MAKİNE ÖĞRENME ALGORİTMALARIYLA SAHTEKÂRLIK ALGILAMA: BİR MOBİL ÖDEME SİSTEMİ ÇALIŞMASI

Yıl 2022, Cilt: 18 Sayı: 3, 895 - 911, 30.09.2022

Özlem Güven Serkan Aras

https://doi.org/10.17130/ijmeb.979302

Öz

With the developing technology, mobile payment systems have become increasingly popular. In the public transport industry, this system has convenient to the sector in terms of purchasing, using, carrying and storing tickets. One of the greatest challenges encountered in the mobile payment system in this sector is fraud. Fraud reduces customer satisfaction, reduces snow margins and causes severe costs for the company. Therefore, it is very important to detect and prevent fraudsters. This study is based on users using a real mobile ticketing application in USA/Kansas, a customer of Kentkart, which has a smart public transportation system. An automatic and intelligent detection system was developed using a machine learning algorithm to detect whether the users in question are fraudulent or not. For this system, the historical profiles of the variables that represent a user that the risky behavior are created. These profiles are classified using Random Forest, Support Vector Machines, Logistic Regression, K-Nearest Neighbor and Naive Bayes machine learning techniques and results are combined with simple ensemble learning methods. Users classified as frauds are automatically blacklisted in accordance with the company's management policy. Thus, the fraud costs that these users caused the company have been reduced.

Anahtar Kelimeler

Classification, Ensemble Learning, Fraud Detection, Machine Learning, Mobile Payment.

FRAUD DETECTION BY MACHINE LEARNING ALGORITHMS: A CASE FROM A MOBILE PAYMENT SYSTEM

Öz

With the developing technology, mobile payment systems have become increasingly popular. In the public transport industry, this system has convenient to the sector in terms of purchasing, using, carrying and storing tickets. One of the greatest challenges encountered in the mobile payment system in this sector is fraud. Fraud reduces customer satisfaction, reduces snow margins and causes severe costs for the company. Therefore, it is very important to detect and prevent fraudsters. This study is based on users using a real mobile ticketing application in USA/Kansas, a customer of Kentkart, which has a smart public transportation system. An automatic and intelligent detection system was developed using a machine learning algorithm to detect whether the users in question are fraudulent or not. For this system, the historical profiles of the variables that represent a user that the risky behavior are created. These profiles are classified using Random Forest, Support Vector Machines, Logistic Regression, K-Nearest Neighbor and Naive Bayes machine learning techniques and results are combined with simple ensemble learning methods. Users classified as frauds are automatically blacklisted in accordance with the company's management policy. Thus, the fraud costs that these users caused the company have been reduced.

Anahtar Kelimeler

Classification, Ensemble Learning, Fraud Detection, Machine Learning, Mobile Payment.

