Bayesian probabilistic models for corporate context, with an application to internal audit activities

Abstract

In this article, we present a business case carried out in Poste Italiane, in the context of fair performance evaluations of human resources engaged in internal audit activities. In addition to the development of a Bayesian network supporting the goal of the Internal Audit unit of Poste Italiane, the work has led to the development of a methodological approach to advanced analytics in corporate context, whose usefulness goes well beyond the specific use case described here. We thus present the different stages of such analytical strategy, from feature selection, to model structure inference and model selection, as a general toolbox that allows a completely transparent and explainable process to support data-driven decisions in business environments.

KEYWORDS:

• Bayesian statistics
• Bayesian networks
• applications to industry

Acknowledgements

The authors thank to Prof. Julia Mortera for the fruitful discussions during the preparation of the paper. They also thank to the anonymous referee for various suggestions which helped to improve the final revision of the article.

Notes

1 Sometimes $\text{MI}\prime$ is called symmetric uncertainty, or $\text{SU}$. By analogy, we could call $\text{CMI}\prime$ conditional symmetric uncertainty.