Comparing Neural Networks and Traditional Algorithms in Fraud Detection
Abstract
Fraud detection has become an essential component of financial security systems. Traditional algorithms have long served as the backbone of these systems. The rise of neural networks is revolutionizing the process as it offers new approaches to identifying complex fraud patterns. The paper presents a comparative analysis of neural networks and traditional algorithms. These include decision trees, rule-based systems, and logistic regression in fraud detection. The comparison is based on scalability, accuracy, interpretability, computational efficiency, and adaptability. The findings reveal that neural networks outperform traditional methods in subtle, non-linear fraud patterns but suffer from interpretability and data requirements. A hybrid detection framework that combines neural intelligence with rule-based logic is proposed for real-time, robust fraud management. For instance, a neural ensemble model achieved over 97% accuracy while traditional systems achieved 89-91%. The paper highlights that the hybrid approach offers optimal results in real-world scenarios.
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References
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