Fraud Detection in Credit Card Transactions; Using Parallel Processing of Anomalies in Big Data

Document Type : Research Paper


1 Associate Prof, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran

2 Ph.D. Candidate in Information Technology Management, Allameh Tabataba’i University, Tehran, Iran

3 Prof, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran

4 Prof, Sheffield Hallam University, Sheffield, England


In parallel to the increasing use of electronic cards, especially in the banking industry, the volume of transactions using these cards has grown rapidly. Moreover, the financial nature of these cards has led to the desirability of fraud in this area. The present study with Map Reduce approach and parallel processing, applied the Kohonen neural network model to detect abnormalities in bank card transactions. For this purpose, firstly it was proposed to classify all transactions into the fraudulent and legal which showed better performance compared with other methods. In the next step, we transformed the Kohonen model into the form of parallel task which demonstrated appropriate performance in terms of time; as expected to be well implemented in transactions with Big Data assumptions.


Main Subjects

Axelsson, S. (2000). The Base-Rate Fallacy and the Difficulty of Intrusion Detection. ACM Trans. Information and System Security. 3(3): 186-205.
Bai, M., Wang, X., Xin, J. & Wang, G. (2016). An efficient algorithm for distributed density-based outlier detection on big data. Neurocomputing. (181): 19-28.
Bermúdez, L., Pérez, J. M., Ayuso, M., Gómez, E. & Vázquez, F.J. (2008). A Bayesian Dichotomous, Model with asymmetric link for fraud in insurance. Insurance: Mathematics and Economics. 42(2): 779-786.
Bhusari, V. & Patil, S. (2011). Study of Hidden Markov Model in Credit Card Fraudulent Detection. International Journal of Computer Applications. 20(5): 33-36.
Bishop, C. M. (2006). Pattern recognition and machine learning. Information Science and Statistics: Springer. Singapore.
Correa Bahnsen, A,. Stojanovic, A., Aouada, D. & Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems With Applications, 51: 134-142.
Correa Bahnsen, A,. Stojanovic, A., Aouada, D., Ottersten, B.(2014). Improving credit card fraud detection with calibrated probabilities. In Proceedings of the fourteenth siam international conference on data mining. Detroit, USA.
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 Applications, 41(10): 4915-4928.
Dean, J. & Ghemawat, S .(2004). MapReduce: simplified data processing on large clusters. OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation, 137-150.
Duman, E. & Ozcelik, M.H. (2011). Detecting credit card fraud by genetic algorithm and scatter search. Expert Systems with Applications, 38(10): 13057-13063.
Epaillard, E. & Bouguila, N. (2016). Proportional data modeling with hidden Markov models based on generalized Dirichlet and Beta-Liouville mixtures applied to anomaly detection in public areas. Pattern Recognition, 55: 125-136.
González, P. C. & Velásquez, J. D. (2013). Characterization and detection of tax payers with false invoices using data mining techniques. Expert systemps with applications, 40(5): 1427-1436.
Halvaiee, N.S. & Akbari, M.K. (2014). A novel model for credit card fraud detection using Artificial Immune Systems. Applied soft computing, 24: 40-49.
Hollmén, J., Tresp, V. & Simula, O. (1999). A self-organizing map for clustering probabilistic models. Proceedings of the Ninth International Conference on Artificial Neural Networks,  7-10 Sept.
Huang, J., Zhu, Q., Yang, L. & Feng, J. (2016). A non-parameter outlier detection algorithm based on Natural Neighbor. Knowledge-Based Systems, 92: 71-77.
Huang, S. Y., Tsaih, R. H. & Yu, F. (2014). Topological pattern discovery and feature extraction for fraudulent financial reporting. Expert systems with applications, 41(9): 4360-4372.
Jha, S., Guillen, M. &  Westland, J. C. (2012).Employing transaction aggregation strategy to detect credit card fraud. Expert Systems with Applications, 39(16): 12650-12657.
