Decision-analytics-based enhancing auditor performance evaluation: Application of multi-criteria methodology with Monte Carlo simulation

Abstract

This study highlights the critical importance of auditor performance, as it directly influences the quality and reliability of financial reporting, which is crucial for maintaining trust in financial markets. High-performing auditing firms contribute to improved transparency and accountability, which are essential for effective corporate governance and investor confidence. The study evaluates the performance of 27 auditing firms in Turkey using two advanced MCDM methods: WENSLO and ARTASI. By focusing on measurable, operational factors such as the number of audited companies, training duration, and client exposure, the study provides a more data-driven and objective approach to performance evaluation. The WENSLO method assigns weights to these criteria, while ARTASI ranks the firms accordingly. According to the WENSLO results, the two most important criteria among those examined are the number of completed audits and the duration of training. The results reveal that PKF, HSY, and YEDITEPE are the top performers, while ECOVIZ, VEZIN, and REFORM ranked the lowest. The study’s contribution lies in its novel approach to performance assessment, offering a comprehensive, data-driven evaluation model that emphasizes quantifiable metrics. It also demonstrates the robustness of the findings through sensitivity analyses and comparisons with other MCDM techniques, such as MABAC and TOPSIS. The study also incorporates a Monte Carlo simulation, which tested the impact of random weight variations on the ARTASI rankings. The simulation confirmed the robustness of the rankings, revealing that certain firms consistently outperformed others, regardless of changes in the weight distribution. The findings suggest that industry specialization and operational factors play a significant role in auditing performance, and future research could expand these criteria or explore additional MCDM methods to enhance the reliability and generalizability of results.