research, 12(12), 146-153.
Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2017). Credit card fraud detection and concept-drift adaptation with delayed supervised information. Proceedings of the 2017 IEEE International Joint Conference on Neural Networks (IJCNN), 2537-2544.
Kanchetti, D. (2022). Developing a scalable framework for real-time predictive analytics in insurance using stream processing and cloud computing technologies. International Journal of Information Technology and Management Information Systems (IJITMIS), 13(1), 69–82.
Duman, E., & Elikucuk, S. (2013). Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization. Proceedings of the 2013 IEEE Congress on Evolutionary Computation, 2645-2652.
Kolluru, V., Mungara, S., & Chintakunta, A. N. (2019). Securing the IoT ecosystem: Challenges and innovations in smart device cybersecurity. International Journal on Cryptography and Information Security (IJCIS), 9(1/2), 37–51.
Ramachandran, K. (2022). Transaction autopilot: AI enhanced fault-resilient processing for zero-downtime in payment systems. Journal of Artificial Intelligence, Machine Learning and Data Science, 1(1).
Han, C., Qian, W., & Yang, J. (2021). A survey on intelligent anomaly detection systems for web applications. Journal of Network and Computer Applications, 178, 102947.
Bayyapu, S. (2020). Blockchain healthcare: Redefining data ownership and trust in the medical ecosystem. International Journal of Advanced Research in Engineering and Technology (IJARET), 11(11), 2748-2755.
Pookandy, J. (2020). Exploring the role of AI-orchestrated workflow automation in cloud CRM to enhance operational efficiency through intelligent task management. International Journal of Computer Science and Information Technology Research (IJCSITR), 1(1), 15-31.
Kou, Y., Lu, C. T., Sirwongwattana, S., & Huang, Y. P. (2004). Survey of fraud detection techniques. Proceedings of the 2004 IEEE International Conference on Networking, Sensing and Control, 749-754.
Kolluru, V., Mungara, S., & Chintakunta, A. N. (2018). Adaptive learning systems: Harnessing AI for customized educational experiences. International Journal of Computational Science and Information Technology (IJCSITY), 6(1/2/3), 13–26. https://doi.org/10.5121/ijcsity.2018.6302
Li, J., Li, X., Peng, Y., & Liu, C. (2019). A stacking model using behavioral and transaction features for fraud detection. IEEE Access, 7, 28255-28264.
Pookandy, J. (2020). End-to-end encryption and data integrity verification in cloud CRM as a framework for securing customer communications and transactional data. International Journal of Computer Science and Engineering Research and Development (IJCSERD), 10(1), 19-32.
Bayyapu, S. (2023). How data analysts can help healthcare organizations comply with HIPAA and other data privacy regulations. International Journal For Advanced Research in Science & Technology, 13(12), 669-674.
Malekian, M., Rahimi, R., & Haddadnia, J. (2019). Real-time credit card fraud detection using convolutional neural networks and VGG16. Proceedings of the 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), 1-6.
Ramachandran, K. (2022). Transaction conductor: Architecting advanced orchestrator solutions for scalable payment processing systems. Journal of Artificial Intelligence, Machine Learning and Data Science, 1(1), 1-5.
Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.
Kanchetti, D. (2021). Optimization of insurance claims management processes through the integration of predictive modeling and robotic process automation. International Journal of Computer Applications (IJCA), 2(2), 1–18.
Ramachandran, K. (2022). From application to deactivation: A comprehensive guide to payment card lifecycle management. Journal of Artificial Intelligence, Machine Learning and Data Science, 1(1).
Sahin, Y., & Duman, E. (2011). Detecting credit card fraud by decision trees and support vector machines. Proceedings of the 2011 International MultiConference of Engineers and Computer Scientists (IMECS), 1-6.
Munirathnam, R. (2021). Integrating omics data with data science techniques to accelerate pharmaceutical research and development. International Journal of Pharmaceutical Research, 13(1)
Ramachandran, K. (2021). ISO 8583 deep dive: Powering transaction tech. International Journal of Science and Research (IJSR), 10(1), 1639-1643.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 International Journal of Information Technology and Electrical Engineering (IJITEE) - UGC Care List Group - I
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
