Open Access Open Access  Restricted Access Subscription Access

Fraud Aware Banking Platform

Myaka Manisha, Chakali Anusha, Mr. G. Nagi Reddy, K. Sunitha, N. Musrat Sulthana

Abstract


This project is designed to provide a robust fraud detection system that addresses the growing threats in today’s digital banking environment, particularly synthetic fraud and account takeover attempts. Synthetic fraud, where attackers create fake identities using a mix of real and fabricated information, and account takeover attempts, where hackers gain unauthorized access to legitimate user accounts, have become major challenges for financial institutions. These fraudulent activities lead to significant financial losses and erode customer trust. Our system specifically targets these sophisticated attacks to enhance the security and reliability of banking operations. To counter these threats, our application integrates advanced authentication mechanisms at both the login and transfer stages. By enforcing strict user verification processes, we aim to block unauthorized access early and prevent fraudulent transactions before they occur. Our system utilizes a combination of real-time risk analysis, behavior monitoring, and multi- factor authentication to detect anomalies and alert users and administrators to suspicious activities immediately.


Full Text:

PDF

References


Mirtaheri, S. L. (2022). Fraud detection in banking data by machine learning techniques. IEEE Conference on Bank Fraud Detection Using Machine Learning. https://doi.org/10.1109/ACCESS.2022.3232287

Patil, A., Mahajan, S., Menpara, J., Wagle, S., Pareek, P., & Kotecha, K. (2024). Enhancing fraud detection in banking by integration of graph databases with machine learning.

Achary, R., & Shelke, C. J. (2023). Fraud detection in banking transactions using machine learning. 2023 ResearchGate Conference on Fraud Detection. https://doi.org/10.1109/IITCEE57236.2023.10091067

Agrawal, S., Saikiran, M., Rajesh, G., & Chary, K. S. K. (2025). Fraud detection in internet banking using machine learning. International Journal of Research Publication and Reviews, 6(1). ISSN 2582-7421.

Isangediok, M., & Gajamannage, K. (2022). Fraud detection using optimized machine learning tools under imbalance classes. arXiv preprint arXiv:2209.01642. https://arxiv.org/abs/2209.01642


Refbacks

  • There are currently no refbacks.