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Development of a decision support system for client acceptance in independent audit process

Author

Listed:
  • Cebi, Selcuk
  • Karakurt, Necip Fazıl
  • Kurtulus, Erkan
  • Tokgoz, Bunyamin

Abstract

Intelligent Information Technology (IIT) applications are crucial in the audit process, enhancing quality, effectiveness, and efficiency. The client acceptance process (CAP), one of the critical audit steps, involves subjective evaluations where business managers' claims intersect with independent audit firm managers' expectations. This subjective nature introduces the potential for errors or misjudgments, impacting audit time and costs. In this paper, therefore, we propose a decision support system considering both auditors' subjective judgments and financial data variations for accepting or rejecting a client enterprise. The decision support system consisting of the Fuzzy Analytic Hierarchy Process (AHP), the logistic regression model, and the fuzzy inference system comprises four phases. In the first phase, a logistic regression model is developed using financial ratios to determine the client's probability of being in a close monitoring market (CMM) which represents publicly traded firms that are struggling to meet specific financial indicators or that are exposed to certain risks. In the second phase, the evaluation criteria used by the audit firm to measure the market reputation of the client enterprise are defined, and the weights of the evaluation criteria are obtained by using Fuzzy AHP. In the third phase, the Client Acceptance Score (CAS) representing market reputation of the client is calculated by incorporating the results of a reputation survey and applying the weights assigned to the evaluation criteria obtained in the second phase. Finally, client acceptance risk level (CARL) is obtained by using a fuzzy inference system and a rule-based defined by auditors. The CMM probability value and CAS score obtained in previous phases are used as input values of the fuzzy inference system. The CARL score guides the audit firm in deciding whether to engage with the client. To illustrate the applicability of the proposed model, a case study has been given in the paper.

Suggested Citation

  • Cebi, Selcuk & Karakurt, Necip Fazıl & Kurtulus, Erkan & Tokgoz, Bunyamin, 2024. "Development of a decision support system for client acceptance in independent audit process," International Journal of Accounting Information Systems, Elsevier, vol. 53(C).
  • Handle: RePEc:eee:ijoais:v:53:y:2024:i:c:s1467089524000162
    DOI: 10.1016/j.accinf.2024.100683
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    References listed on IDEAS

