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This research investigates the effectiveness of data mining techniques in assessing companies’ going concern by external auditors within the Syrian business environment. The study focuses on industrial and agricultural companies listed on the Damascus Securities Exchange (DSE), covering three companies over nine financial periods from 2015 to 2023, yielding 27 observations. The Altman Z-score model was employed as a traditional benchmark for going concern assessment, and its results were compared with those derived from data mining techniques using the Support Vector Machine (SVM) algorithm, optimized by Particle Swarm Optimization (PSO).The findings reveal a 17% discrepancy between the results of traditional auditing methods and data mining techniques within the sample. Moreover, the study highlights the superior predictive accuracy of the SVM algorithm, enhanced with PSO, over conventional models in evaluating companies’ going concern.

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Data Mining Approach for External Auditors in Assessing Companies Going Concern

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  • 发表日期：

    2025年

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  • Ziad Mohammad Roustom,
  • Samer Msallam Shawqal

摘要

This research investigates the effectiveness of data mining techniques in assessing companies’ going concern by external auditors within the Syrian business environment. The study focuses on industrial and agricultural companies listed on the Damascus Securities Exchange (DSE), covering three companies over nine financial periods from 2015 to 2023, yielding 27 observations. The Altman Z-score model was employed as a traditional benchmark for going concern assessment, and its results were compared with those derived from data mining techniques using the Support Vector Machine (SVM) algorithm, optimized by Particle Swarm Optimization (PSO).The findings reveal a 17% discrepancy between the results of traditional auditing methods and data mining techniques within the sample. Moreover, the study highlights the superior predictive accuracy of the SVM algorithm, enhanced with PSO, over conventional models in evaluating companies’ going concern.