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Detection of Credit Card Fraud in Financial Transactions Using Machine Learning Algorithms

Authors:
Keerthana Muthu, Selvi Perumal, Gokulakrishnan Gopalan, Gayathiri Gurunathar, S. Namachivayam, Maruf Farhan

Addresses:
Department of Computer Science and Engineering, Jayalakshmi Institute of Technology, Dharmapuri, Tamil Nadu, India. Department of Information Technology, Jayalakshmi Institute of Technology, Dharmapuri, Tamil Nadu, India. Department of Computer Science and Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. Department of STEM, University of Sussex International Study Centre, Brighton, England, United Kingdom.

Abstract:

This study will explore the effectiveness of computational intelligence in detecting fraud in financial transactions within a digital ecosystem. The study estimates how various classification models can distinguish between legitimate activity and illegal intrusion using a curated dataset of 368 transaction instances. The Python programming environment is the most widely used tool in this study, with the assistance of specialised libraries for data and statistical learning. The approach will focus on feature extraction and the application of supervised learning techniques to address the issue of skewed data distributions common in financial records. The trend analysis of transaction volume and time frequency indicates that both can be significantly reduced with algorithmic intervention to mitigate the risk of monetary loss. The results indicate that some models are highly effective at identifying small anomalies that human operators would not otherwise detect. The paper provides a paradigm for adopting computerised security procedures in the banking sector to enhance the safety and consumer trust of electronic payment systems.

Keywords: Transaction Security; Anomaly Detection; Financial Intelligence; Computational Modelling; Supervised Learning; Financial Transactions; Electronic Payment Systems; Human Operation.

Received on: 27/08/2025, Revised on: 18/10/2025, Accepted on: 19/12/2025, Published on: 09/06/2026

DOI: 10.69888/FTSFDS.2026.000698

FMDB Transactions on Sustainable Finance and Data Science, 2026 Vol. 1 No. 2, Pages: 108-116

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