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INSPIRA-JOURNAL OF COMMERCE,ECONOMICS & COMPUTER SCIENCE(JCECS) [ Vol. 12 | No. 3 | July - September, 2026 ]

A Comparative Analysis of Machine Learning Classifiers for Detection of Credit Card Fraud

Surbhi Bansal, Dr. Reena Hooda & Rohit Yadav

Now the days world become digital, credit card customers have become common; it makes the payment hassle-free. With the ease of use of credit cards; Fraudulent use of credit cards is growing as a significant affair for financial institutions and consumers on an international level. Traditional rule-based detection algorithms are ineffective in determining a transaction's fraudulent nature. First and foremost, it is imperative to comprehend the pattern of fraudulent activities. The current study explores various supervised machine-learning algorithms to analyze patterns and predict the fraudulent nature of transactions in a large dataset used for training the model. The effectiveness of different methods is assessed by comparing their accuracy, precision, F1score, and recall. In the present paper, we discuss the techniques named KNN, SVM(Support Vector Machine), Logistic Regression, Gradient Boosting, Neural Network, XG Boost, Naïve Bayes, Ada Boost, Decision Forest, and Random Forest.

  1. NerdWallet [Internet]. 2024 [cited 2024 Jul 20]. Credit Card Data, Statistics and Research. Available from: https://www.nerdwallet.com/article/credit-cards/credit-card-data
  2. Das P. EdexLive [Internet]. 2021 [cited 2024 Jul 20]. NCRB Report 2020: Debit, credit card fraud online climbs steeply, 225% more cases when compared to 2019. Available from: https://www.edexlive.com/news/2021/Sep/16/ncrb-report-2020-debit-credit-card-fraud-online-climbs-steeply-225-more-cases-when-compared-to-2-24053.html
  3. Prajapati D, Tripathi A, Mehta J, Jhaveri K, Kelkar V. Credit Card Fraud Detection Using Machine Learning. In: 2021 International Conference on Advances in Computing, Communication, and Control (ICAC3) [Internet]. 2021 [cited 2024 Jul 15]. p. 1–6. Available from: https://ieeexplore.ieee.org/document/9697227 doi:10.1109/ICAC353642.2021.9697227
  4. Singh A, Jain A. An Empirical Study of AML Approach for Credit Card Fraud Detection–Financial Transactions. Int J Comput Commun CONTROL. 2020 Feb 2;14(6):670. doi:10.15837/ijccc.2019.6.3498
  5. Rtayli N, Enneya N. Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization. J Inf Secur Appl. 2020 Dec 1;55:102596. doi:10.1016/j.jisa.2020.102596
  6. Rathi P, Singh N. A NOVEL APPROACH TO DETECTION OF EMERGING FRAUD USING MINING TECHNIQUES. ICTACT J SOFT Comput. 2020;11(01).
  7. Awoyemi J, Adetunmbi A, Oluwadare S. Credit card fraud detection using machine learning techniques: A comparative analysis. In. 2017. p. 1–9. doi:10.1109/ICCNI.2017.8123782
  8. Bagga S, Goyal A, Gupta N, Goyal A. Credit Card Fraud Detection using Pipeling and Ensemble Learning. Procedia Comput Sci. 2020 Jan 1;173:104–12. doi:10.1016/j.procs.2020.06.014
  9. Rb A, Kr SK. Credit card fraud detection using artificial neural network. Glob Transit Proc. 2021 Jun 1;1st International Conference on Advances in Information, Computing and Trends in Data Engineering (AICDE - 2020)2(1):35–41. doi:10.1016/j.gltp.2021.01.006
  10. Alharbi A, Alshammari M, Okon OD, Alabrah A, Rauf HT, Alyami H, et al. A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach. Electronics. 2022 Jan;11(5):5. doi:10.3390/electronics11050756
  11. Mohsen O, Nasserddine G, Massoud M. Credit Card Fraud Detector Based on Machine Learning Techniques. J Comput Sci Technol Stud. 2023 Jun 30;5. doi:10.32996/jcsts.2023.5.2.2
  12. V. D, R. D. BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES. ICTACT J Soft Comput. 2012 Jul 1;02(04):391–7. doi:10.21917/ijsc.2012.0061
  13. Singh A, Jain A, Biable SE. Financial Fraud Detection Approach Based on Firefly Optimization Algorithm and Support Vector Machine. Appl Comput Intell Soft Comput. 2022;2022(1):1468015. doi:10.1155/2022/1468015
  14. Kennedy RKL, Villanustre F, Khoshgoftaar TM, Salekshahrezaee Z. Synthesizing class labels for highly imbalanced credit card fraud detection data. J Big Data. 2024 Mar 9;11(1):38. doi:10.1186/s40537-024-00897-7
  15. Mienye ID, Sun Y. A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection. IEEE Access. 2023;11:30628–38. doi:10.1109/ACCESS.2023.3262020
  16. Ileberi E, Sun Y, Wang Z. A machine learning based credit card fraud detection using the GA algorithm for feature selection | Journal of Big Data | Full Text [Internet]. [cited 2024 Jul 15]. Available from: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-022-00573-8
  17. Ileberi E, Sun Y, Wang Z. A machine learning based credit card fraud detection using the GA algorithm for feature selection. J Big Data. 2022 Dec;9(1):24. doi:10.1186/s40537-022-00573-8
  18. Ndama O, Bensassi I, En-Naimi EM. Optimizing credit card fraud detection: a deep learning approach to imbalanced datasets. Int J Electr Comput Eng IJECE. 2024 Aug 1;14(4):4802. doi:10.11591/ijece.v14i4.pp4802-4814
  19. Alarfaj FK, Malik I, Khan HU, Almusallam N, Ramzan M, Ahmed M. Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms. IEEE Access. 2022;10:39700–15. doi:10.1109/ACCESS.2022.3166891
  20. Husejinovic A. Credit card fraud detection using naive Bayesian and C4.5 decision tree classifiers. Vol. 8. 2020 Jan 1;8:1–5. doi:10.21533/pen.v%25vi%25i.300
  21. Gupta A, Lohani MC, Manchanda M. Financial fraud detection using naive bayes algorithm in highly imbalance data set. J Discrete Math Sci Cryptogr [Internet]. 2021 Jul 4 [cited 2024 Jul 15]. Located at: world. Available from: https://www.tandfonline.com/doi/abs/10.1080/09720529.2021.1969733
  22. Gedela B, Karthikeyan PR. Credit Card Fraud Detection using AdaBoost Algorithm in Comparison with Various Machine Learning Algorithms to Measure Accuracy, Sensitivity, Specificity, Precision and F-score. In: 2022 International Conference on Business Analytics for Technology and Security (ICBATS) [Internet]. 2022 [cited 2024 Jul 30]. p. 1–6. Available from: https://ieeexplore.ieee.org/document/9759022 doi:10.1109/ICBATS54253.2022.9759022
  23. Alarfaj FK, Malik I, Khan HU, Almusallam N, Ramzan M, Ahmed M. Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms. IEEE Access. 2022;10:39700–15. doi:10.1109/ACCESS.2022.3166891

