Presenting a fraud detection model in online banking systems based on credit card transactions using multiple weighted random forest and quadratic model
Subject Areas :Farzaneh Rahmani 1 , Changiz Valmohammadi 2 , Kiomars Fathi Hafshjani 3
1 - گروه مدیریت فناوری اطلاعات، دانشگاه آزاد اسلامی، واحد تهران جنوب، تهران، ایران
2 -
3 -
Keywords: Fraud detection, online banking, credit card transactions, multiple weighted random forest, quadratic model,
Abstract :
With the increasing growth of online banking, banks and financial institutions are more and more inclined to use this technology and its services. Due to the high volume of transactions, it is practically impossible to manage them by human resources. For this purpose, today, approaches based on data mining have come online with the help of banking. In this article, an efficient model for identifying fraudsters in bank card transactions is presented. The proposed method uses the adjacency matrix, placement of non-valued features using weighting, and random forest aggregation algorithm, in each branch of which, by calculating the weight of each branch, the best branch of the decision maker is selected by calculating the cost of the selection model. It can be It also selects the best forest for decision-making using the multiple quadratic model. Thus, we have tested this method on two data sets, the first one had 14 features and the second one had 20 features, and it has been observed that the model of this research compared to the decision tree, support vector machine, neural network, and normal random forest, which is currently the highest The results have shown improvements over any method. Also, the tests show that none of the mentioned methods were able to predict the OOB error and the normal random forest which is able to predict this error performed much weaker than the proposed model..
بنائی، هادی، خوش¬نیت، حسام. (1396). نقش و کاربرد هوش عملیاتی و داده¬کاوی در کشف تقلب برخط. ششمین همایش ملی تجارت و اقتصاد الکترونیک. همایش تخصصی امنیت و اعتماد.
حاتمی¬راد، علی، شهریاری، حمیدرضا. (1397). روش¬ها و راهکارهای شناسایی تقلب در بانک¬داری الکترونیک. فصل¬نامه تازه¬هاي اقتصاد، سال نهم، شماره 134، صص 219 تا 228.
قلی پور سلیمانی، علی، ایمانی، سهیلا. (1400) سیر تکنولوژی در بانکداری.دو ماهنامه مدیریت، شماره. ۱۵۹ صص ۲۲ تا ۲۵.
وثوق، ملیحه، تقوی¬فرد، محمدتقی و البرزی، محمود. (1398). شناسایی تقلب در کارت¬های بانکی با استفاده از شبکه¬های عصبی مصنوعی. فصل¬نامه علمی-پژوهشی مدیریت فناوری اطلاعات دانشگاه تهران، دوره 6، شماره 4، صص 721-746.
Ata, H. A., & Seyrek, I. H. (2009). THE USE OF DATA MINING TECHNIQUES IN DETECTING FRAUDULENT FINANCIAL STATEMENTS: AN APPLICATION ON MANUFACTURING FIRMS. Suleyman Demirel University Journal of Faculty of Economics & Administrative Sciences, 14(2).
Bahnsen, A. C., Aouada, D., & Ottersten, B. (2015). Example-dependent cost-sensitive decision trees. Expert Systems with Applications, 42(19), 6609-6619.
Bahnsen, A. C., Aouada, D., Stojanovic, A., & Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 51, 134-142.
Bansal, M., & Sharma, D. (2021). A novel multi-view clustering approach via proximity-based factorization targeting structural maintenance and sparsity challenges for text and image categorization. Information Processing & Management, 58(4), 102546.
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.
Bose, I., & Mahapatra, R. K. (2001). Business data mining—a machine learning perspective. Information & management, 39(3), 211-225.
Breiman, L. (2011). Random forests. Machine learning, 45, 5-32.
Carta, S., Fenu, G., Recupero, D. R., & Saia, R. (2019). Fraud detection for E-commerce transactions by employing a prudential Multiple Consensus model. Journal of Information Security and Applications, 46, 13-22.
Chandra, V., & Singh, P. (2014). Fuzzy Based High Blood Pressure Diagnosis. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 2(2), 2347–8446.
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.
Dreżewski, R., Sepielak, J., & Filipkowski, W. (2015). The application of social network analysis algorithms in a system supporting money laundering detection. Information Sciences, 295, 18-32.
Eberle, W., & Holder, L. (2007). Anomaly detection in data represented as graphs. Intelligent Data Analysis, 11(6), 663-689.
Fang, W., Li, X., Zhou, P., Yan, J., Jiang, D., & Zhou, T. (2021). Deep learning anti-fraud model for internet loan: where we are going. IEEE Access, 9, 9777-9784.
Hirshman, J., Huang, Y., & Macke, S. (2013). Unsupervised approaches to detecting anomalous behavior in the bitcoin transaction network. Technical report, Stanford University.
JYeonkook J. Kim, Bok Baik b, Sungzoon Cho, “Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning”, Expert Systems With Applications, Vol. 62, Pages 32–43, (2019).
Moreira, M. Â. L., Junior, C. D. S. R., de Lima Silva, D. F., de Castro Junior, M. A. P., de Araújo Costa, I. P., Gomes, C. F. S., & dos Santos, M. (2022). Exploratory analysis and implementation of machine learning techniques for predictive assessment of fraud in banking systems. Procedia Computer Science, 214, 117-124.
Nazeer, I., Prasad, K. D. V., Bahadur, P., Bapat, V., & MJ, K. (2023). Synchronization of AI and Deep Learning for Credit Card Fraud Detection. International Journal of Intelligent Systems and Applications in Engineering, 11(5s), 52-59.
Patidar, R., & Sharma, L. (2011). Credit card fraud detection using neural network. International Journal of Soft Computing and Engineering (IJSCE), 1(32-38).
Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.
Salchenberger, L. M., Cinar, E. M., & Lash, N. A. (1992). Neural networks: A new tool for predicting thrift failures. Decision Sciences, 23(4), 899-916.
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.
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 Systems, 75, 38-48.
Wang, X., Wang, X., Wilkes, M., Wang, X., Wang, X., & Wilkes, M. (2021). A k-nearest neighbour spectral clustering-based outlier detection technique. New Developments in Unsupervised Outlier Detection: Algorithms and Applications, 147-172.
