A MACHINE LEARNING FRAMEWORK FOR REAL-TIME ANOMALY DETECTION IN FINANCIAL TRANSACTIONS

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

The rapid growth of digital transactions in sectors such as banking, e-commerce, and financial services has increased the risk of fraudulent activities and abnormal transaction behavior. Traditional fraud detection systems often rely on static, rule-based methods that fail to capture the evolving and sophisticated nature of modern anomalies. This paper proposes an intelligent framework for Real-Time Anomaly Detection in Transaction Data leveraging advanced machine learning algorithms. By analyzing continuous streams of transaction data, the system identifies suspicious patterns and outliers with high precision and minimal latency. The framework integrates feature engineering, adaptive learning, and real-time visualization to provide actionable insights for fraud prevention and risk management. Experimental evaluations conducted on simulated transaction streams demonstrate the system's effectiveness in maintaining high detection accuracy while adapting to dynamic data shifts. This research provides a scalable and robust solution for securing digital financial ecosystems in high-velocity environments.

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Keywords

Anomaly Detection, Machine Learning, Transaction Data, Real-Time Analytics, Fraud Detection, Financial Security, Streaming Data.