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Multi-Factor Authentication for Secured Financial Transactions Through Spatio-TGCN Model
Conference proceeding

Multi-Factor Authentication for Secured Financial Transactions Through Spatio-TGCN Model

Bhagwan Jagwani, Abbasov Habib Hasan, Kuldeep Agnihotri, Khushbu Jain, Anvesh Perada and D. Gobinath
2025 International Conference on Visual Analytics and Data Visualization (ICVADV), pp 270-275
04 Mar 2025
DOI: https://doi.org/10.1109/ICVADV63329.2025.10961247

Abstract

Accuracy Data models Encoding Feature extraction Financial Transactions Fraud Multi-factor authentication Secure Authentication Sockets Spatio-Temporal Graph Convolutional Networks (Spatio-TGCN) Training Visual analytics White spaces
In the information economy, online financial transactions must be secure. Digital platforms for everyday transactions put financial services customers at danger of fraud and unauthorized access. This research integrated machine learning and multi-factor authentication to secure online financial transactions. System steps include data preparation, feature extraction, and model training. Data preparation includes data normalization, label encoding, white space removal, and socket information removal. Feature extraction strengthens variable association to improve model predictive ability. The suggested model uses spatio- TGCN for training. We found that the proposed model outperforms GCN and CNN. Comparing to other models, the framework's 91.40% accuracy rate suggests it could safeguard online financial transactions. This method may make internet financial transactions safer.

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