Research on Anomaly Detection Algorithm System for Financial Transaction Data Processing Based on Deep Learning

This paper studies an anomaly detection algorithm system for financial transaction data processing based on deep learning. First, this paper proposes an anomaly detection algorithm system based on deep learning, which combines multi-layer neural networks with time series analysis methods to process financial transaction data and identify abnormal patterns. Then, the features of financial transaction data are automatically extracted through DNN, and then LSTM is used for time series modeling to capture long-term dependencies in transaction behaviors. Further, AE and VAE are used to reduce the dimension and reconstruct the input data, so as to effectively identify potential abnormal behaviors. Finally, the system performs anomaly detection by comparing the reconstruction error with the set threshold to ensure the efficiency and accuracy of the algorithm. The simulation results of the system show that this method performs better than traditional anomaly detection methods on real financial data sets, with higher detection accuracy and lower false alarm rate. Through comparative experiments, this paper also analyzes the advantages and disadvantages of different algorithms in processing financial transaction data, and verifies the advantages of deep learning models in complex data environments.