Kohonen, T. (1990). The self-organizing map. In Proceedings of the IEEE, 78 (9): 1464-1480.
Kotu, V., Deshpande, B. (2015). Predictive analytics and data mining; Concepts and practice with RapidMiner. Morgan Kaufmann. San Francisco.
Leskovec, J., Rajaraman, A. & Ullm, J.D. (2014). Mining of Massive Datasets. Cambridge University Press.
Loshin, D. (2013). Big data analytucs; from strategic planning to enterprise integration with tools, technigues, NoSQL, and graph. Morgan Kaufmann, London.
Minegishi, T. & Niimi, A. (2011). Proposal of Credit Card Fraudulent Use Detection by Online-type Decision Tree Construction and Verification of Generality. International Journal for Information Security Research, 1(4): 229-235.
Mishra, J. S., Panda, S. &  Mishra, A. K. (2013). A Novel Approach for Credit Card Fraud Detection Targeting the Indian Market. International Journal of Computer Science Issues, 10(3): 172-179.
Nasiri, N. & Minayi, B. (2011). Data mining methods for credit card fraud detection. 1st International conference on E-Citizen & Cellphone. Tehran: Frb 28-29. (in Persian)
Ngai, E. W., Hu, Y., Wong, Y. H., Chen, Y. & Sun, X. (2011).  The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3): 569-559.
Olszewski, D. (2014). Fraud detection using self-organizing map visualizing the user profiles. Knowledge-Based Systems, 70: 324-334.
Patidar, R. & Sharma, L. (2011). Credit Card Fraud Detection Using Neural Network. International Journal of Soft Computing and Engineering, (1): 32-37.
Phua, C., Lee, V., Smith, K. & Gayler, R. (2005).A Comprehensive Survey of Data Mining-based Fraud Detection Research. Artificial Intelligence Review, 1-14.
Powers, D. M.W. (2011). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness,correlation. Journal of Machine Learning Technologies, 2 (1): 37-63.
Qibei, L. & Chunhua, J. (2011). Research on Credit Card Fraud Detection Model Based on Class Weighted Support Vector Machine.  Journal of Convergence Information Technology, 6(1): 62-68.
Quah, J. T.S. & Sriganesh, M. (2008). Real-time credit card fraud detection using computational intelligence. Expert Systems with Applications, 35(4): 1721-1732.
Raj, B. E. & Portia, A. (2011). Analysis on Credit Card Fraud Detection Methods. International Conference on Computer, Communication and Electrical Technology, March.
Rama Kalyani, K. & Uma Devi, D. (2012). Fraud Detection of Credit Card Payment System by Genetic Algorithm. International Journal of Scientific & Engineering Research, 3(7): 1-6.
Rojas, R. (1996). Neural Networks. Berlin: Springer-Verlag.
Srivastava, A., Kundu, A., Sural, S. & Majumdar, A. K. (2008). Credit Card Fraud Detection Using Hidden Markov Model. IEEE transactions on dependable and secure computing, 5(1): 37-48.
Swets, J.A. (1996). Signal Detection Theory and ROC Analysis in Psychology and Diagnostics: Collected Papers. Lawrence Erlbaum Associates, Mahwah, N.J.
Tripathi, K.K. & Ragha, L. (2013). Hybrid Approach for Credit Card Fraud Detection. International Journal of Soft Computing and Engineering, 3(4):  8-11.
Vosough, M., Taghavifard, M.T. & Alborzi, M. (2015). Bank card fraud detection using artificial neural network. Journal of Information Technology Management, 6(4): 721-746. (in Persian)
Whitrow, C., Hand, D.J., Juszczak, P. & Weston, D. (2008). Transaction aggregation as a strategy for credit card fraud detection. Data mining and knowledge discovery, 18(1): 30-55.
Zareapoor, M., Seeja, K.R. & Alam, M. A.(2012). Analysis of credit card fraud detection techniques: based in certain design criteria. International journal of computer applications, 52(3): 35-42.
Zareapoor, M. & Shamsolmoali, P. (2015). Application of credit card fraud detection: based on bagging ensemble classifier. Procedia computer science, 48: 679-685.
Zaslavsky, V. & Strizhak, A. (2006). Credit card fraud detection using self organizing maps. International Journal of Information & Security, 18: 48-63.