Abstract
The mounting dependence on computers and the Internet has made online fraud—an increasing concern for both users and law enforcement agencies. People and organizations can often be seen getting involved in a fraud fiasco, resulting in loss of money, property, legal rights, reputation. Detection of fraud is crucial as it deals with protecting oneself from getting duped. The work presented in this paper provides an empirical study and analysis of supervised learning techniques, i.e., logistic regression, nearest neighbors, linear and RBF SVM, decision trees, random forest and naïve Bayes on a benchmark credit card transaction dataset. The performance results have been evaluated and compared to identify the best predictive technique. The techniques have been used to detect whether a given transaction is fraudulent or not.
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References
Abdallah, A., Maarof, M. A., & Zainal, A. (2016). Fraud detection system: A survey. Journal of Network and Computer Applications, 68, 90–113.
Makki, S., Haque, R., Taher, Y., Assaghir, Z., Ditzler, G., Hacid, M. S., & Zeineddine, H. (2017). Fraud analysis approaches in the age of big data-a review of state of the art. In 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS* W) (pp. 243–250). IEEE.
West, Jarrod, & Bhattacharya, Maumita. (2016). Intelligent financial fraud detection: a comprehensive review. Computers & Security, 57, 47–66.
Christopher, B. M. (2006). Pattern recognition and machine learning. Springer.
Baesens, B., Van Vlasselaer, V., & Verbeke, W. (2015). Fraud analytics using descriptive, predictive, and social network techniques: A guide to data science for fraud detection. Wiley.
Kamaruddin, S., & Ravi, V. (2016). Credit card fraud detection using big data analytics: use of psoaann based one-class classification. In Proceedings of the International Conference on Informatics and Analytics, p. 33. ACM.
Santiago, G. P., Pereira, A., & Hirata Jr, R. (2015). A modeling approach for credit card fraud detection in electronic payment services. In Proceedings of the 30th Annual ACM Symposium on Applied Computing (pp. 2328–2331). ACM.
Gómez, J. A., Arévalo, J., Paredes, R., & Nin, J. (2018). End-to-end neural network architecture for fraud scoring in card payments. Pattern Recognition Letters, 105, 175–181.
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.
Quah, J. T., & Sriganesh, M. (2008). Real-time credit card fraud detection using computational intelligence. Expert Systems with Applications, 35(4), 1721–1732.
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.
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.
Mahmud, M. S., Meesad, P., & Sodsee, S. (2016). An evaluation of computational intelligence in credit card fraud detection. In 2016 International Computer Science and Engineering Conference (ICSEC) (pp. 1–6). IEEE.
Auria, L., & Moro, R. A. (2008). Support vector machines (SVM) as a technique for solvency analysis.
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Kumar, A., Gupta, G. (2018). Fraud Detection in Online Transactions Using Supervised Learning Techniques. In: Chakraverty, S., Goel, A., Misra, S. (eds) Towards Extensible and Adaptable Methods in Computing. Springer, Singapore. https://doi.org/10.1007/978-981-13-2348-5_23
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DOI: https://doi.org/10.1007/978-981-13-2348-5_23
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