• Prini Rastogi
• Shailesh Patil
• Sohum Pawar

The increasing popularity of digital payment systems has led to a growing requirement for real-time fraud detection mechanisms to protect financial transactions. Given the rise of more sophisticated malicious actions, HMRBSs may not be able to handle such forward-thinking threats. The objective of this research is to investigate the possibilities to utilize artificial intelligence (AI) and cloud computing technologies in real-time financial fraud detection. By incorporating recent innovations, such as AI-based analytics, transformer-based models, and GNNs, the basis of this paper explores how these innovations affect the scalability, accuracy, and costs of detecting anomalies. It also presents implemented deployments on cloud environment with flexibility and real-time computing. An in-depth analysis on the technology fundamentals, implementation methodologies, and performance comparisons is presented based on recent peer-reviewed literature and technical reports. The paper also considers serious challenges as those of privacy of data, interpretability of AI models, and regulation. The results indicate that CN towards AI could substantially improve fraud detection performance, which also point to promising solutions for secure and reactive financial transaction analytics.

Fraud Detection, Cloud-based Transaction Processing, Google Cloud Platform (GCP), BigQuery, Pub/Sub, Transaction Monitoring, Streaming Data Pipeline, Merchant Dashboard, Digital Payments.

"SMART MERCHANT PORTAL: ENHANCING TRANSACTION DATA TRANSPARENCY WITH SCALABLE DATA PROCESSING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.b199-b214, May-2025, Available :http://www.jetir.org/papers/JETIR2505123.pdf

"SMART MERCHANT PORTAL: ENHANCING TRANSACTION DATA TRANSPARENCY WITH SCALABLE DATA PROCESSING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppb199-b214, May-2025, Available at : http://www.jetir.org/papers/JETIR2505123.pdf