Deep Learning Approaches for Real-Time Payment Fraud Detection in High-Volume Transaction Systems
Abstract
This paper presents a novel deep learning architecture for detecting fraudulent transactions in real-time payment systems processing millions of transactions per second. We introduce a hybrid model combining convolutional neural networks (CNNs) for pattern recognition with long short-term memory (LSTM) networks for temporal sequence analysis. Our approach achieves a 99.7% detection rate with a false positive rate of only 0.02%, significantly outperforming traditional rule-based systems. The model was validated against a dataset of 50 million transactions from a major payment processor, demonstrating both accuracy and computational efficiency suitable for production deployment.
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Table of Contents
Extended Summary
This research addresses the critical challenge of fraud detection in modern payment infrastructure, where traditional rule-based systems struggle to keep pace with increasingly sophisticated fraud patterns.
Key Contributions
Hybrid Architecture: We propose a novel combination of CNN and LSTM networks that processes both spatial transaction patterns and temporal sequences simultaneously.
Real-Time Processing: Our model achieves inference times under 10ms, enabling deployment in systems requiring sub-second transaction approval.
Interpretability: Unlike black-box approaches, our feature importance analysis provides actionable insights for fraud prevention teams.
Methodology
The study utilized anonymized transaction data spanning 18 months, with careful attention to class imbalance through synthetic minority oversampling (SMOTE) and cost-sensitive learning approaches.
Results
| Metric | Our Model | Baseline (Rules) | Previous SOTA |
|---|---|---|---|
| Detection Rate | 99.7% | 94.2% | 98.1% |
| False Positive Rate | 0.02% | 0.15% | 0.08% |
| Inference Time | 8ms | 2ms | 45ms |
Implications
This work demonstrates that deep learning can be practically deployed in high-stakes financial systems while maintaining the interpretability requirements of regulatory compliance.
Cite This Paper
Abhimanyu, Sarah Chen, Michael Roberts. "Deep Learning Approaches for Real-Time Payment Fraud Detection in High-Volume Transaction Systems." Journal of Financial Technology , 2024. https://doi.org/10.1234/jft.2024.0315