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Credit Card Fraud Detection Using Machine Learning

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  • First Online:
Cybersecurity Challenges in the Age of AI, Space Communications and Cyborgs (ICGS3 2023)

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Abstract

This research explores the application of Machine Learning (ML) algorithms in the detection of credit card fraud. Credit card fraudulence is a major concern that poses a significant threat to the financial industry, and machine learning has emerged as a promising approach to addressing this issue. The motive of this study is mainly to evaluate the efficacy of ML algorithms in the detection of credit card fraudulence and, to identify the key elements that impact the accuracy of these ML algorithms. The study utilizes a rigorous experimental design and a large sample size, which enables the analysis of the performance of diverseML algorithms. However, the choice of ML algorithm and the performance evaluation metrics that is used are the important factors that influence the accuracy of the algorithms. This study also identifies the key factors that influence the accuracy of machine learning algorithms, including the choice of features, the quality of the data, and, also the parameter settings of the algorithms.

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References

  1. Bengio Y, Bengio S (2020) The consciousness prior. https://doi.org/10.48550/arXiv.1709.08568. Accessed 25 Mar 2023

  2. Bhandari A, Gupta S (2020) A review on performance evaluation metrics for machine learning models. Int J Adv Sci Technol 29(9):2843–2849

    Google Scholar 

  3. Bisong E, Bisong E (2019) Introduction to Scikit-learn. In: Building machine learning and deep learning models on google cloud platform: a comprehensive guide for beginners, pp 215–229

    Google Scholar 

  4. Cheema J, Raza K (2021) Data preprocessing techniques in machine learning: a comprehensive review. Int J Comput Intell Syst 14(1):944–971. https://doi.org/10.2991/ijcis.d.210327.001

    Article  Google Scholar 

  5. Chen Y (2020) Credit card fraud detection using machine learning and data mining techniques: a systematic review. J Financ Crime 27(3):647–662. https://doi.org/10.1108/JFC-03-2019-0044. Accessed 05 May 2023

  6. Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP (2020) Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol 9(2):10–14

    Google Scholar 

  7. Copeland BJ (2020) Alan Turing and the mathematical obsolescence of intelligence. Mind Mach 30(2):231–254

    Google Scholar 

  8. Düntsch I, Gediga G (2019) Confusion matrices and rough set data analysis. J Phys Conf Ser IOP Publishing 1229(1):012–055

    Google Scholar 

  9. Fan W, Liu K, Liu H, Ge Y, Xiong H, Fu Y (2021) Interactive reinforcement learning for feature selection with decision tree in the loop. IEEE Trans Knowl Data Eng. https://doi.org/10.48550/arXiv.2010.02506. Accessed 03 May 2023

  10. Ge D, Gu J, Chang S, Cai J (2020) Credit card fraud detection using lightgbm model. In: International conference on E-commerce and internet technology (ECIT), IEEE, pp 232–236. https://doi.org/10.1109/ECIT50008.2020.00060

  11. Gohil S (2020) Comparative analysis of different performance evaluation metrics for breast cancer detection using ROC analysis. Int J Adv Res Comput Sci 11(3):107–114

    Google Scholar 

  12. Gulati M, Thakur M (2021) A comprehensive study on data analysis using Python. Int J Recent Technol Eng 9(3):9123–9130. https://doi.org/10.35940/ijrte.C9799.129321

    Article  Google Scholar 

  13. Harvey H, Glocker B (2019) A standardised approach for preparing imaging data for machine learning tasks in radiology. In: Artificial intelligence in medical imaging. Springer, pp 61–72. https://doi.org/10.1007/978-3-319-94878-2_6

  14. Huang J, Xie M, Wang Q (2021) A comparative study of data normalization techniques for deep learning. Appl Sci 11(6):2846

    Google Scholar 

  15. Khatri S, Arora A, Agrawal AP (2020) Supervised machine learning algorithms for credit card fraud detection: a comparison. In: 10th International conference on cloud computing, data science & engineering (Confluence), IEEE, pp 680–683

    Google Scholar 

  16. Kim M, Kim H, Kim J (2020) Credit card behavioral fraud detection using machine learning techniques. Expert Syst Appl 146:113–188. https://doi.org/10.1016/j.eswa.2019.113188

    Article  Google Scholar 

  17. Kim M, Lee J, Ohno-Machado L, Jiang X (2019) Secure and differentially private logistic regression for horizontally distributed data. IEEE Trans Inf Forensics Secur 15:695–710

    Article  Google Scholar 

  18. Kugler KG, Mueller KR, Mika S (2020) ROC curves: a review of methods with applications in machine learning. Mach Learn 109(1):45–80

