Proceedings of the 2nd International Conference on Innovations and Challenges in Financial Technology (ICICFT 2025)

2nd International Conference on Innovations and Challenges in Financial Technology (ICICFT 2025)

📍Bangalore, India🗓️ 7-8 November 2025
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Ensemble Learning for Fraud Detection in FinTech Transactions: Comparative Analysis of SVM and ANN

Authors
Jyoti Bala Gupta1, *
1Associate Professor, Department of Computer Science and Information Technology, Dr. C.V. Raman University, Kota, Bilaspur, CG, India
*Corresponding author. Email: jyotibalagupta.cvru@gmail.com
Corresponding Author
Jyoti Bala Gupta
Available Online 7 September 2026.
DOI
10.2991/978-94-6239-772-9_26How to use a DOI?
Keywords
Financial Technology (FinTech); SMOTE; Ensemble model
Abstract

This study performs an ensemble method to enhance fraud detection capabilities in Financial Technology (FinTech) systems. Specifically, we develop a hybrid ensemble model that focuses on improving real-time fraud detection in the Financial Technology (FinTech) industry. The proposed system merges three distinct machine learning algorithms: Support Vector Machine (SVM), Artificial Neural Network (ANN), and Decision Tree (DT). Instead of depending on one model, a confidence-based voting method givesfinal prediction. Besides, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to augment the presence of fraud cases during training. The model is tested on Kaggle-credit-card fraud dataset. The outcomes demonstrates that ensemble method surpasses individual models performance, attained a prediction accuracy of 99.35%, a recall of 90.28%, and an AUC score of 0.987. These results suggest the proposed approach could offer a more dependable and feasible way of identifying fraudulent transactions in actual FinTech settings.

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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Volume Title
Proceedings of the 2nd International Conference on Innovations and Challenges in Financial Technology (ICICFT 2025)
Series
Advances in Economics, Business and Management Research
Publication Date
7 September 2026
ISBN
978-94-6239-772-9
ISSN
2352-5428
DOI
10.2991/978-94-6239-772-9_26How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - Jyoti Bala Gupta
PY  - 2026
DA  - 2026/09/07
TI  - Ensemble Learning for Fraud Detection in FinTech Transactions: Comparative Analysis of SVM and ANN
BT  - Proceedings of the 2nd International Conference on Innovations and Challenges in Financial Technology (ICICFT 2025)
PB  - Atlantis Press
SP  - 340
EP  - 355
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-772-9_26
DO  - 10.2991/978-94-6239-772-9_26
ID  - Gupta2026
ER  -