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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 117
Article Submitted : 21318
Article Published : 8476
Total Authors : 22301
Total Reviewer : 802
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| Paper Title: | Credit Card Fraud Detection Using Machine Learning |
| Authors Name: | Prof.Onkar M. Patil , Prof.Dr.Ashwini A. Patil , Swami Shruti Shivappa , Waghmare Supriya Sanjiv |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_208866
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| Published Paper Id: | IJRTI2512154 |
| Published In: | Volume 10 Issue 12, December-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i12.208866 |
| Abstract: | The rapid growth of digital payment systems has significantly increased the risk of credit card fraud, resulting in substantial financial losses for banks and customers. Traditional rule-based fraud detection systems often fail to identify complex and evolving fraudulent patterns in real time. This research aims to develop an efficient credit card fraud detection system using machine learning techniques. A publicly available Kaggle dataset containing anonymized credit card transaction records was used for experimentation. Data preprocessing involved handling missing values, feature scaling, train-test splitting, and addressing class imbalance using the Synthetic Minority Oversampling Technique (SMOTE). Several machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, and XGBoost, were implemented and evaluated using performance metrics such as accuracy, precision, recall, F1-score, and confusion matrix. Experimental results demonstrate that ensemble models, particularly Random Forest and XGBoost, achieve higher accuracy and recall in detecting fraudulent transactions. The findings highlight the importance of handling imbalanced datasets and selecting robust algorithms for fraud detection. This study concludes that machine learning-based approaches can significantly enhance real-time credit card fraud detection, reduce financial losses, and improve trust in digital financial systems. |
| Keywords: | Credit Card Fraud Detection, Machine Learning, Classification, Imbalanced Dataset, Financial Security.. |
| Cite Article: | "Credit Card Fraud Detection Using Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 12, page no.b474-b476, December-2025, Available :http://www.ijrti.org/papers/IJRTI2512154.pdf |
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| ISSN: |
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: IJRTI2512154
Registration ID:208866
Published In: Volume 10 Issue 12, December-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i12.208866
Page No: b474-b476 Country: Latur, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2512154 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2512154 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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