Optimizing Cloud Infrastructure for Real-Time Fraud Detection in Credit Card Transactions

@article{1704128,
      author = {Jeyasri Sekar},
      title = {Optimizing Cloud Infrastructure for Real-Time Fraud Detection in Credit Card Transactions},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {9},
      pages = {381-388},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704128.pdf},
      abstract = {The rapid increase in digital financial transactions has led to a significant rise in credit card fraud, necessitating the development of advanced detection systems. This paper explores the enhancement of cloud-based architectures for real-time fraud detection in credit card transactions. By leveraging cloud technologies, machine learning, and artificial intelligence, organizations can efficiently process large volumes of transaction data, detect fraudulent activities as they occur, and adapt to emerging fraud patterns. The paper discusses key components, including data ingestion, real-time processing, machine learning model deployment, security, and compliance measures. Additionally, it highlights the importance of continuous testing, evaluation, and system improvement to maintain the effectiveness of the fraud detection system. This approach ensures robust protection for consumers and businesses alike, reinforcing trust in digital financial systems.},
      keywords = {Credit card fraud detection, Cloud architecture, Machine learning, Artificial intelligence, Data ingestion, Security and compliance, Financial transactions, Fraud prevention},
      month = {March},
  }