While a wealth of statutory and auditing pronouncements attest to the importance of the auditing of journal entries for preventing and detecting material misstatements to financial statements, existing literature has so far paid inadequate attention to this line of research. To explore this line of research further, this paper proposes a bipartite model that is based on extreme value theory and Bayesian analysis of Poisson distributions. The paper assesses the veracity of the model via a series of experiments on a dataset that contains the journal entries of an international shipping company for fiscal years 2006 and 2007. Empirical results suggest the model can detect journal entries that have a low probability of occurring and a monetary amount large enough to cause financial statements to be materially misstated. Further investigations reveal that the model can assist auditors to form expectations about the journal entries thus detected as well as update their expectations based on new data. The findings indicate that the model can be applied for the auditing of journal entries, and thus supplement existing procedures.