Abstract
The frequent use of digital technology has significantly increased the use of online transactions. Most financial activities are conducted electronically. Financial fraud involves deceiving individuals or businesses for monetary gain. The proposed graph node embedding technique, FA-Struc2vec, is applied to the financial transaction network to extract graph node features based on their structural similarity and generate node embeddings. The node embeddings serve as input to DeepFinSec, which classifies transactions into desired classes. DeepFinSec is a proposed DL-based model that is a hybrid of CNN and LSTM. CNN processes transaction data and extracts local and high-level abstract features while LSTM captures sequential and temporal dependencies, making this a powerful combination for transaction pattern analysis. DeepFinSec outperformed existing ML and DL techniques, achieving 97.70% accuracy, 2.29% higher than the second-highest-accuracy technique, LSTM. 95.61% precision, 100% recall, 97.75% F1 score, and 99.68% AUC.
Keywords
Deep learning, Financial fraud detection, Graph embedding, Online fraud detection, Structural similarity
Subject Area
Computer Science
Article Type
Article
First Page
3377
Last Page
3392
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite this Article
Sharma, Diksha; Mathur, Robin Prakash; and Sharma, Manmohan
(2026)
"DeepFinSec: Advancing Online Financial Fraud Detection via Structural Similarity-Based Graph Embedding,"
Baghdad Science Journal: Vol. 23:
Iss.
9, Article 23.
DOI: https://doi.org/10.21123/2411-7986.5417
