Hybrid Detection of Anomalies in Financial Transactions: A Rule-Based and Machine Learning Approach

Alexandra Stavrositu (Caratas), Cristina Barbu (Antohi), Mihaela-Carmen Muntean, Dragos Sebastian Cristea and Daniela Ancuta Sarpe
Additional contact information
Alexandra Stavrositu (Caratas): Dunarea de Jos University of Galați, Romania
Cristina Barbu (Antohi): Dunarea de Jos University of Galați, Romania
Mihaela-Carmen Muntean: Dunarea de Jos University of Galați, Romania
Dragos Sebastian Cristea: Dunarea de Jos University of Galați, Romania
Daniela Ancuta Sarpe: Dunarea de Jos University of Galați, Romania

Economics and Applied Informatics, 2025, issue 3, 162-170

Abstract: This paper presents a hybrid methodology for detecting anomalies in financial transactions including card-initiated transactions and payments by combining rule-based logic with unsupervised machine learning techniques. Rule-based detection leverages expert-defined heuristics to flag transactions exhibiting high-risk behaviors such as card number, BIN, transaction amount, local time, date, expiry, MCC, country code, 3DSecurity Level, time interval, count, amount and location, plus excessive login attempts, abnormal transaction timing, and demographic inconsistencies. In parallel, three unsupervised models—Local Outlier Factor, One-Class SVM, and Autoencoder—are applied to extract structural and statistical anomalies without requiring labeled data. A weighted scoring mechanism aggregates model outputs to rank suspicious transactions, enhancing robustness through model complementarity. The methodology is evaluated on a synthetically enriched transactional dataset, demonstrating its ability to identify both interpretable and latent anomalies. Comparative results highlight the benefits of model diversity and reveal limited but meaningful overlap between rule-based and ML-based detections. The proposed framework offers transparency, flexibility, and practical scalability, making it well-suited for near real-time monitoring systems in the banking sector. Findings underscore the importance of multi-layered detection in modern anti-fraud card and payment management.

Keywords: anomaly detection; financial transactions; rule-based modeling; machine learning; Local Outlier Factor; One-Class SVM; autoencoder; hybrid framework; fraud detection; unsupervised learning; behavioral analytics; transaction monitoring; data-driven risk management; interpretability; outlier analysis (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://eia.feaa.ugal.ro/images/eia/2025_3/Stavrositu_et_al.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ddj:fseeai:y:2025:i:3:p:162-170

DOI: 10.35219/eai15840409561

Access Statistics for this article

More articles in Economics and Applied Informatics from "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration Contact information at EDIRC.
Bibliographic data for series maintained by Gianina Mihai ().