A Novel Fraud Detection Scheme for Credit Card Usage Employing Random Forest Algorithm Combined with Feedback Mechanism

Document Type : Special Issue: Big Data Analytics and Management in Internet of Things.


Department of Information Technology, Easwari Engineering College, Chennai, India.


As electronic commerce has gained widespread popularity, payments made for users' transactions through credit cards also gained an equal amount of reputation. Whenever shopping through the web is made, the chance for the occurrence of fraudulent activities are escalating. In this paper, we have proposed a three-phase scheme to detect fraudulent activities. A profile for the card users based on their behavior is created by employing a machine learning technique in the second phase extraction of a precise communicative pattern for the card users depending upon the accumulated transactions and the user's earlier transactions. A collection of classifiers are then trained based on all behavioral pattern. The trained collection of classifiers are then used to detect the fraudulent online activities that occurred. If an emerging transaction is fraudulent, feedback is taken, which resolves the drift's difficulty in the notion. Experiments performed indicated that the proposed scheme works better than other schemes.


Bahnsen, A. C., Aouada, D., Stojanovic, A., &Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications51, 134-142.
Behera, T. K., &Panigrahi, S. (2015, May). Credit card fraud detection: a hybrid approach using fuzzy clustering & neural network. In 2015 Second International Conference on Advances in Computing and Communication Engineering (pp. 494-499). IEEE.
Breiman, L. (2001). Random forests. Machine learning45(1), 5-32.
BrzeziƄski, D. (2010). Mining data streams with concept drift. Cs Put Pozna89.
Chen, R. C., Luo, S. T., Liang, X., & Lee, V. C. (2005, October). Personalized approach based on SVM and ANN for detecting credit card fraud. In 2005 International Conference on Neural Networks and Brain (Vol. 2, pp. 810-815). IEEE.
Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., &Bontempi, G. (2017). Credit card fraud detection: a realistic modeling and a novel learning strategy. IEEE transactions on neural networks and learning systems29(8), 3784-3797.
Dal Pozzolo, A., Caelen, O., Le Borgne, Y. A., Waterschoot, S., &Bontempi, G. (2014). Learned lessons in credit card fraud detection from a practitioner perspective. Expert systems with applications41(10), 4915-4928.
Flitman, A. M. (1997). Towards analysing student failures: neural networks compared with regression analysis and multiple discriminant analysis. Computers & Operations Research24(4), 367-377.
Ganji, V. R., &Mannem, S. N. P. (2012). Credit card fraud detection using anti-k nearest neighbor algorithm. International Journal on Computer Science and Engineering4(6), 1035-1039.
Gurjar, R. N., Sharma, N., &Wadhwa, M. (2014, February). Finding outliers using mutual nearness based ranks detection algorithm. In 2014 International Conference on Reliability Optimization and Information Technology (ICROIT) (pp. 141-144). IEEE.
Jiang, C., Song, J., Liu, G., Zheng, L., & Luan, W. (2018). Credit card fraud detection: A novel approach using aggregation strategy and feedback mechanism. IEEE Internet of Things Journal5(5), 3637-3647.
Liu, Q., Li, P., Zhao, W., Cai, W., Yu, S., & Leung, V. C. (2018). A survey on security threats and defensive techniques of machine learning: A data driven view. IEEE access6, 12103-12117.
Malekian, D., &Hashemi, M. R. (2013, August). An adaptive profile based fraud detection framework for handling concept drift. In 2013 10th International ISC Conference on Information Security and Cryptology (ISCISC) (pp. 1-6). IEEE.
Masud, M., Gao, J., Khan, L., Han, J., &Thuraisingham, B. M. (2010). Classification and novel class detection in concept-drifting data streams under time constraints. IEEE Transactions on Knowledge and Data Engineering23(6), 859-874.
NilsonReport, 2019, The Nilson Report: https:// www.nilsonreport.com/ upload/ content promo/ The Nilson Report 01-17-2019.pdf .
Panigrahi, S., Kundu, A., Sural, S., &Majumdar, A. K. (2009). Credit card fraud detection: A fusion 363.
Quah, J. T., &Sriganesh, M. (2008). Real-time credit card fraud detection using computational intelligence. Expert systems with applications35(4), 1721-1732.
Randhawa, K., Loo, C. K., Seera, M., Lim, C. P., & Nandi, A. K. (2018). Credit card fraud detection using AdaBoost and majority voting. IEEE access6, 14277-14284.
Shen, A., Tong, R., & Deng, Y. (2007, June). Application of classification models on credit card fraud detection. In 2007 International conference on service systems and service management (pp. 1-4). IEEE.
Srivastava, A., Kundu, A., Sural, S., &Majumdar, A. (2008). Credit card fraud detection using hidden Markov model. IEEE Transactions on dependable and secure computing5(1), 37-48.
Valecha, H., Varma, A., Khare, I., Sachdeva, A., &Goyal, M. (2018, November). Prediction of consumer behaviour using random forest algorithm. In 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-6). IEEE.
Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., &Baesens, B. (2015). APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems75, 38-48.
Wong, M. A., &Hartigan, J. A. (1979). Algorithm as 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics)28(1), 100-108.
Wei, Q., Yang, Z., Junping, Z., & Yong, W. (2009, August). Mining multi-label concept-drifting streams using ensemble classifiers. In 2009 Sixth international conference on fuzzy systems and knowledge discovery (Vol. 5, pp. 275-279). IEEE.
Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2009). Transaction aggregation as a strategy for credit card fraud detection. Data mining and knowledge discovery18(1), 30-55.
Ye, N., Zhang, Y., &Borror, C. M. (2004). Robustness of the Markov-chain model for cyber-attack detection. IEEE Transactions on Reliability53(1), 116-123.