Deep Learning-Based Real-Time Fraud Detection In Digital Payments

The rapid growth of digital payment systems has significantly increased the risk of fraudulent transactions. Traditional fraud detection methods often fail to identify sophisticated and evolving fraud patterns in real time. This paper proposes a deep learning-based approach for detecting fraudulent transactions in digital payment systems. The model utilizes advanced neural network architectures to analyze transaction patterns and identify anomalies with high accuracy. By leveraging real-time data processing and adaptive learning techniques, the proposed system improves detection efficiency while minimizing false positives. Experimental results demonstrate that deep learning models outperform traditional machine learning approaches in terms of accuracy, precision, and recall. The study highlights the importance of intelligent systems in securing digital financial ecosystems.