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Hybrid Method in Identifying the Fraud Detection in the Credit Card

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Evolutionary Computing and Mobile Sustainable Networks

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 53))

Abstract

As the world is rapidly moving towards digitalization and money transactions are becoming cashless, the utilization of credit cards has rapidly heightened. The fraud activities associated with it have also been increasing which leads to a huge loss to the financial institutions. Therefore, we need to analyze and detect the fraudulent transaction and separate them from the non-fraudulent ones. The paper proffers the frame work to detect credit card frauds. A combination of the three techniques is used. These methodologies include Decision Trees, Neural Network and K-Nearest Neighbor. For each new incoming transaction, the new label is assigned by taking the majority of the labels from the output of these techniques. This model is believed to work fairly good for all sizes and kinds of dataset as it combines the advantages of the individual techniques. We conclude the paper with a comparison of our model with the existing ones.

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References

  1. Statista the Statistic Portal, https://www.statista.com/topics/871/online-shopping/, March 14, 2017

  2. Singh AK, Kumar J (2019) Secure and energy aware load balancing framework for cloud data centre networks. Electron Lett. https://doi.org/10.1049/el.2019.0022

  3. Zheng L et al (2018) A new credit card fraud detecting method based on behavior certificate, IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), Zhuhai, pp 1–6

    Google Scholar 

  4. Delamaire L, Abdou HAH, Pointon J (2009) Credit card fraud and detection techniques: a review. Banks Bank Syst 4(2):57–68

    Google Scholar 

  5. Gupta I, Singh AK (2019) Dynamic threshold based Information leaker identification scheme. Inform Process Lett 147:69–73

    Article  MathSciNet  Google Scholar 

  6. Bhattacharyya S, Jha S, Tharakunnel K, Westland J (2011) Data mining for credit card fraud: a comparative study. Decis Support Syst 50:602–613

    Article  Google Scholar 

  7. Suraj P, Varsha N, Kumar SP (2018) Predictive modelling for credit card fraud detection using data analytics. Procedia Comput Sci 132:385–395

    Article  Google Scholar 

  8. Pearce J, Ferrier S (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model 133(3):225–245

    Article  Google Scholar 

  9. Zareapoor M, Shamsolmoali P (2015) Application of credit card fraud detection: based on bagging ensemble classifier. Procedia Comput Sci 48:679–686

    Article  Google Scholar 

  10. Sudha TRC (2017) Credit card fraud detection in internet using K nearest neighbour algorithm. IPASJ Int J Comput Sci 5(11)

    Google Scholar 

  11. Maes S, Tuyls K, Vanschoenwinkel B, Manderick B (2002) Credit card fraud detection using bayesian and neural networks

    Google Scholar 

  12. Ho TK (1995) Random decision forests, in: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol 1, pp 278–282, IEEE, August-1995

    Google Scholar 

  13. Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167

    Article  Google Scholar 

  14. Zhang ML, Zhou ZH (2007) A lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048

    Article  Google Scholar 

  15. Mishra A, Ghorpade C (2018) Credit card fraud detection on the skewed data using various classification and ensemble techniques, in: 2018 IEEE International Students’ Conference on Electrical Electronics and Computer Science (SCEECS), pp 1–5

    Google Scholar 

  16. Lakshmi SVSS, Kavilla SD, Machine learning for credit card fraud detection system

    Google Scholar 

  17. Duman E, Özçelik M (2011) Detecting credit card fraud by genetic algorithm and scatter search. Expert Syst Appl 38:13057–13063

    Article  Google Scholar 

  18. Shin KS, Lee YJ (2002) A genetic algorithm application in bankruptcy prediction modeling. Expert Syst Appl 23(3):321–328

    Article  Google Scholar 

  19. Malini N, Pushpa M Analysis on credit card fraud identification techniques based on KNN and outlier detection, in: 2017 Third International Conference on Advances in Electrical Electronics Information Communication and Bio-Informatics (AEEICB), pp 255–258

