  In a context of a continuous digitalisation of processes, organisations must
deal with the challenge of detecting anomalies that can reveal suspicious
activities upon an increasing volume of data. To pursue this goal, audit
engagements are carried out regularly, and internal auditors and purchase
specialists are constantly looking for new methods to automate these processes.
This work proposes a methodology to prioritise the investigation of the cases
detected in two large purchase datasets from real data. The goal is to
contribute to the effectiveness of the companies' control efforts and to
increase the performance of carrying out such tasks. A comprehensive
Exploratory Data Analysis is carried out before using unsupervised Machine
Learning techniques addressed to detect anomalies. A univariate approach has
been applied through the z-Score index and the DBSCAN algorithm, while a
multivariate analysis is implemented with the k-Means and Isolation Forest
algorithms, and the Silhouette index, resulting in each method having a
transaction candidates' proposal to be reviewed. An ensemble prioritisation of
the candidates is provided jointly with a proposal of explicability methods
(LIME, Shapley, SHAP) to help the company specialists in their understanding.
