The objective of this project is to develop a cloud-based analytics system that detects fraudulent online transactions by analyzing transaction patterns. The system uses scalable cloud infrastructure to process large datasets and generate alerts, improving transaction security and reducing financial risks.
Study fundamentals of online transaction systems and fraud detection techniques Analyze different types of financial fraud and common detection challenges Design a cloud-based architecture for transaction data ingestion and processing Create a secure cloud database to store transaction records Implement data preprocessing techniques to clean and normalize transaction data Apply analytical rules and pattern-based detection methods to identify anomalies Develop dashboards to visualize suspicious transactions and fraud trends Implement real-time alert mechanisms for detected fraudulent activities Secure sensitive financial data using encryption and access control mechanisms Test system accuracy using sample transaction datasets Evaluate scalability and performance under high transaction loads Document system design, analytical approach, and future improvements
