Intelligent analysis of transaction fraud based on machine learning
With the increasing prevalence of online transactions,transaction fraud has become a growing concern,causing substantial losses to consumers.This paper presents a machine learning-based transaction fraud analysis system that identifies potential fraudulent transactions by analyzing transaction data and user behavior patterns.The system employs machine learning algorithms,such as decision trees and neural networks,to train on large-scale transaction data and evaluates its performance on real-world datasets.The results demonstrate that this intelligent analysis system exhibits outstanding performance in terms of accuracy,recall,and F1 score,effectively identifying potential fraudulent transactions and providing real-time alerts.
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