Literature DB >> 32044113

Developing a strategy map for forensic accounting with fraud risk management: An integrated balanced scorecard-based decision model.

Chih-Hao Yang1, Kuen-Chang Lee2.   

Abstract

Corporate fraud risk management strategy has increasingly become a sustainable business development goal. Recent reforms in forensic accounting technology for corporate fraud risk management globally have opened up new avenues for corporate governance and internal control mechanism implementation. This study thus presents an integrated methodology for forensic accounting implementation to improve the identification of the strategy map relationship between the Balanced Scorecard (BSC)-based perspective and criteria, by combining multiple-criteria decision making (MCDM) with the Decision Making Trial and Evaluation Laboratory (DEMATEL) and the Analytic Network Process (ANP) techniques. The results have implications for corporate decision-makers to effectively fulfil corporate governance quality assurance and anti-fraud through a forensic accounting strategy map illustration. From the evaluation and planning perspective, the in-depth analysis of strategy map is useful to obtain an interrelationship that takes as its starting point the practice professions of the decision maker to improve existing strategy alternatives and focus on the valuable strategy paths. In the evaluation planning application, a strategy map of forensic accounting presents the knowledge regarding key indicators' priorities to achieve satisfactory strategy planning and to practice forensic accounting development linked to fraud risk management in Taiwan.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Keywords:  Balanced Scorecard (BSC); Forensic accounting; Fraud risk management; Multiple Criteria Decision Making (MCDM); Strategy map

Year:  2020        PMID: 32044113     DOI: 10.1016/j.evalprogplan.2020.101780

Source DB:  PubMed          Journal:  Eval Program Plann        ISSN: 0149-7189


  1 in total

1.  Using an Optimized Learning Vector Quantization- (LVQ-) Based Neural Network in Accounting Fraud Recognition.

Authors:  Yuan Zheng; Xiaolan Ye; Ting Wu
Journal:  Comput Intell Neurosci       Date:  2021-06-28
  1 in total

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