Decision tree tool for auditors’ going concern assessment in Spain

C. Beretta Custodio, Y. Gu, J. Portela

International Journal of Digital Accounting Research Vol. 22, pp. 193 - 226

Resumen:

DOI: 10.4192/1577-8517-v22_7

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., Z-scores) 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.

Palabras Clave: Audit, going concern, machine learning, decision tree, COVID19.

Publicado en papel: 2022.

Publicado on-line: Noviembre 2022.

Cita:
C. Beretta Custodio, Y. Gu, J. Portela, Decision tree tool for auditors’ going concern assessment in Spain. International Journal of Digital Accounting Research. Vol. 22, pp. 193 - 226, 2022. [Online: Noviembre 2022]