Interpretable Contrastive Learning for Robust and Explainable Anomaly Detection in Financial and Organizational Data.
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| Title: | Interpretable Contrastive Learning for Robust and Explainable Anomaly Detection in Financial and Organizational Data. |
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| Authors: | Zheng, Jianti1 (AUTHOR) zhengjianti@163.com, Qiu, Wenyan1 (AUTHOR) HQKEira@163.com, Huang, Shiwang2 (AUTHOR) huangshiwang8888@163.com |
| Source: | International Journal of Pattern Recognition & Artificial Intelligence. Jan2026, Vol. 40 Issue 1, p1-22. 22p. |
| Subjects: | Anomaly detection (Computer security), Financial databases, Robust statistics, Metadata, Machine learning |
| Abstract: | Anomaly detection is a fundamental task in modern intelligent systems, yet achieving both robustness and interpretability remains a persistent challenge, especially when labeled data are scarce. Recent advances in contrastive learning have enabled effective representation learning from unlabeled data, but most existing models offer limited insight into the feature-level factors underlying their predictions. In this paper, we propose an interpretable contrastive learning framework for robust and explainable anomaly detection. By integrating a learnable feature attribution mask at the input level directly into the contrastive objective, our approach not only enhances detection accuracy but also provides faithful, sparse attributions for each detected anomaly. Unlike prior interpretable anomaly detection methods, our framework jointly optimizes representation learning and attribution faithfulness, offering both theoretical generalization guarantees and practical scalability for real-world deployment. We conduct comprehensive experiments on four benchmark datasets spanning network intrusion detection, organizational communication analysis, financial fraud identification, and synthetic banking scenarios, and compare our method to both classical and state-of-the-art baselines. The results demonstrate consistent improvements, with our approach achieving up to 4–7% higher AUC than recent deep learning baselines, along with high stability of explanations and clear visual separation of anomalies in the learned embedding space. Beyond these quantitative gains, our work underscores the importance of actionable interpretability, providing a principled and deployable solution for anomaly detection in high-stakes domains such as finance, IoT, and cyber-security. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 190223830 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Interpretable Contrastive Learning for Robust and Explainable Anomaly Detection in Financial and Organizational Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zheng%2C+Jianti%22">Zheng, Jianti</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhengjianti@163.com</i><br /><searchLink fieldCode="AR" term="%22Qiu%2C+Wenyan%22">Qiu, Wenyan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> HQKEira@163.com</i><br /><searchLink fieldCode="AR" term="%22Huang%2C+Shiwang%22">Huang, Shiwang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> huangshiwang8888@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Pattern+Recognition+%26+Artificial+Intelligence%22">International Journal of Pattern Recognition & Artificial Intelligence</searchLink>. Jan2026, Vol. 40 Issue 1, p1-22. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Anomaly+detection+%28Computer+security%29%22">Anomaly detection (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Financial+databases%22">Financial databases</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+statistics%22">Robust statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Metadata%22">Metadata</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Anomaly detection is a fundamental task in modern intelligent systems, yet achieving both robustness and interpretability remains a persistent challenge, especially when labeled data are scarce. Recent advances in contrastive learning have enabled effective representation learning from unlabeled data, but most existing models offer limited insight into the feature-level factors underlying their predictions. In this paper, we propose an interpretable contrastive learning framework for robust and explainable anomaly detection. By integrating a learnable feature attribution mask at the input level directly into the contrastive objective, our approach not only enhances detection accuracy but also provides faithful, sparse attributions for each detected anomaly. Unlike prior interpretable anomaly detection methods, our framework jointly optimizes representation learning and attribution faithfulness, offering both theoretical generalization guarantees and practical scalability for real-world deployment. We conduct comprehensive experiments on four benchmark datasets spanning network intrusion detection, organizational communication analysis, financial fraud identification, and synthetic banking scenarios, and compare our method to both classical and state-of-the-art baselines. The results demonstrate consistent improvements, with our approach achieving up to 4–7% higher AUC than recent deep learning baselines, along with high stability of explanations and clear visual separation of anomalies in the learned embedding space. Beyond these quantitative gains, our work underscores the importance of actionable interpretability, providing a principled and deployable solution for anomaly detection in high-stakes domains such as finance, IoT, and cyber-security. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0218001425510292 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1 Subjects: – SubjectFull: Anomaly detection (Computer security) Type: general – SubjectFull: Financial databases Type: general – SubjectFull: Robust statistics Type: general – SubjectFull: Metadata Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Interpretable Contrastive Learning for Robust and Explainable Anomaly Detection in Financial and Organizational Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zheng, Jianti – PersonEntity: Name: NameFull: Qiu, Wenyan – PersonEntity: Name: NameFull: Huang, Shiwang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02180014 Numbering: – Type: volume Value: 40 – Type: issue Value: 1 Titles: – TitleFull: International Journal of Pattern Recognition & Artificial Intelligence Type: main |
| ResultId | 1 |
