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Convolutional Neural Network Approach for Mobile Banking Fraudulent Transaction to Detect Financial Frauds

Soumya Shrivastava ., Punit Kumar Johari .
2022 International Journal of Engineering Technology and Management Sciences  
Thanks to their versatile nature to detect emergent financial transaction fraud, deep learning approaches were enticing candidates.  ...  In this article, we suggest an in-depth learning approach for adapting financial fraud through the use of convolution neural networks (CNN).  ...  mobile device.  ... 
doi:10.46647/ijetms.2022.v06i01.005 fatcat:h77a47rfrvbptdq5bv7xnta4kq

2021 Index IEEE Transactions on Mobile Computing Vol. 20

2022 IEEE Transactions on Mobile Computing  
Gui, Y., +, TMC Nov. 2021 3251-3266 Kollector: Detecting Fraudulent Activities on Mobile Devices Using Deep Distance measurement Learning.  ...  : Detecting Fraudulent Activities on Mobile Devices Using Deep Adaptive Predictive Power Management for Mobile LTE Devices.  ... 
doi:10.1109/tmc.2021.3133125 fatcat:xbdfaozpkjbbjm3gfbmxcmzj6i

Mobile Privacy in Machine Learning

Lichao Sun
2020
privacy while using machine learning on private data.  ...  (e.g., unauthorization or fraudulent activities detection), private data communication (e.g., local mobile photo labeling), and private model updating (e.g., local mobile AI model updating).  ...  12 : 1212 Figure 12: Results with Incremental Number of Classes (Users) ( This chapter was previously published as "KOLLECTOR: Detecting Fraudulent Activities on Mobile Devices Using Deep Learning (  ... 
doi:10.25417/uic.14134448.v1 fatcat:zb5adrhe5zgsxfzu7cj66jqpc4