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<p>While risk-oriented auditing has gradually emerged as a new trend in current internal auditing, the absence of specific risk assessment methods in related internal auditing standards has made it a common challenge for many enterprise internal auditing organizations and personnel to effectively implement and conduct risk assessments. This research focuses on integrating deep learning technology to construct an audit risk assessment model, first describing the problem of audit risk assessment and then validating it using the CNN-LSTM method. We select the appropriate indicators for audit risk assessment assignment after screening the sample data to ensure their qualitative and quantitative comparability. We construct a CNN-LSTM audit risk assessment model based on the above data. We use two input methods, the convolutional neural network and the long- and short-term memory network, to enhance the assessment model’s learning ability. We then choose 12 enterprise companies as data cases, with Company A serving as the representative for the CNN-LSTM-based assessment model’s performance simulation test and risk level assessment. We compare the four models, and the CNN-LSTM model’s AUC exceeds 0.5 in the confusion matrix and ROC curve graph, confirming the usefulness of CNN-LSTM in audit risk assessment.</p>
</abstract>

While risk-oriented auditing has gradually emerged as a new trend in current internal auditing, the absence of specific risk assessment methods in related internal auditing standards has made it a common challenge for many enterprise internal auditing organizations and personnel to effectively implement and conduct risk assessments. This research focuses on integrating deep learning technology to construct an audit risk assessment model, first describing the problem of audit risk assessment and then validating it using the CNN-LSTM method. We select the appropriate indicators for audit risk assessment assignment after screening the sample data to ensure their qualitative and quantitative comparability. We construct a CNN-LSTM audit risk assessment model based on the above data. We use two input methods, the convolutional neural network and the long- and short-term memory network, to enhance the assessment model’s learning ability. We then choose 12 enterprise companies as data cases, with Company A serving as the representative for the CNN-LSTM-based assessment model’s performance simulation test and risk level assessment. We compare the four models, and the CNN-LSTM model’s AUC exceeds 0.5 in the confusion matrix and ROC curve graph, confirming the usefulness of CNN-LSTM in audit risk assessment.

Audit Risk Assessment Model Construction by Incorporating Deep Learning Techniques

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

While risk-oriented auditing has gradually emerged as a new trend in current internal auditing, the absence of specific risk assessment methods in related...

Audit Risk Assessment Model Construction by Incorporating Deep Learning Techniques

Published Online: Aug 05, 2024

Page range: -

Received: Apr 12, 2024

Accepted: Jul 11, 2024

DOI: https://doi.org/10.2478/amns-2024-2138

Keywords
Deep Learning, Audit Risk Assessment, Convolutional Neural Network, Long and Short Term Memory Network, CNN-LSTM

© 2024 Zhenzheng Yan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Audit Risk Assessment Model Construction by Incorporating Deep Learning Techniques

Published Online: Aug 05, 2024

Page range: -

Received: Apr 12, 2024

Accepted: Jul 11, 2024

DOI: https://doi.org/10.2478/amns-2024-2138

KeywordsDeep Learning, Audit Risk Assessment, Convolutional Neural Network, Long and Short Term Memory Network, CNN-LSTM

© 2024 Zhenzheng Yan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Deep Learning, Audit Risk Assessment, Convolutional Neural Network, Long and Short Term Memory Network, CNN-LSTM