Advertisement

Springer Nature is making Coronavirus research free. View research | View latest news | Sign up for updates

A bio-inspired credit card fraud detection model based on user behavior analysis suitable for business management in electronic banking

  • 18 Accesses

Abstract

The widened uses of Internet credit cards in e-banking systems are currently prone to credit card fraud. Data imbalance also poses a significant difficulty in the method of fraud detection. The efficiency of the existing fraud detection systems is only in question because it detects fraudulent action after the suspect transaction has been completed. To address these difficulties, this article offers an improved two-level credit card fraud tracking model from imbalanced datasets based on the semantic fusion of k-means and the artificial bee colony (ABC) algorithm to improve identification precision and accelerate the convergence of detection. In the proposed model, ABC works as a kind of neighborhood search associated with a global search to be a second classification level to manage the failure of the k-means classifier to explore the actual clusters as it is sensitive to the initial condition. The proposed model filters the characteristics of the dataset using an integrated rule engine to evaluate whether the operation is real or false, depending on many parameters of client conduct (profile) such as geographical locations, usage frequency, and book balance. Experimental findings show that the suggested model can improve the precision of ranking against the danger of suspect operations and provide higher accuracy relative to traditional techniques.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2

References

  1. Adnan M (2011) Detect CNP fraudulent transactions. World Comput Sci Inf Technol J 1(8):326–332

  2. Adnan M (2012) Electronic payment fraud detection techniques. World Comput Sci Inf Technol J 2(4):137–141

  3. Amala MD, Ravi R (2020) Multi feature behavior approximation model based efficient botnet detection to mitigate financial frauds. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-020-01677-w

  4. Bin W, Cun H (2011) Differential artificial bee colony algorithm for global numerical optimization. J Comput 6(5):841–848

  5. Coppolino L, D’Antonio S, Formicola V et al (2015) Use of the Dempster–Shafer theory to detect account takeovers in mobile money transfer services. J Ambient Intell Hum Comput 6:753–762

  6. Deoshree D, Snehlata S (2017) Classification model using optimization technique: a review. Int J Comput Sci Netw 6(1):42–48

  7. Dheepa V, Dhanapal R (2012) Behavior based credit card fraud detection using support vector machines. J Soft Comput 2(4):391–399

  8. Divya S, Rakesh P (2015) Credit card fraud detection using hidden Markov model. Int J Sci Eng Res 6(1):1488–1491

  9. Faiza A, Azuraliza A (2012) A cluster-based deviation detection task using the artificial bee colony algorithm. Int J Soft Comput 2(7):71–78

  10. Falaki S, Alese B, Adewale O, Ayeni J, Aderounmu G (2012) Probabilistic credit card fraud detection system in online transactions. Int J Softw Eng Appl 6(4):69–78

  11. Ganesh K, Vasanth S (2012) Novel artificial neural networks and logistic approach for detecting credit card deceit. Int J Comput Sci Manag Res 1(3):297–303

  12. Ganesh K, Vasanth S (2013) Novel artificial neural networks and logistic approach for detecting credit card deceit. Int J Comput Sci Netw Secur 13(9):58–65

  13. Ghori KM, Imran M, Nawaz A et al (2020) Performance analysis of machine learning classifiers for non-technical loss detection. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-019-01649-9

  14. Hala M, Ghada H, Yousef A (2016) ABC-SVM: artificial bee colony and SVM method for microarray gene selection and multi class cancer classification. Int J Mach Learn Comput 6(3):184–190

  15. Ishu T, Mrigya M (2016) Credit card fraud detection. Int J Adv Res Comput Commun Eng 5(1):39–42

  16. Jain AK, Gupta BB (2018) Two-level authentication approach to protect from phishing attacks in real time. J Ambient Intell Hum Comput 9:1783–1796

  17. Krishna K, Mahesh A (2012) Survey on credit card fraud detection methods. Int J Emerg Technol Adv Eng 2(11):721–726

  18. Madhav P, Anil K, Varun B (2015) Credit card fraud detection using an efficient enhanced k- mean clustering algorithm. Int J Eng Comput Sci 4(2):10367–10374

  19. Meena K, Veena K (2019) Performance evaluation of cybercriminal detection through cluster computing techniques. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-019-01605-7

  20. Meira J, Andrade R, Praça I et al (2019) Performance evaluation of unsupervised techniques in cyber-attack anomaly detection. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-019-01417-9

  21. Mittal S, Tyagi S (2019) Performance evaluation of machine learning algorithms for credit card fraud detection. In 9th international conference on cloud computing, data science and engineering, pp 320–324

