References

[1] Smith, J., Patel, R., and Kumar, S., "AI-powered fraud prevention: Case study of PayPal’s real-time detection," Journal of Financial Security, vol. 35, no. 2, pp. 120-135, 2022.

[2] Kumar, A., and Patel, D., "Machine learning in fraud detection: A study on Mastercard’s Decision Intelligence AI," International Conference on Cybersecurity and AI, pp. 78-92, 2023.

[3] Chen, X., Li, M., and Wang, Y., "AI-driven fraud detection in banking: JPMorgan Chase’s approach to large-scale fraud prevention," IEEE Transactions on Financial Technology, vol. 14, no. 3, pp. 256-270, 2023.

[4] Zhao, H., Lin, J., and Xu, B., "Graph neural networks for anomaly detection in financial transactions," AI & Financial Security Journal, vol. 27, no. 1, pp. 45-60, 2022.

[5] Li, X., and Zhou, L., "Self-supervised learning for fraud detection: Enhancing accuracy with limited labeled data," Proceedings of the AI & Finance Conference, pp. 102-118, 2023.

[6] Chen, M., and Wang, P., "Federated learning in fraud detection: Balancing privacy and security in AI models," Journal of Machine Learning for Finance, vol. 19, no. 4, pp. 310-325, 2022.

[7] Jones, R., Singh, A., and Verma, K., "AI in insurance fraud detection: Reducing false claims with deep learning," Insurance Technology Review, vol. 12, no. 3, pp. 88-103, 2023.

[8] Zhao et al., "AI in financial security and risk compliance," Financial Fraud Review, vol. 15, no. 2, pp. 33-49, 2022.

[9] Smith et al., "Insider trading detection using AI-driven sentiment analysis," Stock Market Security Journal, vol. 10, no. 1, pp. 77-91, 2023.

[10] Li et al., "Blockchain technology in AI-powered fraud prevention," Journal of Cryptographic Security, vol. 22, no. 4, pp. 300-315, 2023.

[11] A. Awasthi, G. Khan, and M. Bargavi, "Application of machine learning to extraction of digital forensics evidence," International Conference on Optimization Computing and Wireless Communication (ICOCWC), pp. 1–6, 2024. doi: 10.1109/ICOCWC60930.2024.10470725.

Cite this Article as:Bargavi S.K.M, Yadav H, Dheeraj, & Faheem  (2025), The AI Shield: Detecting Financial Crimes in Real-Time”,  International Journal of Advances in Engineering & Scientific Research,  Volume 12, Issue 2, 2025, pp 11-28.  DOI : https://doi.org/10.5281/zenodo.15374618

References

[1] Smith, J., Patel, R., and Kumar, S., "AI-powered fraud prevention: Case study of PayPal’s real-time detection," Journal of Financial Security, vol. 35, no. 2, pp. 120-135, 2022.

[2] Kumar, A., and Patel, D., "Machine learning in fraud detection: A study on Mastercard’s Decision Intelligence AI," International Conference on Cybersecurity and AI, pp. 78-92, 2023.

[3] Chen, X., Li, M., and Wang, Y., "AI-driven fraud detection in banking: JPMorgan Chase’s approach to large-scale fraud prevention," IEEE Transactions on Financial Technology, vol. 14, no. 3, pp. 256-270, 2023.

[4] Zhao, H., Lin, J., and Xu, B., "Graph neural networks for anomaly detection in financial transactions," AI & Financial Security Journal, vol. 27, no. 1, pp. 45-60, 2022.

[5] Li, X., and Zhou, L., "Self-supervised learning for fraud detection: Enhancing accuracy with limited labeled data," Proceedings of the AI & Finance Conference, pp. 102-118, 2023.

[6] Chen, M., and Wang, P., "Federated learning in fraud detection: Balancing privacy and security in AI models," Journal of Machine Learning for Finance, vol. 19, no. 4, pp. 310-325, 2022.

[7] Jones, R., Singh, A., and Verma, K., "AI in insurance fraud detection: Reducing false claims with deep learning," Insurance Technology Review, vol. 12, no. 3, pp. 88-103, 2023.

[8] Zhao et al., "AI in financial security and risk compliance," Financial Fraud Review, vol. 15, no. 2, pp. 33-49, 2022.

[9] Smith et al., "Insider trading detection using AI-driven sentiment analysis," Stock Market Security Journal, vol. 10, no. 1, pp. 77-91, 2023.

[10] Li et al., "Blockchain technology in AI-powered fraud prevention," Journal of Cryptographic Security, vol. 22, no. 4, pp. 300-315, 2023.

[11] A. Awasthi, G. Khan, and M. Bargavi, "Application of machine learning to extraction of digital forensics evidence," International Conference on Optimization Computing and Wireless Communication (ICOCWC), pp. 1–6, 2024. doi: 10.1109/ICOCWC60930.2024.10470725.