Client acceptance method for audit firms based on interval-valued fuzzy numbers
To ensure that investors are getting financial statements that conform to Generally Accepted Accounting Principles, the security exchange committee requires publicly traded companies to hire external auditors. Because the information provided in company financial statements has significant economic and social consequences for various parties, external auditors are needed to minimize litigation. Therefore, it is important for audit firms’ risk management teams to evaluate which clients to accept. In this paper, we propose a client acceptance method (CAM) that uses a technique for order preference using similarity to the ideal solution (i.e. TOPSIS) approach to evaluate potential new clients using a decision-making method with interval-valued fuzzy numbers (IVFNs). Through a case study, this paper shows that this CAM results in a high Spearman rankorder correlation coefficient (0.9 to 1.0) with human judgment. This result indicates that CAM could help decision makers evaluate potential clients before acceptance, especially when there are several potential clients but limited resources to provide services. The CAM also could help audit firms more easily ensure that decision makers are complying with firm policies concerning client acceptance through the establishment of uncertainty factor weights.
First published online: 28 Jan 2014
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