Manual Journal Entry Testing: Integrating Natural Language Processing and Deep Learning
Document Type
Peer-Reviewed Article
Publication Date
2025
Abstract
This paper presents an innovative approach to comprehensively and systematically evaluate manual journal entries (MJEs) and enhance the control procedures in auditing. The proposed approach combines quantitative and qualitative information to develop various Key Risk Indicators (KRIs) that provide insights into potential risks associated with MJEs. The approach incorporates textual analytics into traditional quantitative measures. Using the data obtained from a multinational company, the application of the proposed testing approach demonstrates its effectiveness in identifying potential high-risk MJEs and improving the company's journal entry testing and monitoring procedures. The findings contribute to current audit practices by offering a more efficient and comprehensive method for evaluating MJEs.
DOI
10.1002/isaf.70016
Recommended Citation
Huang, Q., Duan, H. K., & Vasarhelyi, M. A. (2025). Manual journal entry testing: Integrating natural language processing and deep learning. Intelligent Systems in Accounting, Finance and Management, 32(3), e70016. Doi: 10.1002/isaf.70016
