Journal's Facebook pageA Knowledge Graph based Approach for Credit Card Fraud Detection
- University of Macedonia, Thessaloniki, Greece
{dai18069,gkoloniari}@uom.edu.gr
Abstract
Credit Card Fraud Detection remains an evolving and difficult challenge due to its complexity, class imbalance, and scale of transactional data. In this work, we propose a novel graph-based approach that constructs a Knowledge Graph (KG) from transactional data to model relationships between different entities. We calculate a variety of centrality measures, both in an unweighted and in a weighted KG, where edge weights represent transaction-specific features like amount, in order to capture structural importance. These centrality measures are then used to enrich the feature space for multiple Machine Learning (ML) models. Our experimental evaluation assesses the performance of the proposed approach with respect to both accuracy and efficiency. Various experiments are conducted, comparing multiple centrality measures, feature combinations and resampling, showcasing how the addition of the centrality measures as classification features significantly improves the performance of our classification models compared to relying only on the original transactional attributes.
Key words
Knowledge Graphs, Centrality Measures, Machine Learning, Weighted Graph, Credit Card Fraud Detection
Digital Object Identifier (DOI)
https://doi.org/10.2298/CSIS250405084D
Publication information
Volume 23, Issue 1 (January 2026)
Year of Publication: 2026
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium
Full text
Available in PDF
Portable Document Format
How to cite
Dimou, G. K., Koloniari, G.: A Knowledge Graph based Approach for Credit Card Fraud Detection. Computer Science and Information Systems, Vol. 23, No. 1, 657-685. (2026), https://doi.org/10.2298/CSIS250405084D