    Google Scholar 

  19. Liang J (2022) Confusion matrix: machine learning. POGIL Activity Clearinghouse, vol 3(4)

    Google Scholar 

  20. Liu S, Zhang J, Wu X, Guo B (2020) A comparative study of performance metrics for imbalanced classification of COVID-19 data. BMC Med Inform Decis Mak 20(1):1–13

    Google Scholar 

  21. McInerney J, Astwood M (2021) A guide to data visualization in Python with Matplotlib. J Open Source Softw 6(61):3171. https://doi.org/10.21105/joss.03171

    Article  Google Scholar 

  22. Mennella TA, Haynes RD (2021) Statistical analysis and data visualization using Python. J Chem Inf Model 61(4):1834–1840. https://doi.org/10.1021/acs.jcim.0c01446. Accessed 26 Apr 2023

  23. Niu X, Wang L, Yang X (2019) A comparison study of credit card fraud detection: supervised versus unsupervised

    Google Scholar 

  24. Park M, Kim J (2019) Credit card fraud detection using a convolutional neural network. Appl Sci 9(21):4585

    Google Scholar 

  25. Raabe CA, Fritsch J (2021) Analyzing and visualizing data with python: a practical guide. Springer. https://doi.org/10.1007/978-3-030-79701-5

  26. Ramezan C, Warner AT, Maxwell AE (2019) Evaluation of sampling and cross-validation tuning strategies for regional-scale machine learning classification. Remote Sens 11(2):185

    Article  ADS  Google Scholar 

  27. Razzak I, Choudhury SR (2019) Credit card fraud detection using machine learning: a systematic literature review. Expert Syst Appl 132:381–394. https://doi.org/10.1016/j.eswa.2019.04.053

    Article  Google Scholar 

  28. Ristea A, Petrescu L, Grigoras G (2020) Performance evaluation metrics for classification problems in machine learning. Adv Electr Comput Eng 20(3):91–96

    Google Scholar 

  29. Roelofs R, Shankar V, Recht B, Fridovich-Keil S, Hardt M, Miller J, Schmidt L (2019) A meta-analysis of overfitting in machine learning. Adv Neural Inf Process Syst 32

    Google Scholar 

  30. Sailusha R, Gnaneswar V, Ramesh R, Rao GR (2020) Credit card fraud detection using machine learning. In: 4th International conference on intelligent computing and control systems (ICICCS), IEEE, pp 1264–1270

    Google Scholar 

  31. Santos MS, Embrechts MJ (2020) A review of methods for addressing class imbalance in machine learning. J Big Data 7(1):1–35. https://doi.org/10.1186/s40537-020-00323-4

    Article  Google Scholar 

  32. Shaohui D, Qiu G, Mai H, Yu H (2021) Customer transaction fraud detection using random forest. In: International conference on consumer electronics and computer engineering (ICCECE), IEEE, pp 144–147

    Google Scholar 

  33. Singh RK, Dwivedi G, Singh SK (2020) Comparative study of performance metrics for machine learning models on sentiment analysis of social media data. J Ambient Intell Humaniz Comput 11(7):2985–2997

    Google Scholar 

  34. Subasi A (2020) Practical machine learning for data analysis using python. Academic Press

    Google Scholar 

  35. Tock K (2019) Google colaboratory as a platform for Python coding with students. In: RTSRE Proceedings, vol 2(1)

    Google Scholar 

  36. Vabalas A, Gowen E, Poliakoff E, Casson AJ (2019) Machine learning algorithm validation with a limited sample size. PLoS ONE 14(11):224–365

    Article  Google Scholar 

  37. Yang L, Shami A (2020) On hyperparameter optimization of machine learning algorithms: theory and practice. Neurocomputing 415:295–316

    Article  Google Scholar 

  38. scikit-learn (2022) Decision Trees. [online] https://scikit-learn.org; https://scikit-learn.org/stable/modules/tree.html. Accessed 2023

  39. scikit-learn (2022) Logistic regression. [online] https://scikit-learn.org; https://scikit-learn.org/stable/modules/regression.html. Accessed 2023

  40. scikit-learn (2022) Random forest. https://scikit-learn.org; https://scikit-learn.org/stable/modules/forest.html. Accessed 2023

  41. scikit-learn (2022) XGboost. https://scikit-learn.org; https://scikit-learn.org/stable/modules/xgboost.html. Accessed 2023

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Authors and Affiliations

  1. Northumbria University, London, UK

    Berlin Srojila Manickam & Hamid Jahankhani

Authors
  1. Berlin Srojila Manickam
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  2. Hamid Jahankhani
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Correspondence to Hamid Jahankhani .

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  1. Northumbria University, London, UK

    Hamid Jahankhani

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Manickam, B.S., Jahankhani, H. (2024). Credit Card Fraud Detection Using Machine Learning. In: Jahankhani, H. (eds) Cybersecurity Challenges in the Age of AI, Space Communications and Cyborgs. ICGS3 2023. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-031-47594-8_15

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