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A Hybrid Machine Learning Approach for Credit Card Fraud Detection

A Hybrid Machine Learning Approach for Credit Card Fraud Detection

Sonam Gupta, Tushtee Varshney, Abhinav Verma, Lipika Goel, Arun Kumar Yadav, Arjun Singh
Copyright: © 2022 |Volume: 13 |Issue: 3 |Pages: 13
ISSN: 1938-0232|EISSN: 1938-0240|EISBN13: 9781683180500|DOI: 10.4018/IJITPM.313420
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MLA

Gupta, Sonam, et al. "A Hybrid Machine Learning Approach for Credit Card Fraud Detection." IJITPM vol.13, no.3 2022: pp.1-13. http://doi.org/10.4018/IJITPM.313420

APA

Gupta, S., Varshney, T., Verma, A., Goel, L., Yadav, A. K., & Singh, A. (2022). A Hybrid Machine Learning Approach for Credit Card Fraud Detection. International Journal of Information Technology Project Management (IJITPM), 13(3), 1-13. http://doi.org/10.4018/IJITPM.313420

Chicago

Gupta, Sonam, et al. "A Hybrid Machine Learning Approach for Credit Card Fraud Detection," International Journal of Information Technology Project Management (IJITPM) 13, no.3: 1-13. http://doi.org/10.4018/IJITPM.313420

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Abstract

The online banking system is the new trend in the developing digital world. The transferring of a large amount of currency in a millisecond is leading to fast accessing of the banking system as it saves more time at the online payment and digital shopping. The increase in rate of use of banking credit and debit card leads to a large amount of fraud in the field of finance. Machine learning has the new discovering faces in the field of the finance. So, this research work proposed a hybrid model using the logistic regression, multilayer perceptron, and the XgBoost. The study involves both the balance and imbalance dataset to conclude the result based on the accuracy precision and recall. The results show that accuracy of the model is 100%, and precision, recall, and F1-scores are 95.63%, 99.99%, and 97.76% respectively.

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