    Google Scholar 

  20. Navamani C, Krishnan S Credit card nearest neighbor based outlier detection techniques

    Google Scholar 

  21. Kazemi Z, Zarrabi H Using deep networks for fraud detection in the credit card transactions, in: 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), pp 630–633

    Google Scholar 

  22. Wang C, Wang Y, Ye Z, Yan L, Cai W, Pan S Credit card fraud detection based on whale algorithm optimized BP neural network, in: 2018 13th International Conference on Computer Science & Education (ICCSE), pp 1–4

    Google Scholar 

  23. Gupta I, Singh AK (2019) An integrated approach for data leaker detection in cloud environment. J Inf Sci Eng

    Google Scholar 

  24. Gupta I, Singh N, Singh AK (2019) Layer-based privacy and security architecture for cloud data sharing. J Commun Softw Syst (JCOMSS) 15(2)

    Google Scholar 

  25. Wang Y, Adams S, Beling P, Greenspan S, Rajagopalan S, Velez-Rojas M, ankovski S, Boker S, Brown D (2018) Privacy preserving distributed deep learning and its application in credit card fraud detection, 1070–1078

    Google Scholar 

  26. Reshma RS (2018) Deep learning enabled fraud detection in credit card transactions. Int J Res Sci Innov (IJRSI)

    Google Scholar 

  27. Pandey Y (2017) Credit card fraud detection using deep learning. Int J Adv Res Comput Sci, May–June

    Google Scholar 

  28. Dal Pozzolo A, Boracchi G, Caelen O, Alippi C, Bontempi G (2018) Credit card fraud detection: a realistic modeling and a novel learning Strategy. IEEE Trans Neural Netw Learn Syst 29(8):3784–3797

    Article  Google Scholar 

  29. Kazemi Z, Zarrabi H (2017) Using deep networks for fraud detection in the credit card transactions, in: IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), Tehran, 2017, pp 0630–0633

    Google Scholar 

  30. Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning, arXiv preprint arXiv:1506.00019

  31. Fine S, Singer Y, Tishby N (1998) The hierarchical hidden Markov model: analysis and applications. Mach Learn 32(1):41–62

    Article  Google Scholar 

  32. Gupta I, Singh AK (2018) A probabilistic approach for guilty agent detection using bigraph after distribution of sample data. Procedia Comput Sci 125:662–668

    Article  Google Scholar 

  33. Swain PH, Hauska H (1977) The decision tree classifier: design and potential. IEEE Trans Geosci Electron 15(3):142–147

    Article  Google Scholar 

  34. De Sá A, Pereira A, Pappa G (2018) A customized classification algorithm for credit card fraud detection

    Google Scholar 

  35. Panigrahi S, Kundu A, Sural S, Majumdar A (2009) Credit card fraud detection: a fusion approach using dempster–shafer theory and bayesian learning. Inform Fusion 10:354–363

    Article  Google Scholar 

  36. Cheng J, Greiner R (2011) Learning bayesian belief network classifiers: algorithms and system, in: Conference of the Canadian Society for Computational Studies of Intelligence, June 2011, pp 141–151. Springer, Berlin, Heidelberg

    Google Scholar 

  37. Kang F, Dawei C, Yi T, Liqing Z (2016) Credit card fraud detection using convolutional neural networks, 483–490

    Google Scholar 

  38. Kumar J, Singh AK (2018) Workload prediction in cloud using artificial neural network and adaptive differential evolution. Future Gener Comput Syst 81:41–52

    Article  Google Scholar 

  39. Jurgovsky J, Granitzer M, Ziegler K, Calabretto S, Portier PE, He L, Caelen O (2018) Sequence classification for credit-card fraud detection. https://doi.org/10.1016/j.procs.2015.04.201

