Audit Risk Prediction Based on Long Short-Term Memory (LSTM) Algorithm
Keywords:
Audit risk prediction, deep learning, ong short-term memory algorithmAbstract
The objective of the present study is to predict audit risk using the Long Short-Term Memory (LSTM) algorithm in companies listed on the Tehran Stock Exchange and to compare its results with those of other deep learning algorithms. To achieve this objective, a total of 1,650 firm-year observations (150 companies over 11 years) were collected from the annual financial reports of companies listed on the Tehran Stock Exchange during the period from 2013 to 2023. In this study, four deep learning algorithms—including Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN)—were utilized. Additionally, to select the final research variables for model construction, the two-sample mean comparison test method was applied. The results of the deep learning algorithms show that the overall accuracy of the LSTM, SVM, CNN, and RNN algorithms was 99.1%, 89.6%, 85.8%, and 96.4%, respectively, indicating that the LSTM algorithm has the best performance and the CNN algorithm the poorest performance in predicting audit risk. In other words, the results demonstrate the superior efficiency of the Long Short-Term Memory (LSTM) algorithm compared to other deep learning algorithms. Therefore, in companies listed on the Tehran Stock Exchange, the LSTM algorithm provides the most efficient model for audit risk prediction. The findings of this study can offer useful insights into enhancing the prediction of audit risk and minimizing errors in evaluating financial statement information, improving the assessment of audit evidence based on data, and facilitating auditors’ ability to issue more reality-based opinions.
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