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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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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