@article{paperid:1106362,
author = {Mohammadhamed Khanmohammadi and Salehi, Mahdi and Mohammad Hassani and Arezoo Memarimoghadam},
title = {The alchemy of accuracy: machine learning illuminates audit fees in emerging markets},
journal = {Journal of Revenue and Pricing Management},
year = {2025},
month = {October},
issn = {1476-6930},
pages = {1--10},
			numpages = {9},
keywords = {The determination of audit fees in emerging markets often faces challenges due to the limitations of traditional linear estimation methods; necessitating more robust and adaptive predictive frameworks. This study aims to bridge this gap by employing advanced machine learning techniques to enhance the accuracy and transparency of audit pricing within the unique regulatory environment of the Tehran stock exchange. Utilizing a longitudinal dataset of financial indicators from listed companies; the research conducts a comprehensive comparative analysis of sixteen machine learning algorithms to identify the most effective predictive model. The empirical results demonstrate that the Gradient Boosting Regressor significantly outperforms traditional models and other algorithms; identifying asset size as the primary determinant of audit fees Furthermore; the research bridges the gap between theoretical modelling and practical application by developing and introducing a novel; user-friendly web-based platform. This tool allows auditors and client companies to utilize the optimized model for real-time; accurate fee estimation; thereby offering a tangible solution for modernizing audit pricing mechanisms in developing economies.},
}