Enhanced Integrated Model for Financial Fraud Detection Using Graph Machine Learning

DOI

10.2991/978-94-6239-693-7_115How to use a DOI?

Keywords

Graph-Based Fraud Detection; Financial Transaction Networks; Graph Machine Learning; Data Imbalance Handling

Abstract

Fraud in Financial domain bearings a major hazard to financial systems of modern era, causing in significant commercial losses and lessen the trust among the stakeholders. Conventional fraud detection techniques based on traditional algorithms of machine learning habitually fail to identify the compound relational patterns present in large-scale financial transaction data. In order to deal this limitation, this study proposes an intelligent graph-based machine learning framework for financial fraud detection. By modeling financial entities and their interactions as graphs, the proposed approach leverages Graph Machine Learning (GML) techniques to uncover hidden relationships, structural patterns, and anomalous behaviors associated with fraudulent activities. Furthermore, the impact of data imbalance—a common challenge in fraud datasets—is analyzed, and appropriate balancing strategies are applied to enhance detection performance. Experimental results exhibit that the graph-based approach significantly outstrips the conventional methods in identifying fraudulent transactions, highlighting its effectiveness in improving accuracy, robustness, and reliability in real-world financial fraud detection systems.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - S. Babu
AU  - V. Rama Narayanan
AU  - T. Nirmal Raj
AU  - J. Srinivasan
PY  - 2026
DA  - 2026/06/16
TI  - Enhanced Integrated Model for Financial Fraud Detection Using Graph Machine Learning
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 1204
EP  - 1213
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_115
DO  - 10.2991/978-94-6239-693-7_115
ID  - Babu2026
ER  -