    as
    1. Krieger, Felix & Drews, Paul & Velte, Patrick, 2021. "Explaining the (non-) adoption of advanced data analytics in auditing: A process theory," International Journal of Accounting Information Systems, Elsevier, vol. 41(C).
    2. repec:eme:majpps:02686901311284522 is not listed on IDEAS
    3. Xin Chen & Yang Wang & Yifei Zhang, 2023. "Detecting Financial Statement Fraud Using Machine-Learning Methods," World Scientific Book Chapters, in: Daisy Chou & Conall O'Sullivan & Vassilios G Papavassiliou (ed.), FinTech Research and Applications Challenges and Opportunities, chapter 6, pages 235-263, World Scientific Publishing Co. Pte. Ltd..
    4. Bethany Hoogs & Thomas Kiehl & Christina Lacomb & Deniz Senturk, 2007. "A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 41-56, January.
    5. Lynnette Purda & David Skillicorn, 2015. "Accounting Variables, Deception, and a Bag of Words: Assessing the Tools of Fraud Detection," Contemporary Accounting Research, John Wiley & Sons, vol. 32(3), pages 1193-1223, September.
    6. repec:eme:majpps:02686900310495151 is not listed on IDEAS
    7. Jeremy Bertomeu & Edwige Cheynel & Eric Floyd & Wenqiang Pan, 2021. "Using machine learning to detect misstatements," Review of Accounting Studies, Springer, vol. 26(2), pages 468-519, June.
    8. George Salijeni & Anna Samsonova-Taddei & Stuart Turley, 2019. "Big Data and changes in audit technology: contemplating a research agenda," Accounting and Business Research, Taylor & Francis Journals, vol. 49(1), pages 95-119, January.
    9. Jenny Zha Giedt, 2018. "Modelling Receivables and Deferred Revenues to Detect Revenue Management," Abacus, Accounting Foundation, University of Sydney, vol. 54(2), pages 181-209, June.
    10. Messod D. Beneish, 1999. "The Detection of Earnings Manipulation," Financial Analysts Journal, Taylor & Francis Journals, vol. 55(5), pages 24-36, September.
    11. Geerts, Guido L., 2011. "A design science research methodology and its application to accounting information systems research," International Journal of Accounting Information Systems, Elsevier, vol. 12(2), pages 142-151.
    12. Jianrong Yao & Yanqin Pan & Shuiqing Yang & Yuangao Chen & Yixiao Li, 2019. "Detecting Fraudulent Financial Statements for the Sustainable Development of the Socio-Economy in China: A Multi-Analytic Approach," Sustainability, MDPI, vol. 11(6), pages 1-17, March.
    13. Kurt M. Fanning & Kenneth O. Cogger, 1998. "Neural network detection of management fraud using published financial data," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 7(1), pages 21-41, March.
    14. Chen, Yuh-Jen & Wu, Chun-Han & Chen, Yuh-Min & Li, Hsin-Ying & Chen, Huei-Kuen, 2017. "Enhancement of fraud detection for narratives in annual reports," International Journal of Accounting Information Systems, Elsevier, vol. 26(C), pages 32-45.
    15. Chang, Da-Yong, 1996. "Applications of the extent analysis method on fuzzy AHP," European Journal of Operational Research, Elsevier, vol. 95(3), pages 649-655, December.
    16. repec:eme:arjpps:arj-04-2020-0079 is not listed on IDEAS
    17. Tianlang Xiong & Zhishuo Ma & Zhuangzhuang Li & Jiangqianyi Dai, 2022. "The analysis of influence mechanism for internet financial fraud identification and user behavior based on machine learning approaches," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 996-1007, December.
    18. Chyan-long Jan, 2018. "An Effective Financial Statements Fraud Detection Model for the Sustainable Development of Financial Markets: Evidence from Taiwan," Sustainability, MDPI, vol. 10(2), pages 1-14, February.
    19. Guohua Zhang & Chengtang Wang & Yuyong Jiao & Hao Wang & Weimin Qin & Wu Chen & Guoqiang Zhong, 2020. "Collapse Risk Analysis of Deep Foundation Pits in Metro Stations Using a Fuzzy Bayesian Network and a Fuzzy AHP," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-18, April.
    20. T. Padma & S. P. Shantharajah & P. Ramadoss, 2022. "Hybrid Fuzzy AHP and Fuzzy TOPSIS Decision Model for Aquaculture Species Selection," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 21(03), pages 999-1030, May.
    21. Belinna Bai & Jerome Yen & Xiaoguang Yang, 2008. "False Financial Statements: Characteristics Of China'S Listed Companies And Cart Detecting Approach," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 339-359.
    22. repec:eme:majpps:02686900410509802 is not listed on IDEAS
    23. Tusar Kanti Hembram & Sunil Saha, 2020. "Prioritization of sub-watersheds for soil erosion based on morphometric attributes using fuzzy AHP and compound factor in Jainti River basin, Jharkhand, Eastern India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(2), pages 1241-1268, February.
    24. Sutton, Steve G. & Arnold, Vicky & Collier, Phil & Leech, Stewart A., 2021. "Leveraging the synergies between design science and behavioral science research methods," International Journal of Accounting Information Systems, Elsevier, vol. 43(C).
    25. Michael Haenlein & Andreas Kaplan & Chee-Wee Tan & Pengzhu Zhang, 2019. "Artificial intelligence (AI) and management analytics," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(4), pages 341-343, October.
    26. repec:eme:majpps:02686900210424321 is not listed on IDEAS
    27. Craja, Patricia & Kim, Alisa & Lessmann, Stefan, 2020. "Deep Learning application for fraud detection in financial statements," IRTG 1792 Discussion Papers 2020-007, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    Full references (including those not matched with items on IDEAS)

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