Kaynakça

• A Liaw and M Wiener. Classification and regression... - Google Akademik. (n.d.). Retrieved July 28, 2021, from https://scholar.google.com.tr/scholar?hl=tr&as_sdt=0%2C5&q=A+Liaw++and+M+Wiener.+Classification+and+regression+by+randomForest.+R+news.+2002%3B+2%283%29%2C+18-22.&btnG=
• Abdallah, A., Maarof, M. A., & Zainal, A. (2016). Fraud detection system: A survey. Journal of Network and Computer Applications, 68, 90–113.
• Abe, S. (2005). Support vector machines for pattern classification (Vol. 2). Springer.
• Adewumi, A. O., & Akinyelu, A. A. (2017). A survey of machine-learning and nature-inspired based credit card fraud detection techniques. International Journal of System Assurance Engineering and Management, 8(2), 937–953.
• Aras, S., & Gulay, E. (2017). A new consensus between the mean and median combination methods to improve forecasting accuracy. Serbian Journal of Management, 12(2), 217–236.
• Awoyemi, J. O., Adetunmbi, A. O., & Oluwadare, S. A. (2017). Credit card fraud detection using machine learning techniques:
• A comparative analysis. 2017 International Conference on Computing Networking and Informatics (ICCNI), 1–9.
• Behdad, M., Barone, L., Bennamoun, M., & French, T. (2012). Nature-inspired techniques in the context of fraud detection. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 1273–1290.
• Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, 17(3), 235–255.
• Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees–crc press. Boca Raton, Florida.
• Camenisch, J., Piveteau, J.-M., & Stadler, M. (1996). An efficient fair payment system. Proceedings of the 3rd ACM Conference on Computer and Communications Security, 88–94.
• Cao, J., Kwong, S., Wang, R., Li, X., Li, K., & Kong, X. (2015). Class-specific soft voting based multiple extreme learning machines ensemble. Neurocomputing, 149, 275–284.
• Chan, P. K., Fan, W., Prodromidis, A. L., & Stolfo, S. J. (1999). Distributed data mining in credit card fraud detection. IEEE Intelligent Systems and Their Applications, 14(6), 67–74.
• Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1), 1–13.
• Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
• Dahlberg, T., Mallat, N., Ondrus, J., & Zmijewska, A. (2008). Past, present and future of mobile payments research: A literature review. Electronic Commerce Research and Applications, 7(2), 165–181.
• De Menezes, L. M., Bunn, D. W., & Taylor, J. W. (2000). Review of guidelines for the use of combined forecasts. European Journal of Operational Research, 120(1), 190–204.
• Dewan, S. G., & Chen, L. (2005). Mobile payment adoption in the US: A cross-industry, crossplatform solution. Journal of Information Privacy and Security, 1(2), 4–28.
• Domingos, P., & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29(2), 103–130.
• Erbacher, R. F., Walker, K. L., & Frincke, D. A. (2002). Intrusion and misuse detection in large-scale systems. IEEE Computer Graphics and Applications, 22(1), 38–47.
• Fujimura, K., & Nakajima, Y. (1998). General-purpose Digital Ticket Framework. USENIX Workshop on Electronic Commerce, 177–186.
• Genuer, R., Poggi, J.-M., & Tuleau-Malot, C. (2010). Variable selection using random forests. Pattern Recognition Letters, 31(14), 2225–2236.
• Ghosh, S., & Reilly, D. L. (1994). Credit card fraud detection with a neural-network. System Sciences, 1994. Proceedings of the Twenty-Seventh Hawaii International Conference On, 3, 621–630.
• Hashemi, M. R., & Soroush, E. (2006). A secure m-payment protocol for mobile devices. 2006 Canadian Conference on Electrical and Computer Engineering, 294–297.
• Hassinen, M., Hyppönen, K., & Haataja, K. (2006). An open, PKI-based mobile payment system. International Conference on Emerging Trends in Information and Communication Security, 86–100.
• Hwang, Y.-S., Han, S.-W., & Nam, T.-Y. (2006). Secure rejoining scheme for dynamic sensor networks. International Conference on Emerging Trends in Information and Communication Security, 101–114.
• Jyothsna, V., Prasad, R., & Prasad, K. M. (2011). A review of anomaly based intrusion detection systems. International Journal of Computer Applications, 28(7), 26–35.
• Karnouskos, S., Hondroudaki, A., Vilmos, A., & Csik, B. (2004). Security, trust and privacy in the secure mobile payment service. 3rd International Conference on Mobile Business, 35.
• La Polla, M., Martinelli, F., & Sgandurra, D. (2012). A survey on security for mobile devices. IEEE Communications Surveys & Tutorials, 15(1), 446–471.
• Lewis, D. D. (1998). Naive (Bayes) at forty: The independence assumption in information retrieval. European Conference on Machine Learning, 4–15.
• Liao, Y., & Vemuri, V. R. (2002). Use of k-nearest neighbor classifier for intrusion detection. Computers & Security, 21(5), 439–448.
• Linck, K., Pousttchi, K., & Wiedemann, D. G. (2006). Security issues in mobile payment from the customer viewpoint.
• Melo-Acosta, G. E., Duitama-Muñoz, F., & Arias-Londoño, J. D. (2017). Fraud detection in big data using supervised and semi-supervised learning techniques. 2017 IEEE Colombian Conference on Communications and Computing (COLCOM), 1–6.
• Pirker, M., & Slamanig, D. (2012). A framework for privacy-preserving mobile payment on security enhanced arm trustzone platforms. 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications, 1155–1160.
• Qin, Z., Sun, J., Wahaballa, A., Zheng, W., Xiong, H., & Qin, Z. (2017). A secure and privacy-preserving mobile wallet with outsourced verification in cloud computing. Computer Standards & Interfaces, 54, 55–60.
• Ryali, S., Supekar, K., Abrams, D. A., & Menon, V. (2010). Sparse logistic regression for whole-brain classification of fMRI data. NeuroImage, 51(2), 752–764.
• Steiner, J. G., Neuman, B. C., & Schiller, J. I. (1988). Kerberos: An Authentication Service for Open Network Systems. Usenix Winter, 191–202.
• Tharwat, A. (2016). Principal component analysis-a tutorial. International Journal of Applied Pattern Recognition, 3(3), 197–240.
• Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.
• 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, S. (2010). A comprehensive survey of data mining-based accounting-fraud detection research. 2010 International Conference on Intelligent Computation Technology and Automation, 1, 50–53.
• Wang, Y., Hahn, C., & Sutrave, K. (2016). Mobile payment security, threats, and challenges. 2016 Second International Conference on Mobile and Secure Services (MobiSecServ), 1–5.
• Weinberger, K. Q., & Saul, L. K. (2009). Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10(2).
• Zareapoor, M., Seeja, K. R., & Alam, M. A. (2012). Analysis on credit card fraud detection techniques: Based on certain design criteria. International Journal of Computer Applications, 52(3).