ItemConference Paper

SMOTE-Enhanced Machine Learning Techniques for Credit Card Fraud Detection

Title
SMOTE-Enhanced Machine Learning Techniques for Credit Card Fraud Detection
Creator
Prakasan, Adwaidh; Chandana, B.; Kokatnoor, Sujatha Arun; Mandala, Jyothi; Kumar, Sandeep
Description
In today's digital world, most daily money transactions are done virtually through online systems. The rise in credit card transactions has increased the number of fraudulent transactions, leading to significant financial losses. Currently, the main problem faced during the analysis of transactions is the imbalance in the dataset. To address the issue of data imbalance and identify good models for accurately detecting fraudulent transactions, this paper presents a comparative study to determine the suitable machine learning algorithms for credit fraud detection. In this research study, Synthetic Minority Oversampling Technique (SMOTE) processing is done to balance the dataset, and various machine learning classifiers, Logistic Regression, Naive Bayes, K-Nearest Neighbor (KNN), Decision Trees, and Support Vector Machine (SVM) are compared and analyzed. During the experimental process, it was observed that after SMOTE was enhanced, SVM outperformed other models with an accuracy of 98.9%. When there are numerous features (variables) in the data, as is often the case in credit card transactions when several elements are taken into account, SVM can perform well. SVM differentiated outliers because of its margin-maximizing characteristics, which assisted in separating the fraudulent class from the non-fraudulent class. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Lecture Notes in Networks and Systems;Volume;1237;pp.1-12
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Decision trees; Imbalanced dataset and credit card frauds; K-near neighbor (KNN); Logistic regression; Nae Bayes; SMOTE; Support vector machine (SVM)
Coverage
Prakasan A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, 560074, India; Chandana B., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, 560074, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, 560074, India; Mandala J., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, 560074, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, 560074, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981961184-3;
Format
online
Language
English
Type
Conference paper

Citation

Prakasan, Adwaidh; Chandana, B.; Kokatnoor, Sujatha Arun; Mandala, Jyothi; Kumar, Sandeep, “SMOTE-Enhanced Machine Learning Techniques for Credit Card Fraud Detection,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25463.

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