Machine Learning Techniques for Real-Time Fraud Detection in Financial Transactions
Anavena Rajkumar · Lloyd Business Review · 2025
Financial fraud poses a significant threat to financial institutions and customers, leading to substantial monetary losses and undermining trust. Traditional fraud detection methods, such as rule-based systems, have proven inadequate in detecting sophisticated fraud patterns. This paper investigates advanced machine learning algorithms for real-time fraud detection in financial transactions, including Random Forest, Support Vector Machine (SVM), and Artificial Neural Networks (ANN). The study demonstrates that these models can identify fraudulent activities with high accuracy and minimal false positives. The experimental results indicate that Random Forest outperforms other models in terms of accuracy, precision, recall, and F1 score. The findings suggest that machine learning models can significantly enhance fraud detection systems, reducing false positives and providing better protection for financial institutions and customers