Abstract
Fraud is a costly business problem which causes every organization to face huge loss. Fraud may lead to risk of financial loss and loss of the confidence of customers and stakeholders of the company. Cyber security teams and internal audit departments of most of the organizations try to monitor such fraudulent activities using traditional rule-based fraud detection systems. However, with the rapid adaptation of online financial transactions, it is more difficult to identify fraudulent activities by static methods and via data analysis. Further, as traditional rule-based fraud detection systems cannot dynamically adjust the rule set based on the behavioral changes of the fraudsters, there is a high possibility of detecting false positive alerts. The aim of this paper is to review selected machine learning techniques where it can be used to develop a fraud detection system which identifies fraudulent activities in financial transactions.
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A.C. Bahnsen et al., Feature engineering strategies for credit card fraud detection. Expert Syst. Appl. 51, 134–142 (2016). https://doi.org/10.1016/j.eswa.2015.12.030
K. Bui, 4 Reasons Why Fraud Prevention Needs to Move Beyond Rules Based Engines (Feedzai, 2016). Available at: https://feedzai.com/blog/4-reasons-why-fraud-prevention-needs-to-move-beyond-rules-based-engines/. Accessed: 3 Nov 2018
R. Zainal, A. Md. Som, A review on computer technology applications in fraud detection and prevention. ResearchGate (2017). Available at: https://www.researchgate.net/publication/323392316_A_REVIEW_ON_COMPUTER_TECHNOLOGY_APPLICATIONS_IN_FRAUD_DETECTION_AND_PREVENTION
B. Vijay, J. Swathi, Credit card fraud detection analysis. Int. J. Res. Appl. Sci. Eng. Technol. (IJRASET) 4, 4 (2016)
A. Patcha, J.-M. Park, An overview of anomaly detection techniques: existing solutions and latest technological trends. Comput. Netw. 51(12), 3448–3470 (2007). https://doi.org/10.1016/j.comnet.2007.02.001
J. McGibney, S. Hearne, An approach to rules based fraud management in emerging converged networks (2004)
How Machine Learning Facilitates Fraud Detection? Maruti Techlabs, 5 June 2017. Available at: https://www.marutitech.com/machine-learning-fraud-detection/. Accessed: 2 Nov 2018
Y. Kou et al. Survey of fraud detection techniques, in IEEE International Conference on Networking, Sensing and Control, 2004. IEEE International Conference on Networking, Sensing and Control, 2004 (IEEE, Taipei, Taiwan, 2004), pp. 749–754. https://doi.org/10.1109/icnsc.2004.1297040
T. Milo, S. Novgorodov, W.C. Tan, Interactive rule refinement for fraud detection. OpenProceedings.org (2018). https://doi.org/10.5441/002/edbt.2018.24
B. Baesens, V. Van Vlasselaer, W. Verbeke, Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques (Wiley, Hoboken, NJ, 2015)
T.M. Amarasinghe, A.C. Aponso, Fraud detection solution for financial transactions with artificial neural network (2018)
C. Mishra, D.L. Gupta, R. Singh, Credit Card Fraud Identification Using Artificial Neural Networks (no date), p. 9
A. Gulati et al., Credit card fraud detection using neural network and geolocation, in IOP Conference Series: Materials Science and Engineering, vol. 263 (2017), p. 042039. https://doi.org/10.1088/1757-899x/263/4/042039
Z. Zhang et al., A model based on convolutional neural network for online transaction fraud detection. Secur. Commun. Netw. (2018). https://doi.org/10.1155/2018/5680264
Fraud Detection Technique in Credit Card Transactions using Convolutional Neural Network. ResearchGate (no date). Available at: https://www.researchgate.net/publication/321383884_Fraud_Detection_Technique_in_Credit_Card_Transactions_using_Convolutional_Neural_Network. Accessed: 5 Oct 2018
A. Chouiekh, E.H.I.E. Haj, ConvNets for fraud detection analysis. Proc. Comput. Sci. (2018). https://doi.org/10.1016/j.procs.2018.01.107
Y. Ando, H. Gomi, H. Tanaka, in Detecting Fraudulent Behavior Using Recurrent Neural Networks (2016), p. 6
S. Wang et al., Session-based fraud detection in online E-commerce transactions using recurrent neural networks, in Machine Learning and Knowledge Discovery in Databases, ed. by Y. Altun, et al. (Springer, Cham, 2017), pp. 241–252. https://doi.org/10.1007/978-3-319-71273-4_20
S. Maes, T. Karl, V. Bram, Credit card fraud detection using bayesian and neural networks. ResearchGate (2002). Available at: https://www.researchgate.net/profile/Karl_Tuyls/publication/248809471_Credit_Card_Fraud_Detection_Applying_Bayesian_and_Neural_networks/links/0deec52519708c5f7a000000/Credit-Card-Fraud-Detection-Applying-Bayesian-and-Neural-networks.pdf
R. Chen et al., Novel questionnaire-responded transaction approach with SVM for credit card fraud detection (2005)
V.B. Nipane et al., Fraudulent detection in credit card system using SVM & decision tree. Int. J. Sci. Dev Res. (IDSDR) 1(5), 5 (2016)
S.S. Dhok, D.G.R. Bamnote, Credit card fraud detection using hidden Markov model. Int. J. Adv. Res. Comput. Sci. 5(1), 37–48 (2010)
G. Mhatre et al., Credit card fraud detection using hidden markov model. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 5, 3 (2014)
V. Bhusari, S. Patil, Application of hidden markov model in credit card fraud detection. Int. J. Distrib. Parallel Syst. 2(6), 203–211 (2011). https://doi.org/10.5121/ijdps.2011.2618
Credit card fraud detection using anti k-nearest algorithm. ResearchGate (no date). Available at: https://www.researchgate.net/publication/236962626_credit_card_fraud_detection_using_anti_k-nearest_algorithm. Accessed: 5 Oct 2018
N. Malini, M. Pushpa, Analysis on credit card fraud identification techniques based on KNN and outlier detection, in 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB). 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB) (2017), pp. 255–258 https://doi.org/10.1109/aeeicb.2017.7972424
M. Zanin et al., Credit card fraud detection through parenclitic network analysis. Complexity (2018). https://doi.org/10.1155/2018/5764370
S.J. Stolfo et al., Credit card fraud detection using meta-learning: issues and initial results (no date), p. 8
Acknowledgements
I would like to express my sincere gratitude to everyone who were behind me from the beginning to the completion of the paper. Firstly, I am very much thankful to my supervisor Mr. Achala Chathuranga Aponso for the unwavering support, guidance, and insight throughout this period. I acknowledge with thanks for the encouragement received from Ms. Naomi Krishnarajah, Dean of the Informatics Institute of Technology. Special thanks to Mr. Thushara Madushanka Amarasinghe and Ms. Minoli De Silva for all the support and ideas. Last but not least, I would like to express my love and gratitude to all my family members, especially to my father and mother for the constant support toward my studies and for believing me. I am sure that this would have not been possible without the valuable contribution of each one of you.
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Chandradeva, L.S., Amarasinghe, T.M., De Silva, M., Aponso, A.C., Krishnarajah, N. (2020). Monetary Transaction Fraud Detection System Based on Machine Learning Strategies. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Fourth International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 1041. Springer, Singapore. https://doi.org/10.1007/978-981-15-0637-6_33
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DOI: https://doi.org/10.1007/978-981-15-0637-6_33
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