Applied Machine Learning to Anomaly Detection in Enterprise Purchase Processes

TL;DR

This paper presents a machine learning-based methodology for anomaly detection in enterprise purchase data, combining unsupervised techniques and explainability tools to improve audit efficiency and detection accuracy.

Contribution

It introduces a comprehensive approach integrating multiple unsupervised algorithms and explainability methods for anomaly detection in purchase processes.

Findings

Effective identification of suspicious purchase transactions.

Enhanced interpretability of anomaly detection results.

Improved prioritization of audit cases.

Abstract

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.