  22. Mohd A, Yuk Y, Wei C, Noorhaniza W, Ahmed M (2011) ABC based data mining algorithms for classification tasks. CA Center Sci Educ 5(4):217–231

  23. Mortazavi E, Ahmadzadeh M (2014) A hybrid approach for automatic credit approval. Int J Sci Eng Res 5(8):614–619

  24. Morteza T (2015) Improving genetic algorithm operators for analyzing. Int J New Technol Res 1(6):1–9

  25. Nadisha A, Rakendu R, Surekha M (2015) A hybrid approach to detect credit card fraud. Int J Sci Res Publ 5(11):304–314

  26. Naik H, Kanikar P (2019) Credit card fraud detection based on machine learning algorithms. Int J Comput Appl 182(44):8–12

  27. Nimisha P, Sherly K (2012) Credit card fraud detection based on behavior mining. Int J Sci Technol Res 1(2012):7–12

  28. Pooja C, Prajakta K, Madhura G, Priyanka N (2015) Genetic K-means algorithm for credit card fraud detection. Int J Comput Sci Inf Technol 6(2):1724–1727

  29. Prakash A, Chandrasekar C (2015) An optimized multiple semi-hidden Markov model for credit card fraud detection. Indian J Sci Technol 8(2):165–171

  30. Pumsirirat A, Yan L (2018) Credit card fraud detection using deep learning based on auto-encoder and restricted boltzmann machine. Int J Adv Comput Sci Appl 9(1):18–25

  31. Rama K, Uma D (2012) Fraud detection of credit card payment system by genetic algorithm. Int J Sci Eng Res 3(7):1–6

  32. Rinkal S, Samir K, Hiteshkumar N (2014) Artificial bee colony algorithm, a comparative approach for optimization algorithm and application: survey. Int J Futur Trends Eng Technol 4(1):17–21

  33. Roy A, Sun J, Mahoney R, Alonzi L, Adams S, Beling P (2018) Deep learning detecting fraud in credit card transactions. In IEEE systems and information engineering design symposium, pp 129–134

  34. Shilpa H, Kulkarni R (2015) Credit card fraud detection system based on user based model with GA and artificial immune system. J Multidiscip Eng Sci Technol 2(7):1820–1825

  35. Siddhi D, Vidhi S, Jay V (2016) Credit card fraud detection using hybrid approach. Int J Adv Res Comput Commun Eng 5(5):287–289

  36. Sivakumar N, Balasubramanian R (2016) Enhanced anomaly detection in imbalanced credit card transactions using hybrid PSO. Int J Comput Appl 35(1):28–32

  37. Stephen G, Olumide O, Oluwafunmito A (2016) Hybrid methods for credit card fraud detection using k-means clustering with hidden Markov model and multilayer perception algorithm. Br J Appl Sci Technol 13(5):1–11

  38. Sudan J (2015) Emerging issues of credit card frauds and their detection techniques using genetic algorithm. Int J Integr Comput Appl Res 1(1):1–6

  39. Sunil B, Rashmi B, Santosh H (2016) Analysis of credit card fraud detection techniques. Int J Sci Res 5(3):1302–1306

  40. Vadoodparast M, Razak H (2015) Fraudulent electronic transaction detection using dynamic model. Int J Comput Sci Inf Secur 13(2):1–10

  41. Vaishali V (2014) Fraud detection in credit card by clustering approach. Int J Comput Appl 98(3):29–32

  42. Vasilomanolakis E, Karuppayah S, Mühlhäuser M, Fischer M (2015) Taxonomy and survey of collaborative intrusion detection. ACM Comput Surv 47(4):55–87

  43. Wu S, Foo B, Mei Y, Bagchi S (2003) Collaborative intrusion detection system (CIDS): a framework for accurate and efficient IDS. In: 19th annual computer security applications conference, pp 234–244

  44. Yashvi J, Namrata T, Shripriya D, Sarika J (2019) A comparative analysis of various credit card fraud detection techniques. Int J Recent Technol Eng 7(2):402–407

Download references

Funding

None.

Author information

SMD: conceived and designed the analysis, collected the data, performed the analysis, and write and correct proof the submitted manuscript.

Correspondence to Saad M. Darwish.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Darwish, S.M. A bio-inspired credit card fraud detection model based on user behavior analysis suitable for business management in electronic banking. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-01759-9

Download citation

Keywords

  • Behavior analysis
  • Credit card fraud detection
  • E-banking
  • Information fusion
  • Multi-level classification
  • Optimization algorithm