Bank card fraud detection using artificial neural network

Document Type: Research Paper


1 MSc. in Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Assistant Prof., Allameh Tabatabaii University, Tehran, Iran.

3 Assistant Prof., Science and Research Branch, Islamic Azad University, Tehran, Iran.


There is no accurate data for the bank cards fraud in Iran. But, it seems to be a growing trend in this regard and in the near future it is going to become one of the critical problems in Iran's banking system. Unfortunately, not enough research works have been done in this field in our country and the banking system requires models that are efficient enough to ensure safe use of bank cards. In this paper, after identifying the most common types of bank cards frauds and fraudulent transactions simulation, Artificial Neural Network (ANN) was used for the classification of transactions into two types of legitimate (non-fraud) and fraudulent (suspicious) actions. The proposed model is a Multi-Layer Perceptron (MLP) neural network designed based on the domestic banking system and is able to classify the transactions with more than 99 percent accuracy. Measures of performance calculated in this study are compared with the results of other research models. The results show that the proposed model is quite reliable and valid.


Main Subjects

Alborzi, M., Mohammad Pourzarandi, M. E., Khanbabayi, M. (2010). Using Genetic Algorithm in optimizing decision trees for credit scoring of banks customers. Journal of Information Technology Management, 2(4): 23-38. (in Persian)

Al-Khatib, A. (2011). Detect CNP fraudulent transactions.World of Computer Science and Information Technology Journal, 1(8):326-332.

Azar, A., Ahmadi, P., & Sabt M. V. (2010). Model design for personnel selection with data mining approach (Case Study: A commerce bank of Iran). Journal of Information Technology Management, 2(4): 3-22. (in Persian)

Bentley, P., Kim, J., Jung, G., & Choi, J. (2000). Fuzzy Darwinian detection of credit card fraud. Proceedings of 14th Annual Fall Symposium of the Korean Information Processing Society, Seoul.

Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J., C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems. 50(3): 602-613.

Bolton, R. & Hand, D. (2002). Statistical fraud detection: Areview (with discussion). Statistical Science, 17(3): 235-255.

Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010). The balanced accuracy and its posterior distribution. Proceedings of 20th International Conference on Pattern Recognition, 3121-3124.

Chan, P., Fan, W., Prodromidis, A., & Stolfo, S. (1999). Distributed datamining in credit card fraud detection. IEEE Intelligent Systems, 14: 67-74.

Delamaire, L., Abdou H., & Pointon J. (2009). Credit card fraud and detection techniques: a review. Banks and Bank Systems, 4(2):57-68.

Fan, W., Miller, M., Stolfo, S., Lee, W. & Chan, P. (2004). Using artificial anomalies to detect unknown and known network intrusions. Knowledge and Information Systems, 6 (5): 507-527.

Fawcett, T. (2003). ROC Graphs: Notes and practical considerations for data mining researchers. CA: Intelligent Enterprise Technologies Laboratory of Hewlett-Packard Company.

Gadi, M. F. A., Wang, X., Pereira, A. & Lago, D. (2008). Credit card fraud detection with artificial immune system. Lecture Notes in Computer Science, 5132: 119-131.

Ghasemi, A. R. & Asgharizadeh, E. (2014). Presenting a hybrid ANN-MADM Method to Define Excellence Level of Iranian Petrochemical Companies. Journal of Information Technology Management, 6(2): 267-284.
(in Persian)

Ghosh, S. & Reilly, D. (1994). Credit card fraud detection with a neural-network. Proceedings of 27th Hawaii International Conference on System Sciences, 3:621-630.

Gullapalli, V., Kalli, S., & Vijay, A. (2012).Credit card transaction fraud and mitigating trends: Latest credit card fraud trends and mitigation methodologies. Retrieved from files/resource/pdf/Credit_Card_Transaction_Fraud_and_Mitigation_Trends.pdf‎.

