A Hybrid FinTech Fraud Detection Model Integrating Multiscale Entropy and Transformer-GAT Techniques

Title

A Hybrid FinTech Fraud Detection Model Integrating Multiscale Entropy and Transformer-GAT Techniques

Creator

Rajagopal, Manikandan; Mathew, Mareena

Description

Credit card fraud has become a major concern in the FinTech industry due to the rapid growth of digital payment platforms and the increasing sophistication of fraudulent activities. Accurate and timely detection of fraud is essential to minimize financial losses and maintain trust in FinTech services. This study presents a hybrid deep learning framework for credit card fraud detection using the 2023 Credit Card Fraud Detection Dataset. The proposed approach with data preprocessing, which includes handling missing values, removing duplicate entries, and encoding categorical features to ensure clean and structured input for modeling. Normalization is applied to scale features uniformly, preventing bias from varying magnitudes and improving model convergence. Multiscale Entropic (MSE) analysis is employed for feature extraction, capturing both short- and long-term temporal patterns within transaction sequences, enhancing the representation of complex transactional behaviors. The extracted features are then processed using a Transformer-GAT classifier, which combines the attention mechanism of Transformers with Graph Attention Networks (GAT) to learn complex inter-transaction dependencies and graph-based relationships. This hybrid architecture enables the model to capture both local and global patterns, improving fraud detection performance. On the training dataset, the model achieved outstanding results with 98.65% accuracy, 98.70% precision, 98.50% recall, and an F1-score of 98.60 %, demonstrating a strong balance between correctly identifying fraudulent transactions and minimizing false alarms. The approach offers significant advantages for FinTech applications, including robust handling of imbalanced data, effective detection of subtle fraud patterns, and strong generalization to unseen transactions. 2025 IEEE.

Source

2025 IEEE 5th International Conference on ICT in Business Industry and Government, ICTBIG 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Credit Card Fraud Detection Dataset 2023; Multiscale Entropic; Normalization; Transformer-GAT

Coverage

Rajagopal M., School of Business Management, Christ University, Banglaore, India; Mathew M., School of Business Management, Christ University, Banglaore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833157981-4;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICTBIG68706.2025.11323750

https://www.scopus.com/pages/publications/105032897163?origin=resultslist