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NTU Management Review Vol. 33 No. 1 Apr. 2023

tasks, regulatory tasks (Chychyla, Leone, and Minutti-Meza, 2019), auditing unit size,
communication style, team culture, and individual characteristics (Francis and Yu,
2009; Proell, Zhou, and Nelson, 2022; Blum, Hatfield, and Houston, 2022), as well as
the independence of the audit works and the opinions of assessments (Lee, Chen, and

Tsai, 2020; Shiue, Yeh, and Chen, 2021); the analysis model constructs are incomplete.
Besides, the parameter changes do not significantly affect the model results, as the analysis
process of this study do not consider the self-adaptive mechanism to such changes in the

parameters. Future study can explore the adaptive parameters and further construct a more
complete model to improve the complete evaluation in practice.

5. Originality/Contribution

Compared with previous internal audit research, this study has the following
contributions. Given the trend of empirical research such as audit workforce planning,
internal audit variable discussion, and audit performance evaluation, this research

proposes an audit performance optimization model of integrated machine learning and
heuristic algorithm technology—an empirical study of audit performance management in
the financial industry. An integrative practical model is proposed by applying theoretical
perspectives on performance appraisals and auditing assignments from the past literature.
With the provided performance evaluation mode and audit-task scheduling mode, it

can solve the common audit management problems in an audit department of financial
holding companies (auditor performance forecast, audit task assignment, audit assignment
criteria). What’s more, we can systematically evaluate the performance of each auditor in
an enterprise. Both task performance and departmental review work planning optimization

are of substantial assistance. To provide a reference for the practical application of
management science methods in financial audit research, the data analysis model is
suitable for all audit departments in the financial industry.

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