Hatami Rad, A. & Shahriari, H. R. (2012). E-Banking fraud detection methods and solutions.Journal of Economics News, 134: 219-228. (in Persian)

Huang, R., Tawfik, H., Nagar, A.K. (2010). A novel hybrid artificial immune inspired approach for online break-in fraud detection. Procedia Computer Science,1(1): 2733-2742.

Krivko, M. (2010). A hybrid model for plastic card fraud detection system. Expert System with Application, 37(8): 6070-6076.

Kundu, A., Panigrahi, S., Sural, S. & Majumdar, A. K. (2009). BLAST-SSAHA hybridization for credit card fraud detection. IEEE Transactions on Dependable and Secure Computing, 6(4): 309-315.

Leonard, K. J. (1995). The development of a rule based expert system model for fraud alert in consumer credit.European Journal of Operational Research, 80(2): 350-356.

Marzban, C.(2004).The ROC curve and the area under it as performance measures. Weather and Forecasting, 19(6): 1106-1114.

Mohaghar, A., Lucas, C., Hoseini, F., & Monshi, A. A. (2009). Use of business intelligence as a strategic information technology in banking: fraud discovery & detection. Journal of Information Technology Management, 1(1): 105-120. (in Persian)

Nasiri, N. & Minayi, B. (2011). Data mining methods for credit card fraud detection. 1st International conference on E-Citizen & Cellphone, 28-29 Feb., Tehran. (in Persian)

Nobarzad, A. R. (2013). Bank card fraud detection using Genetic Algorithm and Scatter Search. Iran Banking Institute, Tehran. (in Persian)

Noriega, L. (2005).Multilayer Perceptron tutorial. School of Computing, Staffordshire University, UK.

Ogwueleka, F. N. (2011). Datamining application in credit card fraud detection system. Journal of Engineering Science and Technology, 6(3): 311-322.

Paasch, C. A. W. (2008). Credit card fraud detection using artificial neural network tuned by genetic algorithms (Doctoral dissertation). Retrieved from the HKUST Institutional Repository (Thesis ISMT 2008 Paasch).

Panigrahi, S., Kundu, A., Sural, S., & Majumdar, A., K.(2009). Credit card fraud detection: A fusion approach using Dempster-Shafer theory and Bayesian learning. Information Fusion. 10(4): 354-363.

Patidar, R. & Sharma L. (2011). Credit card fraud detection using neural network. International Journal of Soft Computing and Engineering, 1 (NCAI2011): 2231-2307.

Phua, C., Lee, V., Smith, K., & Gayler, R. (2005). A comprehensive survey of data mining-based fraud detection research. Artificial Intelligence Review.

Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. Computing Research Repository, abs/1009. 6119.

Powers, D. (2011). Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation. Journal of Machine Learning Technologies, 2(1): 37-63.

Pulina, M. & Paba, A. (2010). A discrete choice approach to model credit card fraud. (No. 20019). University Library of Munich: MPRA.

Robinson S. (2004). Simulation: The practice of model development and use. England: John Wiley & Sons.

Sakharova, I. (2012). Payment card fraud: Challenges and solutions. Proceedings of IEEE International Conference on Intelligence and Security Informatics (ISI), 227-234.

Shahrabi, J. (2013). Data Mining. Tehran, Amirkabir University Branch of Iranian Academic Center for Education Culture and Research. (in Persian)

Shen, A., Tong, R. & Deng, Y. (2007). Application of classification model on credit card fraud detection. Proceedings of International Conference on Services Systems and Services Management (ICSSSM). 9-11 June 2007, Chengdu.

Srivastava, A., Kundu, A. & Sural, S. (2008). Credit card fraud detection using hidden markov model. IEEE Transactions on Dependable and Secure Computing, 5(1): 37-48.

Tang, Y., Zhang, Y.Q., Chawla, N.V. & Krasse, S. (2002). SVMs modeling for highly imbalanced classification. Journal of Latex Class Files, 1(11):1-9.

Toloie Eshlaghi, A. & Haghdoust, Sh. (2007). Stock price prediction modelling using neural networks and comparison with mathematical prediction methods. Journal of Economic Research, 25: 237-252. (in Persian)