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.

在不断数字化的过程中，组织必须应对检测异常情况的挑战，以揭示日益增长的数据中的可疑活动。为了追求这个目标，定期进行审计，内部审计员和采购专家不断寻找自动化这些过程的新方法。本研究提出了一种方法来优先处理从真实数据中检测到的两个大型采购数据集的案例调查，旨在增加公司控制工作的效力和提高执行此类任务的绩效。在使用无监督的机器学习技术检测异常之前，进行全面的探索性数据分析。通过 z-Score 指数和 DBSCAN 算法，应用单变量方法进行处理，而通过 k-Means 和 Isolation Forest 算法以及 Silhouette 指数，实施多变量分析，从而每种方法都提出了待审查的交易候选人。还提供了候选人的集成排序以及 LIME、Shapley 和 SHAP 等解释方法的建议，以帮助公司专家的理解。