Author(s)

Yitong Liu ¹

Affiliation(s)

¹ Statistic Department, Columbia University, 116th and Broadway, New York, U.S.A.

Corresponding Author

Yitong Liu

ABSTRACT

Nowadays personal credit history is very important, it could affect opening account at bank, renting apartments, etc. It is crucial for the bank to use machine learning techniques to recognize unusual behaviors to avoid unnecessary losses. In China, there are large amount of people who hold their own credit card, but only a few of them would use their card in daily life due to uncertainty of the safety of the credit card payment [ref1]. This project focuses on detecting credit card fraud using a combination of supervised and unsupervised learning techniques. The dataset, sourced from Kaggle, contains over 594,000 credit card transactions from 4,112 unique customers, with key features such as transaction amount, merchant, and customer details. We process the data by cleaning and dealing with its nature of imbalance. By applying different method, we have concluded that that while supervised learning models provided excellent recall and accuracy, there is still room for improvement in reducing false positives, particularly in unsupervised methods. Future work includes oversampling to further balance the dataset and testing the models on larger datasets to enhance generalization.

KEYWORDS

Data visualization; credit card fraud; PCA

CITE THIS PAPER

Yitong Liu, Credit Card Fraud Detection. Financial Engineering and Risk Management (2024) Vol. 7: 71-79. DOI: http://dx.doi.org/10.23977/ferm.2024.070610.

REFERENCES

[1] Khyati Chaudhary, Jyoti Yadav, Bhawna Mallick. A Review of Fraud Detection Techniques: Credit Card. International Journal of Computer Applications, Volume 45, No.1, pp. 39-44, May 2012.
[2] Kaggle. Synthetic data from a financial payment    system. Retrieved from https://www.kaggle.com/ datasets/ ealaxi/ banksim1/ data
[3] https://www.kaggle.com/code/turkayavci/fraud-detection-on-bank-payments https://www.kaggle.com/ code/andr adaolteanu/ ii- fraud- detection- classify-cluster-pca
[4] Muhammad Zohaib Khan, Sarmad Ahmed Shaikh, Muneer Ahmed Shaikh, Kamlesh Kumar Khatri, Mahira Abdul Rauf, Ayesha Kalhoro, and Muhammad Adnan, The Performance Analysis of Machine Learning Algorithms for Credit Card Fraud Detection, International Journal of Online and Biomedical Engineering (iJOE), vol. 19, no. 3, pp. 82-98, 2023. doi:10.3991/ijoe.v19i03.35331.
[5] Keshetti Sreekala, Rayavarapu Sridivya, Nynalasetti Kondala Kameswara Rao, Raman Kumar Mandal, G. Jose Moses, and A. Lakshmanarao, A Hybrid Kmeans and ML Classification Approach for Credit Card Fraud Detection, in Proceedings of the IEEE Conference, 2020.
[6] Jonnalagadda Vaishnave, Priya Gupta, and Eesita Sen, Credit card fraud detection using Random Forest Algorithm, International Journal of Advance Research, Ideas and Innovations in Technology, vol. 5, no. 2, pp. 1797-1801, 2019.

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