    Article  Google Scholar 

  40. Bayer JS (2015) München, Technische Universität München. Learning sequence representations, Diss

    Google Scholar 

  41. Gupta I, Singh AK (2017) A probability based model for data leakage detection using bigraph, in: 7th International Conference on Communication and Network Security (ICCNS-2017), Tokyo, Japan, ACM, 2017

    Google Scholar 

  42. Gupta I, Singh AK (2019) A confidentiality preserving data leaker detection model for secure sharing of cloud data using integrated techniques, in: Seventh International Conference on Smart Computing and Communication Systems (ICSCC), Sarawak, Malaysia, pp 1–5

    Google Scholar 

  43. Kumar J, Singh AK (2019) Cloud resource demand prediction using differential evolution based learning scheme, in: 2019 Seventh International Conference on Smart Computing and Communication Systems (ICSCC), Curtin University, Miri

    Google Scholar 

  44. Patidar R, Sharma L (2011) Credit card fraud detection using neural network. Int J Soft Comput Eng (IJSCE) 1:32–38

    Google Scholar 

  45. Roy A, Sun J, Mahoney R, Alonzi L, Adams S, Beling P (2018) Deep learning detecting fraud in credit card transactions, in: Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, vol 2018, pp 129–134

    Google Scholar 

  46. Yusuf S, Duman E (2011) Detecting credit card fraud by decision trees and support vector machines, in: IMECS 2011 – International Multiconference of Engineers and Computer Scientists 2011, vol 1, pp 442–447

    Google Scholar 

  47. Sahin Y, Bulkan S, Duman E (2013) A cost-sensitive decision tree approach for fraud detection. Expert Syst Appl 40:5916–5923

    Google Scholar 

  48. Behera TK, Panigrahi S (2015) Credit card fraud detection: a hybrid approach using fuzzy clustering, neural network, in: Second International Conference on Advances in Computing and Communication Engineering, Dehradun, pp 494–499

    Google Scholar 

  49. Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 8:841–847

    Article  Google Scholar 

  50. Sanaz M, Mehdi S (2018) Cost-sensitive payment card fraud detection based on dynamic random forest and k-nearest neighbors

    Google Scholar 

  51. Srivastava A, Kundu A, Sural S, Majumdar A (2008) Credit card fraud detection using hidden markov model, in: IEEE Transactions on Dependable and Secure Computing, vol 5(1), pp 37–48

    Google Scholar 

  52. Onukwugha C (2018) Data mining application in credit card fraud detection system

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Applications, National Institute of Technology, Kurukshetra, India

    Pooja Tiwari, Simran Mehta, Nishtha Sakhuja, Ishu Gupta & Ashutosh Kumar Singh

Authors
  1. Pooja Tiwari
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  2. Simran Mehta
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  3. Nishtha Sakhuja
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  4. Ishu Gupta
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  5. Ashutosh Kumar Singh
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Corresponding authors

Correspondence to Pooja Tiwari or Ishu Gupta .

Editor information

Editors and Affiliations

  1. Research and Industry Incubation Center, Department of Information Science and Engineering, Dayananda Sagar College of Engineering, Bangalore, India

    V. Suma

  2. Department of Technology and Maritime Innovation, University of Southeast, Horten, Norway

    Noureddine Bouhmala

  3. Go Perception Laboratory, Cornell University, Ithaca, NY, USA

    Haoxiang Wang

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Tiwari, P., Mehta, S., Sakhuja, N., Gupta, I., Singh, A.K. (2021). Hybrid Method in Identifying the Fraud Detection in the Credit Card. In: Suma, V., Bouhmala, N., Wang, H. (eds) Evolutionary Computing and Mobile Sustainable Networks. Lecture Notes on Data Engineering and Communications Technologies, vol 53. Springer, Singapore. https://doi.org/10.1007/978-981-15-5258-8_3

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  • DOI: https://doi.org/10.1007/978-981-15-5258-8_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5257-1

  • Online ISBN: 978-981-15-5258-8

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