Application of Machine Learning in Financial Fraud Detection and Prevention

A Comparative Analysis of Algorithms

DOI

10.2991/978-94-6239-658-6_2How to use a DOI?

Keywords

Machine learning; Credit card fraud; Fraud prevention; Logistic regression

Abstract

Detecting fraudulent activities in the financial sector is a critical challenge that requires robust, adaptive approaches. This paper investigates the application of machine learning (ML) algorithms—Logistic Regression, SVM, KNN, Decision Trees and Random Forests—for credit card fraud detection. Utilizing a highly imbalanced dataset, models were evaluated using precision, recall, and F2 score, prioritizing recall to minimize undetected fraud. Our findings demonstrate that Logistic Regression achieved the highest recall (91%), effectively identifying the majority of fraudulent transactions while maintaining a low false-negative rate. SVMs achieved balanced performance with 89% recall, while Random Forests showed superior precision (98%), minimizing false alarms. These results highlight the strengths and trade-offs of ML algorithms for uncovering complex patterns in large-scale financial data and for reducing fraud risk when integrated with real-time detection systems. This research underscores the importance of continuous model optimization using updated data and advanced techniques to counter evolving fraud tactics. By bridging technological innovation with proactive fraud prevention, this paper provides actionable insights for financial institutions, contributing to the development of secure and resilient financial ecosystems.

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  - Adaleta Hasanović
AU  - Savo Stupar
AU  - Kemal Kačapor
AU  - Nijaz Bajgorić
PY  - 2026
DA  - 2026/05/01
TI  - Application of Machine Learning in Financial Fraud Detection and Prevention
BT  - Proceedings of the Kautz Conference on Business and Economics 2025 (KCBE 2025)
PB  - Atlantis Press
SP  - 9
EP  - 30
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-658-6_2
DO  - 10.2991/978-94-6239-658-6_2
ID  - Hasanović2026
ER  -