International Journal of Advanced Networking and Applications

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Utilization of Machine Learning Strategies in the Investigation of Suspected Credit Card Fraud


Affiliations
1 Department of Computer Science and Engineering, American International University, Bangladesh
2 Department of Computer Science, American International University, Bangladesh
 

Credit card fraud transactions have been one of the most difficult issues for banks and other financial institutions in recent years. In such events, billions of dollars are lost by financial institutions and the banking system. Concurrently, user information is not safe for that purpose. To address these issues, this paper proposes an efficient solution to automate the task using machine learning techniques such as SMOTE and ADASYN. This paper also intends to run machine learning supervised models. We discovered class imbalancing issues after examining the experiment outcomes on European cardholder datasets. Oversampling and under sampling strategies are utilized to solve fraud situations to avoid them. Predictive models such as the LR, K-nearest neighbors, decision tree, random forest XGBoost, and support vector machines are utilized to achieve the model accuracy required to find the most fit-able models for credit card fraud. The performance of SMOTE machine learning approaches increased with a 0.96 model accuracy in random forest and XGBoost.

Keywords

SMOTE, ADASYN, XGBoost, Machine Learning, SVM, Precision, Recall
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  • P. T. Praj Save, A Novel Idea for Credit Card Fraud Detection Using decision Tree, International Journal of Computer Application, p. 161,2017.

  • H. T. M. A. a. A. B. Thanh Thi Nguyen1, Deep Learning Methods for Credit Card Fraud Detection, IEEE, India, 2020.

  • S. Rahman, Credit, Debit Cards: Swindling on the rise, Fraudsters clone cards with data skimmed from shops, p. 1, 13 August 2017.

  • W. Jolly, Common credit card frauds and how to avoid them, p. 1, 10 July 2019.

  • B. G. G. V. R. S Venkata Suryanarayana, Machine Learning Approaches for Credit Card Fraud Detection, May 2018.

  • S. M. Zainab Assaghir, An Experimental Study With Imbalanced Classification Approaches for Credit Card Fraud Detection, Vols. IEEE Access PP (99):1-1, July 2019.

  • S. k. Darshan Kaur, Machine-Learning Approach for Credit Card Fraud Detection (KNN Naive Bayes), p. 5, 30 March 2020.

  • T. D. C.-H. N. T. T. Nghia Nguyen, A Proposed Model for Card Fraud Detection Based on Cat Boost And Deep Neural Network, 19 April 2022.

  • G. S. Vaishnavi Nath Dornadulaa*, Credit Card Fraud Detection using Machine Learning Algorithms, International conference on recent trends in advanced computing, 2019.

  • J. O. Awoyemi, A. O. Adetunmbi and S. A. Oluwadare, Credit card fraud detection using machine learning techniques: A comparative analysis, 2017.

  • n. D. Q. A. Kaneez zainab, A novel technique to Defraud credit card using an optimized cat boost Algorithm, vol. 100.4, no. 28th February 2022.

  • R. RAJAMANI and M. RATHIKA, Credit card fraud detection using hidden Markov model, ijana.in, https://ijana.in/Special%20Issue/file38.pdf.

  • Kumar, Ashish & Soni, Shivank & Agrawal, Chetan, A Survey Paper On Credit Card Fraud Detection Using Different Classifiers. International Journal of Computer Sciences and Engineering. 7. 552-559. 10.26438/ijcse/v7i1.552559, 2019.


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  • Utilization of Machine Learning Strategies in the Investigation of Suspected Credit Card Fraud

Abstract Views: 100  |  PDF Views: 1

Authors

Rifat Al Mamun Rudro
Department of Computer Science and Engineering, American International University, Bangladesh
Md. Faruk Abdullah Al Sohan
Department of Computer Science, American International University, Bangladesh

Abstract


Credit card fraud transactions have been one of the most difficult issues for banks and other financial institutions in recent years. In such events, billions of dollars are lost by financial institutions and the banking system. Concurrently, user information is not safe for that purpose. To address these issues, this paper proposes an efficient solution to automate the task using machine learning techniques such as SMOTE and ADASYN. This paper also intends to run machine learning supervised models. We discovered class imbalancing issues after examining the experiment outcomes on European cardholder datasets. Oversampling and under sampling strategies are utilized to solve fraud situations to avoid them. Predictive models such as the LR, K-nearest neighbors, decision tree, random forest XGBoost, and support vector machines are utilized to achieve the model accuracy required to find the most fit-able models for credit card fraud. The performance of SMOTE machine learning approaches increased with a 0.96 model accuracy in random forest and XGBoost.

Keywords


SMOTE, ADASYN, XGBoost, Machine Learning, SVM, Precision, Recall

References