Decision Tree Tool for Auditors’ Going Concern Assessment in Spain

Abstract

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The COVID-19 pandemic increased uncertainty about the financial future of many organizations, and regulators alerted auditors to be increasingly skeptical in assessing an entity’s ability to continue as a going concern. An auditor’s assessment of an entity’s ability to continue as a going concern is a matter of significant judgment. This paper proposes to use machine learning to construct a Decision Tree Automated Tool, based on both quantitative financial indicators (e.g., Zscores) and qualitative factors (e.g., partners’ judgment and assessment of industry risk given the pandemic). Considering both quantitative and qualitative factors results in a model that provides additional audit evidence for auditors in their going-concern assessment. An auditing firm in Spain used the model as a supplemental guide, and the model’s suggested results were compared to auditors’ reports to evaluate its effectiveness and accuracy. The model’s predictions were significantly similar to the auditors’ assessments, indicating a high level of accuracy, and differences between the model’s proposed outcomes and auditors’ final conclusions were investigated. This paper also provides insights for regulators on both the use of machine-learning predictive models and additional factors to be considered in future going-concern assessment research.