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Intelligent Financial Auditing Model Based on Deep Learning.

Xiaofeng Dai1,2, Weidong Zhu2

  • 1Department of Engineering Management, Anhui Audit College, Hefei 230601, China.

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|September 8, 2022
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Summary
This summary is machine-generated.

This study introduces an intelligent financial audit model using deep belief neural networks (DBN) and long-short term memory (LSTM) to predict audit opinions. The novel approach enhances audit quality by automating complex financial statement analysis.

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Area of Science:

  • Accounting Information Systems
  • Artificial Intelligence in Auditing
  • Financial Statement Analysis

Background:

  • Traditional auditing processes are resource-intensive and complex.
  • The integration of artificial intelligence offers a pathway to enhance audit efficiency and accuracy.
  • Predicting audit opinions is crucial for stakeholders and regulatory compliance.

Purpose of the Study:

  • To develop an intelligent financial audit model for predicting audit opinions on consolidated financial statements.
  • To improve the overall quality and efficiency of the auditing process through AI integration.
  • To leverage deep learning techniques for more accurate and reliable audit opinion prediction.

Main Methods:

  • An indicator system for audit opinions was established using multiple financial parameters.
  • A deep belief neural network (DBN) was employed for deep feature extraction.
  • A long-short term memory (LSTM) network was trained using the extracted features for prediction.

Main Results:

  • The proposed DBN-LSTM model demonstrated validity and reliability in predicting audit opinions.
  • Experimental results showed superior performance compared to traditional Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and standalone LSTM models.
  • The model accurately predicts subsequent audit opinions based on financial data.

Conclusions:

  • The intelligent financial audit model effectively predicts audit opinions, addressing the limitations of traditional methods.
  • The fusion of DBN and LSTM offers a robust solution for enhancing financial audit quality.
  • This AI-driven approach represents a significant advancement in the intelligent development of auditing practices.