Intelligent auditing and risk management based on encoding/decoding structure and gated recurrent units
摘要
This research introduces an audit multidimensional temporal risk prediction model that integrates variational autoencoders, gated recurrent units, and a position-aware mechanism, to enhance the efficacy of audit risk detection. The model introduces a dual-branch module of temporal attention and position awareness into an improved variational autoencoder-gated recurrent unit structure. It captures multi-scale risk evolution features through sliding window segmentation. In the experiment, the proposed model achieves F1 scores of 97.21% and 96.57% on two types of public datasets, respectively, with a detection latency reduced to approximately 60 ms. In the noise robustness experiment, the false negative rate is controlled within 6.52% under 30% noise interference, and the statistical test p value is less than 0.05. The method has broad application potential in intelligent financial auditing, internal control assessment, and enterprise risk management.