How to Incorporate Audit Information into the Statistical Model: Bayesian Generalized Linear Modeling for Audit Sampling

The audit environment of today offers a wealth of information in the form of data. Consequently, data about the auditee is expected to guide and improve auditors’ approach to tests of details. However, to be able to make optimal use of this data, auditors must have tools that facilitate the effective and efficient use of quantitative information throughout an audit. In this article, we introduce Bayesian generalized linear modeling as a statistical framework to incorporate this information into tests of details, thereby enabling auditors to deliver a fine-grained and specifically tailored audit opinion to stakeholders. We begin with an introduction of Bayesian inference in audit sampling, then explain the main concepts underpinning Bayesian generalized linear modeling and show how this approach allows auditors to bridge the gap between analytics on integrally available data and analytics on data that is available on a sample basis, making optimal use of their information.