Fraud detection within bankcard enrollment on mobile device based payment using machine learning

  • Hao ZhouEmail author
  • Hong-feng Chai
  • Mao-lin Qiu


The rapid growth of mobile Internet technologies has induced a dramatic increase in mobile payments as well as concomitant mobile transaction fraud. As the first step of mobile transactions, bankcard enrollment on mobile devices has become the primary target of fraud attempts. Although no immediate financial loss is incurred after a fraud attempt, subsequent fraudulent transactions can be quickly executed and could easily deceive the fraud detection systems if the fraud attempt succeeds at the bankcard enrollment step. In recent years, financial institutions and service providers have implemented rule-based expert systems and adopted short message service (SMS) user authentication to address this problem. However, the above solution is inadequate to face the challenges of data loss and social engineering. In this study, we introduce several traditional machine learning algorithms and finally choose the improved gradient boosting decision tree (GBDT) algorithm software library for use in a real system, namely, XGBoost. We further expand multiple features based on analysis of the enrollment behavior and plan to add historical transactions in future studies. Subsequently, we use a real card enrollment dataset covering the year 2017, provided by a worldwide payment processor. The results and framework are adopted and absorbed into a new design for a mobile payment fraud detection system within the Chinese payment processor.

Key words

Fraud detection Mobile payment Bankcard enrollment Mobile device based GBDT XGBoost 

CLC number

TP309.2 TP181 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



We want to thank Drs. Jun WU, Jin-tao ZHAO, and Jian-hua LI for their advice on machine learning based bankcard fraud scoring and its applications design.


  1. Bhattacharyya S, Jha S, Tharakunnel K, et al., 2011. Data mining for credit card fraud: a comparative study. Dec Support Syst, 50(3):602–613. CrossRefGoogle Scholar
  2. Bolton RJ, Hand DJ, 2002. Statistical fraud detection: a review. Stat Sci, 17(3):235–255. MathSciNetCrossRefzbMATHGoogle Scholar
  3. Chen TQ, Guestrin C, 2016. XGBoost: a scalable tree boosting system. Proc 22nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.785–794. CrossRefGoogle Scholar
  4. Cheng J, Wang PS, Li G, et al., 2018. Recent advances in efficient computation of deep convolutional neural networks. Front Inform Technol Electron Eng, 19(1):64–77. MathSciNetCrossRefGoogle Scholar
  5. China UnionPay, 2017. China Bank Card Annual Fraud Report 2017.Google Scholar
  6. Correa Bahnsen A, Stojanovic A, Aouada D, et al., 2013. Cost sensitive credit card fraud detection using Bayes minimum risk. Proc 12th Int Conf on Machine Learning and Applications, p.333–338. Google Scholar
  7. Correa Bahnsen A, Aouada D, Ottersten B, 2014. Exampledependent cost–sensitive logistic regression for credit scoring. Proc 13th Int Conf on Machine Learning and Applications, p.263–269. Google Scholar
  8. Correa Bahnsen A, Aouada D, Stojanovic A, et al., 2016. Feature engineering strategies for credit card fraud detection. Expert Syst Appl, 51:134–142. CrossRefGoogle Scholar
  9. Dal Pozzolo A, Caelen O, Le Borgne YA, et al., 2014. Learned lessons in credit card fraud detection from a practitioner perspective. Expert Syst Appl, 41(10):4915–4928. CrossRefGoogle Scholar
  10. Fu K, Cheng DW, Tu Y, et al., 2016. Credit card fraud detection using convolutional neural networks. Proc 23rd Int Conf on Neural Information Processing, p.483–490. CrossRefGoogle Scholar
  11. Hand DJ, Henley WE, 1997. Statistical classification methods in consumer credit scoring: a review. J R Stat Soc Ser A, 160(3):523–541. CrossRefGoogle Scholar
  12. Hand DJ, Whitrow C, Adams NM, et al., 2008. Performance criteria for plastic card fraud detection tools. J Oper Res Soc, 59(7):956–962. CrossRefGoogle Scholar
  13. Jurgovsky J, Granitzer M, Ziegler K, et al., 2018. Sequence classification for credit–card fraud detection. Expert Syst Appl, 100:234–245. CrossRefGoogle Scholar
  14. Krivko M, 2010. A hybrid model for plastic card fraud detection systems. Expert Syst Appl, 37(8):6070–6076. CrossRefGoogle Scholar
  15. Li S, Song SJ, Wu C, 2018. Layer–wise domain correction for unsupervised domain adaptation. Front Inform Technol Electron Eng, 19(1):91–103. CrossRefGoogle Scholar
  16. Li XR, Yu W, Luwang TY, et al., 2018. Transaction fraud detection using GRU–centered sandwich–structured model. Google Scholar
  17. Maes S, Tuyls K, Vanschoenwinkel B, et al., 2002. Credit card fraud detection using Bayesian and neural networks. Proc 1st Int NAISO Congress on Neuro Fuzzy Technologies, p.261–270.Google Scholar
  18. Sánchez D, Vila M, Cerda L, et al., 2009. Association rules applied to credit card fraud detection. Expert Syst Appl, 36(2):3630–3640. CrossRefGoogle Scholar
  19. Tian YH, Chen XL, Xiong HK, et al., 2017. Towards humanlike and transhuman perception in AI 2.0: a review. Front Inform Technol Electron Eng, 18(1):58–67. CrossRefGoogle Scholar
  20. Wang HZ, Zhang P, Xiong L, et al., 2016. A secure and high–performance multi–controller architecture for software–defined networking. Front Inform Technol Electron Eng, 17(7):634–646. CrossRefGoogle Scholar
  21. Wang S, Wu J, Zhang YT, 2018. Consumer preferenceenabled intelligent energy management for smart cities using game theoretic social tie. Int J Distrib Sens Networks, 14(4):1–11. Google Scholar
  22. Wu J, Dong MX, Ota K, et al., 2018. Big data analysis–based secure cluster management for optimized control plane in software–defined networks. IEEE Trans Netw Serv Manag, 15(1):27–38. CrossRefGoogle Scholar
  23. Zhou YK, Chai HF, 2017. Research and practice on system engineering management of a mobile payment project. Front Eng Manag, 2017, 4(2):127–137. Scholar

Copyright information

© Editorial Office of Journal of Zhejiang University Science and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Cyber SecurityShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Department of Risk ControlChina UnionPayShanghaiChina
  3. 3.Office of Board of DirectorsChina UnionPayShanghaiChina
  4. 4.Chinese Academy of EngineeringBeijingChina

Personalised recommendations