Applied Machine Learning to Anomaly Detection in Enterprise Purchase Processes: A Hybrid Approach Using Clustering and Isolation Forest Article Swipe
Antonio Herreros-Martínez
,
Rafael Magdalena‐Benedito
,
Joan Vila‐Francés
,
Antonio J. Serrano-López
,
Sonia Pérez-Dı́az
,
José-Javier Martínez-Herráiz
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/info16030177
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/info16030177
In the era of increasing digitalisation, organisations face the critical challenge of detecting anomalies in large volumes of data, which may indicate suspicious activities. To address this challenge, audit engagements are conducted regularly, and internal auditors and purchasing specialists seek innovative methods to streamline these processes. This study introduces a methodology to prioritise the investigation of anomalies identified in two large real-world purchase datasets. The primary objective is to enhance the effectiveness of companies’ control efforts and improve the efficiency of anomaly detection tasks. The approach begins with a comprehensive exploratory data analysis, followed by the application of unsupervised machine learning techniques to identify anomalies. A univariate analysis is performed using the z-Score index and the DBSCAN algorithm, while multivariate analysis employs k-Means clustering and Isolation Forest algorithms. Additionally, the Silhouette index is used to evaluate the quality of the clustering, ensuring each method produces a prioritised list of candidate transactions for further review. To refine this process, an ensemble prioritisation framework is developed, integrating multiple methods. Furthermore, explainability tools such as SHAP are utilised to provide actionable insights and support specialists in interpreting the results. This methodology aims to empower organisations to detect anomalies more effectively and streamline the audit process.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/info16030177
- https://www.mdpi.com/2078-2489/16/3/177/pdf?version=1740574142
- OA Status
- gold
- Cited By
- 5
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407965775
All OpenAlex metadata
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- Title
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Applied Machine Learning to Anomaly Detection in Enterprise Purchase Processes: A Hybrid Approach Using Clustering and Isolation ForestWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-26Full publication date if available
- Authors
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Antonio Herreros-Martínez, Rafael Magdalena‐Benedito, Joan Vila‐Francés, Antonio J. Serrano-López, Sonia Pérez-Dı́az, José-Javier Martínez-HerráizList of authors in order
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https://doi.org/10.3390/info16030177Publisher landing page
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https://www.mdpi.com/2078-2489/16/3/177/pdf?version=1740574142Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2078-2489/16/3/177/pdf?version=1740574142Direct OA link when available
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Cluster analysis, Isolation (microbiology), Anomaly detection, Computer science, Artificial intelligence, Machine learning, Data mining, Biology, MicrobiologyTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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