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.

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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.