Abstract
A considerable body of regulatory pronouncements attests to the significance of auditing journal entries for ensuring that financial statements are free of material misstatements; however, existing empirical studies have paid insufficient attention to the audit of journal entries. To explore this issue further, this paper proposes a model based on self-organizing map as well as validates this model by performing experiments on a dataset containing journal entries. Empirical results suggest that the proposed model can detect {}``suspicious'' and legitimate transactions with a high degree of accuracy. Further investigations reveal that the performance of the model is robust to varying prior probabilities of {}``suspicious'' journal entries occurring in the population. The findings indicate that the model can assist auditors in detecting {}``suspicious'' journal entries.
Recommended Citation
Argyrou, Argyris, "Auditing Journal Entries Using Self-Organizing Map" (2012). AMCIS 2012 Proceedings. 16.
https://aisel.aisnet.org/amcis2012/proceedings/AccountingInformationSystems/16
Auditing Journal Entries Using Self-Organizing Map
A considerable body of regulatory pronouncements attests to the significance of auditing journal entries for ensuring that financial statements are free of material misstatements; however, existing empirical studies have paid insufficient attention to the audit of journal entries. To explore this issue further, this paper proposes a model based on self-organizing map as well as validates this model by performing experiments on a dataset containing journal entries. Empirical results suggest that the proposed model can detect {}``suspicious'' and legitimate transactions with a high degree of accuracy. Further investigations reveal that the performance of the model is robust to varying prior probabilities of {}``suspicious'' journal entries occurring in the population. The findings indicate that the model can assist auditors in detecting {}``suspicious'' journal entries.