Citation :

Braja Gopal Mahapatra, 2024. "AI and Machine Learning in Fraud Detection: Strengthening Security in the Financial Payment Domain", ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 4: 125-139.

Abstract :

Financial fraud analysis has emerged as a top priority for the financial industry due to the growing intricacy and number of transactions. Even though traditional approaches are quite useful in earlier settings, they promise little when detecting complex and evolutionary fraudulent patterns. AI and ML are buzzwords transforming the fraud detection domain by providing better features like real-time anomaly detection and further predictive analysis. This paper focuses on how artificial intelligence and machine learning can be incorporated into fraud detection in the financial payment system. We discuss the limitations of traditional approaches and demonstrate how the application of AI/ML has proven to scale, is accurate, and has learned from fraud strategies. This paper reviews case studies, current bodies of literature, and datasets to explore the methods to enhance financial resilience. However, there are scrutinized findings on the consequences of supervised and unsupervised learning, the impact of deep learning algorithms, and a union of the two. In addition, we talk about ethics, the issue of data protection, and legislation. In conclusion, the paper underlines the need to integrate the efforts aimed at AI advancement with sound policies.

References :

[1] Potla, R. T. (2023). AI in Fraud Detection: Leveraging Real-Time Machine Learning for Financial Security. Journal of Artificial Intelligence Research and Applications, 3(2), 534-549.

[2] Bello, O. A., Folorunso, A., Onwuchekwa, J., & Ejiofor, O. E. (2023). A Comprehensive Framework for Strengthening USA Financial Cybersecurity: Integrating Machine Learning and AI in Fraud Detection Systems. European Journal of Computer Science and Information Technology, 11(6), 62-83.

[3] Javaid, H. A. (2024). How Artificial Intelligence is Revolutionizing Fraud Detection in Financial Services. Innovative Engineering Sciences Journal, 4(1).

[4] Sharma, R., Mehta, K., & Sharma, P. (2024). Role of Artificial Intelligence and Machine Learning in Fraud Detection and Prevention. In Risks and Challenges of AI-Driven Finance: Bias, Ethics, and Security (pp. 90-120). IGI Global.

[5] Ryman-Tubb, N. F., Krause, P., & Garn, W. (2018). How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark. Engineering Applications of Artificial Intelligence, 76, 130-157.

[6] Yalamati, S. (2023). Identify fraud detection in corporate tax using Artificial Intelligence advancements. International Journal of Machine Learning for Sustainable Development, 5(2), 1-15.

[7] El Haddad, G., Aimeur, E., & Hage, H. (2018, August). Understanding trust, privacy and financial fears in online payment. In 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (pp. 28-36). IEEE.

[8] Schuh, S., & Stavins, J. (2016). HOW DO SPEED AND SECURITY INFLUENCE CONSUMERS’PAYMENT BEHAVIOR?. Contemporary Economic Policy, 34(4), 595-613.

[9] García, S., Luengo, J., & Herrera, F. (2015). Data preprocessing in data mining (Vol. 72, pp. 59-139). Cham, Switzerland: Springer International Publishing.

[10] Chen, B. (2022). Data collection and preprocessing. In Wineinformatics: A New Data Science Application (pp. 5-16). Singapore: Springer Nature Singapore.

[11] Pencina, M. J., D’Agostino, R. B., & Massaro, J. M. (2013). Understanding increments in model performance metrics. Lifetime data analysis, 19, 202-218.

[12] Taofeek, A. (2024). Ethical Considerations and Bias Mitigation in AI-Powered Movie Recommendations. Available at SSRN.

[13] Moolchandani, S. (2024). Advancing Credit Risk Management: Embracing Probabilistic Graphical Models in Banking. International Journal of Science and Research (IJSR), 13(6), 74-80.

[14] Ardabili, S., Mosavi, A., & Várkonyi-Kóczy, A. R. (2019, September). Advances in machine learning modeling reviewing hybrid and ensemble methods. In International conference on global research and education (pp. 215-227). Cham: Springer International Publishing.

[15] Cunningham, P., Cord, M., & Delany, S. J. (2008). Supervised learning. In Machine learning techniques for multimedia: case studies on organization and retrieval (pp. 21-49). Berlin, Heidelberg: Springer Berlin Heidelberg.

[16] Liu, H., Xu, Y., & Chen, C. (2019). Improved pollution forecasting hybrid algorithms based on the ensemble method. Applied Mathematical Modelling, 73, 473-486.

[17] Salloum, S. A., Alshurideh, M., Elnagar, A., & Shaalan, K. (2020, March). Machine learning and deep learning techniques for cybersecurity: a review. In The International Conference on Artificial Intelligence and Computer Vision (pp. 50-57). Cham: Springer International Publishing.

[18] Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., ... & Nahavandi, S. (2021). A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information fusion, 76, 243-297.

[19] Parasa, N. A., Namgiri, J. V., Mohanty, S. N., & Dash, J. K. (2021). Introduction to unsupervised learning in bioinformatics. Data Analytics in Bioinformatics: A Machine Learning Perspective, 35-49.

[20] Ongie, G., Jalal, A., Metzler, C. A., Baraniuk, R. G., Dimakis, A. G., & Willett, R. (2020). Deep learning techniques for inverse problems in imaging. IEEE Journal on Selected Areas in Information Theory, 1(1), 39-56.

Keywords :

Artificial Intelligence, Machine Learning, Fraud Detection, Payment Systems, Anomaly Detection, Regulatory Compliance.