Abstract
Ever since the inception of online transactions, it has positively impacted the ease of business by making money transactions straightforward and secure, irrespective of location or amount of money. However, along with the increase in online transactions, the number of fraudulent transactions also increased. With the rapid growth in technology in the current environment, fraudsters are creating new methods to conduct these fraudulent transactions, which seems legitimate. Therefore, there is an ever-growing need to curb these incidents using real-time detection and reporting. This chapter explores the different techniques such as the ANNs or Artificial Neural Networks, CNNs or Convolutional neural networks, Rule-based methods(RBM), Hidden Markov Models(HMM), Autoencoders, and much more, in machine learning and deep learning. Most of the datasets for these models are not accessible to the public because of the privacy concerns of the financial institutes. We also assess various parameters like precision, accuracy, and recall of these solutions to make a comprehensive study. The chapter is concluded with the latest improvements and the future prospects to keep track of these fraudulent transactions.
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References
Sohony, I., Pratap, R., Nambiar, U.: Ensemble learning for credit card fraud detection. In: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data (CoDS-COMAD’18). Association for Computing Machinery, New York, NY, USA, pp. 289–294 (2018). https://doi.org/10.1145/3152494.3156815
Singh, P., Singh, M.: Fraud detection by monitoring customer behavior and activities. Int. J. Comput. Appl. 111(11) (2015). https://doi.org/10.1.1.695.5814
Maniraj, S.P., Saini, A., Ahmed, S., Sarkar, S.: Credit card fraud detection using machine learning and data science. Int. J. Eng. Res. Technol. (IJERT) 8(9) (2019)
Fawcett, T., Haimowitz, I., Provost, F., Stolfo, S.: Ai approaches to fraud detection and risk management. AI Mag. 19(2), 107–107 (1998)
Author, A.-B.: Contribution title. In: 9th International Proceedings on Proceedings, pp. 1–2. Publisher, Location (2010)
Annie Brown: AI Changing the Entertainment World. www.forbes.com/sites/anniebrown/2021/07/13/making-the-youtube-algorithm-less-elusive-with-the-help-of-Gregory-chase-a-creator-with-10m-subscribers/?sh=ac0b3bcd681f. Accessed 4 Sept 2021
Marr, B.: The amazing ways YouTube uses artificial intelligence and machine learning. Sept 5, 2021. www.forbes.com/sites/bernardmarr/2019/08/23/the-amazing-ways-youtube-uses-artificial-intelligence-and-machine-learning?sh=47b3720858522
Chase, M.: Introduction to deep learning (2021). www.geeksforgeeks.org/introduction-deep-learning. Accessed 7 Sept 2021
IBM Cloud R&D: Introduction to AI, Deep Learning and AI. http://www.ibm.com/cloud/learn/deep-learning. Accessed 12 Sept 2021
Srivastava, A., Kundu, A., Sural, S., Majumdar, A.: Credit card fraud detection using hidden markov model. IEEE Trans. Dependable Secur. Comput. 5(1) (2008)
Behera, T.K., Panigrahi, S.: Credit card fraud detection: a hybrid approach using fuzzy clustering & neural network. In: 2015 Second International Conference on Advances in Computing and Communication Engineering (2015)
Modi, K., Dayma, R.: Review on fraud detection methods in credit card transactions. In: 2017 International Conference on Intelligent Computing and Control (I2C2) (2017)
Singla, J.: A survey of deep learning-based online transactions fraud detection systems. In: 2020 International Conference on Intelligent Engineering and Management (ICIEM), 2020, pp. 130–136 (2020). https://doi.org/10.1109/ICIEM48762.2020.9160200.
Chen, J., Shen, Y., Ali, R.: Credit card fraud detection using sparse autoencoder and generative adversarial network 1054–1059 (2018). https://doi.org/10.1109/IEMCON.2018.8614815
Misra, S., Thakur, S., Ghosh, M., Saha, S.K.: An autoencoder based model for detecting fraudulent credit card transaction. In: Procedia Computer Science (2020)
Saha, S.: Towards data science. In: A Comprehensive Guide to Convolutional Neural Networks - the ELI5 way (2021). https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53. Accessed 14 Sept 2021
Singh, P., Singh, M.: Fraud detection by monitoring customer behavior and activities. Int. J. Comput. Appl. 111(11) (2015). https://doi.org/10.1.1.695.5814
Prusti, D., Rath, S.K.: Fraudulent transaction detection in credit card by applying ensemble machine learning techniques. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–6 (2019). https://doi.org/10.1109/ICCCNT45670.2019.8944867
Ghattamaneni, S., Portilla, R., Gupta, N.: Combining rules-based and AI models to combat financial fraud. Eng Blog. https://databricks.com/blog/2021/01/19/combining-rules-based-and-ai-models-to-combat-financial-fraud.html. Accessed 23rd Sept 2021
Leonard, K.J.: The development of a rule-based expert system model for fraud alert in consumer credit. Eur. J. Oper. Res. 80(2), 350–356 (1995). ISSN 0377-2217, https://doi.org/10.1016/0377-2217(93)E0249-W, https://www.sciencedirect.com/science/article/pii/0377221793E0249W
Brownlee, J.: A gentle introduction to Generative Adversarial Networks (GANs), machine learning mastery (2021). https://machinelearningmastery.com/what-are-generative-adversarial-networks-gans/. Accessed 25 Sept 2021
Saha, S.: Towards data science. In: A Comprehensive Guide to Convolutional Neural Networks - the ELI5 way. https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53. Accessed 14 Sept 2021
Vatsa, V., Sural, S., Majumdar, A.: A rule-based and game-theoretic approach to online credit card fraud detection. IJISP 1, 26–46 (2007). https://doi.org/10.4018/jisp.2007070103
Maniraj, S.P., Saini, A., Ahmed, S., Sarkar, S.: Credit card fraud detection using machine learning and data science. Int. J. Eng. Res. Technol. (IJERT) 8(9) (2019)
Zheng, Y.J., Zhou, X.H., Sheng, W.G., Xue, Y., Chen, S.Y.: Generative adversarial network-based telecom fraud detection at the receiving bank. Neural Netw. 102, 78–86 (2018). ISSN 0893-6080, https://doi.org/10.1016/j.neunet.2018.02.015
Times of India: https://timesofindia.indiatimes.com/city/delhi/virus-of-cybercrime-over-3000-cases-every-month/articleshow/77967994.cms. Accessed 20 Jan 2021
Times of India: https://timesofindia.indiatimes.com/business/india-business/in-92-days-india-lost-rs-128-crore-in-card-online-fraud/articleshow/74571025.cms. Accessed 20 Jan 2021
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Rajendran, S., John, A.A., Suhas, B., Sahana, B. (2023). Role of ML and DL in Detecting Fraudulent Transactions. In: Biswas, A., Semwal, V.B., Singh, D. (eds) Artificial Intelligence for Societal Issues. Intelligent Systems Reference Library, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-031-12419-8_4
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DOI: https://doi.org/10.1007/978-3-031-12419-8_4
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