Page 9 - 臺大管理論叢第33卷第1期

NTU Management Review
Vol. 33 No. 1 Apr. 2023, 1-38
DOI:10.6226/NTUMR.202304_33(1).0001

An Integrated Data-Driven Methodology for Auditor
Performance Appraisals and Auditor Assignment Optimization

整合數據驅動方法以進行稽核績效最佳化之稽核指派

Tzu-Chien Wang, Department of Business Administration, National Taiwan University
王子騫 / 國立臺灣大學商學研究所
Received 2020/9, Final revision received 2022/5

Abstract
With the expansion of related business groups and impact of digitization on financial
industry in Taiwan, in recent years, financial holding companies often fail to provide
adequate internal supervision and management due to problems of audit-staff scheduling,
which frequently leads to adjudication and penalization. The study aims to solve the
problem and find better performance evaluation method of auditors beginning by
reviewing past performance appraisal models in prior literature. We then integrate
the random forest and differential evolution technology and propose a two-stage data
analysis method. Applying this method, we analyze audit data collected from the financial
information system; optimize the internal audit performance; and resolve issues related
to audio performance evaluation and task planning. The verification results show this
two-stage model can accurately evaluate the audit performance of high-level audits
under different tasks; smoothly assign appropriate audit tasks; and strengthen operational
decisions. Secondly, we prove the construction of a multi-objective mathematical model
based on provision of professional courses, audit qualification system, task auditing,
etc. can optimally classify the most applicable action plan and dispatch rules, which are
extremely helpful to the management of audit-staff scheduling. Lastly, with the practical
application of this audit data project, we demonstrate the heuristic method suggested
would be more feasible in execution than the conventional planning method.
【Keywords】decision-making model, auditors performance appraisals, audit-task
scheduling, dispatching rules, integrated data-driven models

領域主編：蔡瑞煌教授

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