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Anonymized Credit Card Transaction Using Machine Learning Techniques

  • B. K. Padhi
  • S. Chakravarty
  • B. N. Biswal
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 109)

Abstract

In the last few years, anonymized credit card transactions have grown more threats that have caused serious consequences in the finance and banking sectors. Due to the dramatical growth of the online payment system, now many banks and financial sectors are implementing various types of automatic fraud detection system to analyze the fraud transactions; machine learning (ML) is one of the promising approaches to find out the fraud transactions. Machine learning methodologies have proved the most promising solution for anonymized transactions. This paper comparatively analyzes the basic machine learning algorithms which include SVM, LDA, QDA, DT, and RF for fraud detection. At the same time, some of the modern open-sourced boosting machine learning algorithms which include XGBoost, LGBoost, and CatBoost are also implemented.

Keywords

Fraud Fraud detection Machine learning Boosting algorithm Performance evaluation 

References

  1. 1.
    Zareapoor M, Shamsolmoali P (2015) Application of credit card fraud detection: based on bagging ensemble classifier. In: International Conference on Computer, Communication and Convergence (The authors published by Elsevier B.V)CrossRefGoogle Scholar
  2. 2.
    Shen A, Tong R, Deng Y (2007) Application of classification models on credit card fraud detection. In: 2007 International conference on service systems and service management, IEEE, pp 1–4Google Scholar
  3. 3.
    Awoyemi JO, Adetunmbi AO, Oluwadare SA (2017) Credit card fraud detection using machine learning techniques: a comparative analysis. In: 2017 International Conference on Computer Networking and Informatics (ICCNI), Lagos, pp 1–9Google Scholar
  4. 4.
    Rajora S, Li D-L, Jha C, Bharill N, Patel OP, Joshi S, Puthal D, Prasad M (2018) A comparative study of machine learning techniques for credit card fraud detection based on time variance, 978–1-5386-9276-9/18Google Scholar
  5. 5.
    Popat RR, Chaudhary J (2018) A survey on credit card fraud detection using machine learning. In: Proceedings of the 2nd International Conference on Trends in Electronics and Informatics (ICOEI 2018) IEEE Conference Record:# 42666; IEEE Xplore ISBN:978-1-5386-3570-4Google Scholar
  6. 6.
    Mittal S, Tyagi S (2019) Performance evaluation of machine learning algorithms for credit card fraud detection, 978-1-5386-5933-5/19/$31.00Google Scholar
  7. 7.
    Dhankhad S, Mohammed EA, Far B (2018) Supervised machine learning algorithms for credit card fraudulent transaction detection: a comparative study. In: 2018 IEEE international conference on information reuse and integration for data scienceGoogle Scholar
  8. 8.
    Rama Kalyani K, Uma Devi D (2012) Fraud detection of credit card payment system by genetic algorithm. Int J Sci Eng Res 3(7):1–6 (ISSN 2229-5518)Google Scholar
  9. 9.
    Meshram PL, Bhanarkar P (2012) Credit and ATM card fraud detection using genetic approach. Int J Eng Res Technol 1(10):1–5 (ISSN: 2278-0181)Google Scholar
  10. 10.
    Singh G, Gupta R, Rastogi A, Chandel MDS, Riyaz A (2012) A machine learning approach for detection of fraud based on SVM. Int J Sci Eng Technol 1(3):194–198 (ISSN : 2277-1581)Google Scholar
  11. 11.
    Seeja KR, Zareapoor M (2014) Fraud miner: a novel credit card fraud detection model based on frequent itemset mining. Sci World J 2014:1–10.  https://doi.org/10.1155/2014/252797 (Hindawi Publishing Corporation)CrossRefGoogle Scholar
  12. 12.
    Patil S, Somavanshi H, Gaikwad J, Deshmane A, Badgujar R (2015) Credit card fraud detection using decision tree induction algorithm. Int J Comput Sci Mobile Comput 4(4):92–95 (ISSN: 2320-088X)Google Scholar
  13. 13.
    Duman E, Buyukkaya A, Elikucuk I (2013) A novel and successful credit card fraud detection system implemented in a turkish bank. In: 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW), pp 162–171Google Scholar
  14. 14.
    Bahnsen AC, Stojanovic A, Aouada D, Ottersten B (2014) Improving credit card fraud detection with calibrated probabilities. In: Proceedings of the 2014 SIAM international conference on data mining, Society for Industrial and Applied Mathematics, pp 677–685Google Scholar
  15. 15.
    Ng AY, Jordan MI (2002) On discriminative vs. generative classifiers: a comparison of logistic regression and naive bayes. Adv Neural Inf Process Syst 2:841–848Google Scholar
  16. 16.
    Maes S, Tuyls K, Vanschoenwinkel B, Manderick B (2002) Credit card fraud detection using Bayesian and neural networks. In: Proceedings of the 1st international naiso congress on neuro fuzzy technologies, pp 261–270Google Scholar
  17. 17.
    Hsu C, Chang C, Lin C (2003) A practical guide to support vector classification. Technical report, Department of Computer Science and Information Engineering, National Taiwan UniversityGoogle Scholar
  18. 18.
    Ogwueleka FN (2011) Data mining application in credit card fraud detection system. J Eng Sci Technol 6(3):311–322Google Scholar
  19. 19.
    Mahmoudi N, Duman E (2015) Detecting credit card fraud by modified Fisher discriminant analysis. Expert Syst Appl 42:2510–2516CrossRefGoogle Scholar
  20. 20.
    Sorournejad S, Zojaji Z, Atani RE, Monadjemi AH (2016) A survey of credit card fraud detection techniques: data and technique oriented perspective. arXiv:1611.06439
  21. 21.
    Herawan T et al (eds) An application of oversampling, undersampling, bagging and boosting in handling imbalanced datasets. In: Proceedings of the first international conference on advanced data and information engineering (DaEng-2013). Lecture notes in electrical engineering vol 285. Springer Science+Business Media Singapore.  https://doi.org/10.1007/978-981-4585-18-7_2Google Scholar
  22. 22.
  23. 23.
    Syaripudin A, Khodra ML (2014) A comparison for handling imbalanced datsets. In: 2014 International Conference of Advanced Informatics: Concepts, Theory and Aplications (ICAICTA), 978-1-4799-5100-0/14Google Scholar
  24. 24.
    Thennakoon A, Bhagyani C, Premadasa S, Mihiranga S, Kuruwitaarachchi N (2019) Real-time credit card fraud detection using machine learning. 978-1-5386-5933-5/19Google Scholar
  25. 25.
    Nuno Abrunhosa Carneiro (2017) A data mining approach to fraud detection in e-tail. Published in Decision Support Systems.  https://doi.org/10.1016/j.dss.2017.01.002CrossRefGoogle Scholar
  26. 26.
    Lee S (2000) Noisy replication in skewed binary classification. Comput Stat Data Anal 34:165–191CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • B. K. Padhi
    • 1
  • S. Chakravarty
    • 1
  • B. N. Biswal
    • 2
  1. 1.Department of Computer Science & EngineeringCenturion University of Technology & ManagementBhubaneswarIndia
  2. 2.Department of Computer Science & EngineeringBhubaneswar Engineering CollegesBhubaneswarIndia

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