Learning Fraud Detection from Big Data in Online Banking Transactions: A Systematic Literature Review


  • . Indrajani School of Information System, Bina Nusantara University, KH. Syahdan, 9, 11480 Jakarta, Indonesia
  • Harjanto Prabowo Bina Nusantara University, KH. Syahdan, 9, 11480 Jakarta, Indonesia
  • . Meyliana School of Information System, Bina Nusantara University, KH. Syahdan, 9, 11480 Jakarta, Indonesia


The implementation of fraud detection in online banking transactions on big data is one of the most important strategies applied by banks to protect their transactions and highly related to algorithms. In fact, it is not easy to successfully implement this strategy because it requires a huge investment and is influenced by complexity algorithms, training, and testing. The frauds bring fatal impact, such as destruction of the banking reputation, banking loss, and state financial loss. One target of the fraud perpetrators in banking is online banking transactions. Security has become a major issue in the online banking transaction. Furthermore, the research of fraud is switching to big data and turns out that online banking data are stored in the database operational and big data. This study aims to find out what kind of algorithms fraud detection for online banking transactions using a systematic literature review to the 25 relevant papers.


K 1. R. Khande: Online Banking in India, “Attacks and Preventive Measures to Minimize Risk,” ICICES2014, no. 978, 2014.

O. O. Maruatona, P. Vamplew, and R. Dazeley , “Prudent Fraud Detection in Internet Banking,” Third Cybercrime Trust. Comput. Work, pp. 60–65, 2012.

T. Gregory, C. Tina, D. Naman, J. Keith, and R. Richard, “A Synthesis of Fraud Related Research. Am,” Account. Assoc. J. , pp. 63, 2012.

M. Ivanović and M. Radovanović, “Modern machine learning techniques and their applications,” International Conference on Electronic, Communication, and Network, 2014.

I. Muhammad and Z. Yan, , “Supervised Machine Learning Approaches : A Survey,” ICTACT Journal on Soft Computing, pp. 946–952, 2015.

M. A. Alsheikh, S. Lin, D. Niyato, and H. Tan , “Machine Learning in Wireless Sensor Networks : Algorithms , Strategies , and Applications,”Communications Surveys & Tutorials, IEEE, pp.1–23, 2015.

J. P. Linda Delamaire, Hussein Abdou , “Credit card fraud and detection techniques : a review,” Banks Bank Syst., vol. 4, no. 2, 2009.

S. B. E. Raj, A. A. Portia, and A. Sg , “Analysis on Credit Card Fraud Detection Methods,” International Conference on Computer, Communication and Electrical Technology, pp. 152–156, 2011.

M. Paliwal and U. A. Kumar , “Neural networks and statistical techniques : A review of applications,” Expert Syst. Appl., vol. 36, no. 1. pp. 2–17, 2009.

A. Dal, O. Caelen, Y. Le Borgne, S. Waterschoot, and G. Bontempi, “Learned lessons in credit card fraud detection from a practitioner perspective,” Expert Syst. Appl., vol. 41, no. 10, pp. 4915

, 2014.

M. A. B. Bella, J. H. P. Eloff, and M. S. Olivier , “A fraud management system architecture for next-generation networks,” Forensic Science International, vol. 185, pp. 51–58, 2009.

X. Chen, S. Member, and X. Lin, “Big Data Deep Learning : Challenges and Perspectives,”. 2014 IEEE. Translations and content mining are permitted for academic research only, vol. 2, pp. 2169-3536, 2014.

E. Duman, “A Novel and Successful Credit Card Fraud Detection System,” IEEE 13th International Conference on Data Mining Workshops, 2015.

R. Rieke, M. Zhdanova, J. Repp, R. Giot, and Gaber , “Fraud Detection in Mobile Payments Utilizing Process Behavior Analysis,”Int. Conf. Availability, Reliab. Secur, pp. 662–669, 2013.

V. Van Vlasselaer, C. Bravo, O. Caelen, T. Eliassi-rad, L. Akoglu, M. Snoeck, and B. Baesens, “APATE : A novel approach for automated credit card transaction fraud detection using network-based extensions,”Decis. Support Syst., vol. 75, pp. 38–48, 2015.

L. Zhang, J. Yang, and B. Tseng, “Online Modeling of Proactive Moderation System for Auction Fraud Detection. International World Wide Web Conference Committee,” pp. 669–678, 2012.

C. Classifier, J. Kim, K. Choi, G. Kim, and Y. Suh, “Expert Systems with Applications Classification cost : An empirical comparison among traditional classifier,” Expert Syst. Appl., vol. 39, no. 4, pp. 4013–4019,2012.

R. C. Newman: Cybercrime , Identity Theft , and Fraud , “Practicing Safe Internet - Network Security Threats and Vulnerabilities,” InfoSecCD Conference , 2006.

K. Ramakalyani and D. Umadevi, “Fraud Detection of Credit Card Payment System by Genetic Algorithm,” International Journal of Scientific & Engineering Research, vol. 3, no. 7, pp. 1–6, 2012.

T. Tian, J. Zhu, F. Xia, X. Zhuang, and T. Zhang, “Crowd Fraud Detection in Internet Advertising,” International World Wide Web Conference Committee, 2015.

F. Louzada and A. Ara, “Expert Systems with Applications Bagging kdependence probabilistic networks : An alternative powerful fraud detection tool,” Expert Syst. Appl., vol. 39, no. 14, pp. 11583–11592, 2012.

M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic, “Deep learning applications and challenges in big data analytics,” pp. 1–21, 2015.

V. Aggelis and W. P. Sa, “Offline Internet Banking Fraud Detection. ARES,” , 2006.

A. Leung, Z. Yan, and S. Fong , “On designing a flexible e-payment system with fraud detection capability,” Proc. - IEEE Int. Conf. ECommerce Technol. CEC. pp. 236–241, 2004.

S. S. Mhamane and L. M. R. J. Lobo, “Internet banking fraud detection using HMM,” Third Int. Conf. Comput. Commun. Netw. Technol. pp. 1–4 , 2012.




How to Cite

Indrajani, ., Prabowo, H., & Meyliana, . (2016). Learning Fraud Detection from Big Data in Online Banking Transactions: A Systematic Literature Review. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(3), 127–131. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1017

Most read articles by the same author(s)