 

  1. Novakovic J, Markovic S. Classifier Ensembles for Credit Card Fraud Detection. In: 2020 24th International Conference on Information Technology (IT) [Internet]. 2020 [cited 2024 Jul 30]. p. 1–4. Available from: https://ieeexplore.ieee.org/document/9070534 doi:10.1109/ IT48810.2020.9070534
  2. Mohsen OR, Nasserddine G, Massoud M fawaz. (PDF) Credit Card Fraud Detector Based on Machine Learning Techniques [Internet]. [cited 2024 Jul 15]. Available from: https://www.researchgate.net/publication/372101533_Credit_Card_Fraud_Detector_Based_on_Machine_Learning_Techniques
  3. Liu Q, Hu D, Yan Q. Decision tree algorithm based on average Euclidean distance. In: 2010 2nd International Conference on Future Computer and Communication [Internet]. 2010 [cited 2024 Jul 23]. p. V1-507-V1-511. Available from: https://ieeexplore.ieee.org/abstract/document/ 5497736 doi:10.1109/ICFCC.2010.5497736
  4. Du H, Lv L, Wang H, Guo A. A novel method for detecting credit card fraud problems. Alex SA, editor. PLOS ONE. 2024 Mar 6;19(3):e0294537. doi:10.1371/journal.pone.0294537
  5. Wang T, Zhao Y. Credit Card Fraud Detection using Logistic Regression. In: 2022 International Conference on Big Data, Information and Computer Network (BDICN) [Internet]. 2022 [cited 2024 Jul 15]. p. 301–5. Available from: https://ieeexplore.ieee.org/document/9758405 doi:10.1109/BDICN55575.2022.00064
  6. Mahajan AM, Baghel VS, Jayaraman R. Credit Card Fraud Detection using Logistic Regression with Imbalanced Dataset [Internet]. [cited 2024 Jul 15]. Available from: https://ieeexplore.ieee.org/document/10112302
  7. Krishna MV, Praveenchandar J. Comparative Analysis of Credit Card Fraud Detection using Logistic regression with Random Forest towards an Increase in Accuracy of Prediction. In: 2022 International Conference on Edge Computing and Applications (ICECAA) [Internet]. 2022 [cited 2024 Jul 15]. p. 1097–101. Available from: https://ieeexplore.ieee.org/document/9936488 doi:10.1109/ICECAA55415.2022.9936488
  8. Randhawa K, Loo CK, Seera M, Lim CP, Nandi AK. Credit Card Fraud Detection Using AdaBoost and Majority Voting. IEEE Access. 2018;6:14277–84. doi:10.1109/ ACCESS.2018.2806420
  9. Seera M, Lim CP, Kumar A, Dhamotharan L, Tan KH. An intelligent payment card fraud detection system. Ann Oper Res. 2024 Mar 1;334(1):445–67. doi:10.1007/s10479-021-04149-2
  10. Mniai A, Tarik M, Jebari K. A Novel Framework for Credit Card Fraud Detection. IEEE Access. 2023;11:112776–86. doi:10.1109/ACCESS.2023.3323842
  11. Kalid SN, Khor KC, Ng KH, Tong GK. Detecting Frauds and Payment Defaults on Credit Card Data Inherited With Imbalanced Class Distribution and Overlapping Class Problems: A Systematic Review. IEEE Access. 2024;12:23636–52. doi:10.1109/ACCESS.2024.3362831
  12. Dornadula VN, Geetha S. Credit Card Fraud Detection using Machine Learning Algorithms. Procedia Comput Sci. 2019;165:631–41. doi:10.1016/j.procs.2020.01.057

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