An intelligent risk warning model of enterprise internal audit based on deep autoencoder network in the ChatGPT era:Audit case of current accounts
The generative AI technology represented by ChatGPT,considered as the second information revolution,have transformed the depth of data analysis,offering new perspectives for intelligent internal audits in enterprises.In response to the limitations in the existing audit risk warnings,such as the limited improvement in the generalization capability of traditional machine learning and the insufficient feature analysis dimensions,we propose a method based on the core technology of ChatGPT—A deep autoencoder network.This method aims to pre-determine risks in the critical accounting activity of incoming funds.First,based on influencing factors,audit features are selected and extracted from various perspectives including business matching,term structure,impairment loss,related transactions,individual statistics,and text information.Subsequently,considering the imbalance of risk samples and the temporal charac-teristics of financial indicators over the operating cycle,an unsupervised and deep learning-based approach is employed.This involves constructing a deep autoencoder(DAE)pre-training model with the addition of an attention mechanism and employing bidirectional long short-term mem-ory(Bi-LSTM)as the network.Additionally,drawing from the concept of multi-task learning,an integrated framework with model transfer is utilized to quantify audit risk probabilities,en-suring the stability of warnings.Finally,real data from enterprise transactions and finances are collected by using big data technology for comprehensive comparative validation of the proposed method.Experimental results indicate that this method effectively and accurately extracts audit features under different warning time windows.In comparison to common practices like super-vised learning and iterative clustering,it significantly enhances the precision and robustness of audit risk warnings.Moreover,it also identifies key factors leading to risk,enabling quickly swift localization of audit suspicions.Our study can provide intelligent decision support for enterprises to improve the quality and efficiency of internal audit.
deep autoencoderrisk early warning of internal auditaccounts receivable and payablesintelligent audit
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