Bayesian benefits for auditing

A proposal to innovate audit methodology

About the Project

I am a PhD Candidate at Nyenrode Business University in collaboration with the University of Amsterdam. My project aims to develop new efficient Bayesian methods for statistical auditing. The project is based on two pillars:

Pillar 1: Improving availability of modern statistical auditing techniques by programming these methods into the free, user-friendly and open-source software packages JASP for Audit and jfa (R).

Pillar 2: Developing Bayesian alternatives to current statistical methods that are specifically suited to perform audit work, by combining various sources of data such that the amount of data that is needed to get to the desired level of audit risk is minimized.

    JASP for Audit (JfA)

    JASP for Audit is open-source and free-of-charge software built to support the statistical aspects of audit sampling. The core functionality of the software is implemented in an R package (jfa) accessible through the official CRAN repository. JASP for Audit provides a point-and-click interface designed with the auditor in mind. JfA's audit sampling workflow is intuitive to follow:

  • Planning

    JASP for Audit allows the auditor to calculate the required sample size for their audit objectives. Bayesian methods make it possible to optimally quantify the auditors prior knowledge for increased efficiency.

  • Selection

    JfA supports the auditor by guiding them through the selection process. Appropriate sampling methods are selected automatically by the software.

  • Execution

    JfA helps the auditor in determining how to execute their audit, and provides a user-friendly way to add these annotations.

  • Evaluation

    JfA applies the correct scientifically-backed statistical techniques to provide a statement about the fairness of the population, both in Classical and Bayesian manifestations. Upon completion of an audit, JfA produces a full report with the interpretation of the results.


  • Nyenrode Business University is the driving force behind this project, providing me with many opportunities to develop and share my work while being among experienced people from the field.

  • The Psychological Methods department of the University of Amsterdam is invaluable in the realization of this project. Their knowledge of Bayesian statistics is the foundation of many developed techniques.

  • A close cooperation with JASP is required to create the most optimal product. This collaboration brings exiting opportunities for implementing my software.

  • PricewaterhouseCoopers has an advisory role in the project, providing insights and feedback from the practical audit field.