Securing Authentication and Fraud Detection in Financial Systems Using Machine Learning

DOI

10.2991/978-94-6239-664-7_69How to use a DOI?

Keywords

Fraud Detection; Secure Authentication; Deep Learning; Parallel Model; Machine Learning Algorithms; XGBoost; Data Imbalance; Financial Security

Abstract

As we are moving to online financial applications, it becomes necessary to have fraud detection and authentication tools. In general, the traditional methods are unable to fight against growing advanced fraud attacks and lead to greater losses in the economic industry. To the best of our knowledge, this is the first work to jointly tackle fraud detection and secure authentication using a parallel deep learning model, harnessing two strengths rather than focusing on one and replacing the other. We leverage a few machine learning algorithms, including Logistic Regression, Random Forest, and XGBoost, to work with a parallel deep learning method. MaxScore XGBoost model gives the best performance by 82% accuracy, yet the advancement using deep learning for detection. This approach has a significant impact on the daily life of citizens as it reinforces safe online transactions, reduces fraud risks and is conducive to building trust in digital channels. Last but not least, the parallel DL model proposed in this study is an efficient application to deal with financial fraud detection and authentication, whose future work lies in two aspects: to optimise the general DL model, as well as handle imbalanced data and fraud pattern changes.

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  - Samia Hasan Suha
AU  - Sufia Zareen
AU  - Md Reduanur Rahman
AU  - Md Abdul Alim
AU  - Nasrin Akter Tohfa
AU  - Md Shakhawat Hossen
PY  - 2026
DA  - 2026/06/08
TI  - Securing Authentication and Fraud Detection in Financial Systems Using Machine Learning
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 1009
EP  - 1023
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_69
DO  - 10.2991/978-94-6239-664-7_69
ID  - Suha2026
ER  -