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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: | A COMPARATIVE SUPERVISED MACHINE LEARNING FRAMEWORK FOR CREDIT CARD FRAUD DETECTION ON HIGHLY IMBALANCED TRANSACTION DATA |
| Authors Name: | Megha Baghsawari , Swati Choudhary , Muskan Uday , Twinkal Yadav , Deepali Chourey |
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IJRTI_208874
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| Published Paper Id: | IJRTI2512160 |
| Published In: | Volume 10 Issue 12, December-2025 |
| DOI: | |
| Abstract: | The rapid expansion of digital payment systems has significantly increased the prevalence and sophistication of credit card fraud, posing serious financial risks to consumers and financial institutions. Traditional rule-based fraud detection systems struggle to adapt to evolving fraud patterns and large-scale transaction data. To address these challenges, this study presents a comprehensive comparative analysis of three supervised machine learning models—Logistic Regression, Decision Tree, and Random Forest—for detecting and predicting fraudulent credit card transactions using a highly imbalanced dataset. Data preprocessing techniques, including feature scaling and undersampling, are employed to mitigate bias toward the majority class. Model performance is evaluated using accuracy, precision, recall, F1-score, specificity, and the area under the receiver operating characteristic curve (AUC). Experimental results demonstrate that the Random Forest model outperforms the other classifiers, achieving an accuracy of 96% and an AUC of 98.9%. Additionally, demographic and temporal analyses reveal that cardholders above 60 years are more vulnerable to fraud, with a higher frequency of fraudulent transactions occurring between 22:00 and 04:00 GMT. The findings highlight the effectiveness of ensemble-based learning approaches and provide practical insights for enhancing fraud detection systems in the financial sector. |
| Keywords: | Credit Card Fraud Detection, Supervised Machine Learning, Random Forest, Imbalanced Data, Financial Analytics |
| Cite Article: | "A COMPARATIVE SUPERVISED MACHINE LEARNING FRAMEWORK FOR CREDIT CARD FRAUD DETECTION ON HIGHLY IMBALANCED TRANSACTION DATA", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 12, page no.b522-b530, December-2025, Available :http://www.ijrti.org/papers/IJRTI2512160.pdf |
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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: IJRTI2512160
Registration ID:208874
Published In: Volume 10 Issue 12, December-2025
DOI (Digital Object Identifier):
Page No: b522-b530 Country: Indore, M.P, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2512160 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2512160 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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