Priors in a Bayesian audit: How integration of existing information into the prior distribution can improve audit transparency and efficiency

Auditors often have prior information about the auditee before starting the substantive testing phase. We show that applying Bayesian statistics in substantive testing allows for integration of this information into the statistical analysis through the prior distribution. For example, an auditor might have performed an audit last year, they might have information on certain controls in place, or they might have performed analytical procedures in an earlier stage of the audit. Incorporating this information directly in the statistical procedure enables auditors to tailor their sampling plan to the auditee, thereby increasing audit transparency and efficiency. However, defining a suitable prior distribution can be difficult because what constitutes a suitable prior depends on the specifics of the audit and the auditee. To help the auditor in constructing a prior distribution we introduce five methodologies, consider their pros and cons, and give examples of how to apply them in practice.