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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 119
Article Submitted : 23478
Article Published : 9055
Total Authors : 24029
Total Reviewer : 831
Total Countries : 162
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| Paper Title: | Credit Card Fraud Detection Using machine Learning |
| Authors Name: | Shweta Sandip Patil , Sanjana Laxne , Gayatri Bhendarkar , Ruchita yeyyawar , Achal Shende |
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IJRTI_210448
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| Published Paper Id: | IJRTI2603123 |
| Published In: | Volume 11 Issue 3, March-2026 |
| DOI: | |
| Abstract: | Credit card fraud has become one of the most critical issues in the financial sector due to the increasing volume of digital transactions. Fraudulent activities not only lead to major monetary losses for banks and customers but also reduce trust in online payment systems. This project presents a Credit Card Fraud Detection System that uses machine learning algorithms to identify fraudulent transactions. The dataset used in this project work is highly imbalanced, with very few fraudulent cases compared to genuine ones. To address this challenge, data preprocessing techniques such as resampling, feature scaling, and normalization Itre applied. Several machine learning models, including Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting, Performance was measured using precision, recall, F1score, and AUCROC curve. The experimental results show that ensemble models provide better performance and higher fraud detection rates than traditional models. The proposed system achieves high accuracy and precision, reducing false positives and ensuring secure financial transactions. It highlights the potential of machine learning in strengthening fraud prevention mechanisms and can be applied to real-world banking systems for enhanced security |
| Keywords: | Credit Card Fraud, Fraud Detection, Machine Learning, Data Imbalance, Classification, Financial Security, Anomaly Detection. |
| Cite Article: | "Credit Card Fraud Detection Using machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.b167-b196, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603123.pdf |
| Downloads: | 00091 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2603123
Registration ID:210448
Published In: Volume 11 Issue 3, March-2026
DOI (Digital Object Identifier):
Page No: b167-b196 Country: ramtek, maharashtra, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2603123 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2603123 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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