Real-Time Fraud Detection and Prevention in Financial Transactions Using Big Data Analytics on Microsoft Azure

Abstract

- The rapid growth of digital financial transactions has increased the risk of financial fraud and created a need for fast and accurate fraud detection systems. Traditional rule-based and batch-processing approaches may not be effective against continuously changing fraud patterns and can also produce a high number of false alerts. This research proposes a real-time fraud detection and prevention framework using Big Data Analytics, Machine Learning, and Microsoft Azure. The proposed system integrates transaction data, customer information, behavioral patterns, and other relevant data sources to identify suspicious transactions in real time. Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, Azure Stream Analytics, and Azure Machine Learning are used to support data storage, distributed processing, real-time analysis, and machine learning model deployment. The study evaluates multiple machine learning algorithms, including Logistic Regression, Random Forest, XGBoost, LightGBM, Neural Networks, and Isolation Forest. The research also considers class imbalance using techniques such as SMOTE, cost-sensitive learning, and threshold optimization. Initial analysis of the Credit Card Fraud Detection dataset containing 284,807 transactions identified 492 fraudulent transactions, representing only 0.17% of the total data. Preliminary results show that XGBoost achieved an F1-score of 90.1%, while the implemented Azure-based approach demonstrated the potential for sub-100 ms inference latency. The research aims to provide a scalable, secure, and cost-effective framework for real-time fraud detection while considering privacy, regulatory compliance, and evolving fraud patterns.