Machine Learning and Artificial Intelligence in Online Fake Transaction Alerting
Keywords:
Machine Learning (ML), Artificial Intelligence (AI), Fraud Transaction, Cyber Attacks, Algorithms TechnologyAbstract
Artificial Intelligence (AI) is one of the most promising and intriguing innovations of modernity. Its potential is virtually unlimited, from smart music selection in personal gadgets to intelligent analysis of big data and real-time fraud detection and aversion. At the core of the AI philosophy lies an assumption that once a computer system is provided with enough data, it can learn based on that input. The more data is provided, the more sophisticated its learning ability becomes. This feature has acquired the name "machine learning" (ML). The opportunities explored with ML are plentiful today, and one of them is an ability to set up an evolving security system learning from the past cyber-fraud experiences and developing more rigorous fraud detection mechanisms. Read on to learn more about ML, the types and magnitude of fraud evidenced in modern banking, e-commerce, and healthcare, and how ML has become an innovative, timely, and efficient fraud prevention technology.
Downloads
References
Bynagari, N. B. (2014). Integrated Reasoning Engine for Code Clone Detection. ABC Journal of Advanced Research, 3(2), 143-152. https://doi.org/10.18034/abcjar.v3i2.575
Donepudi, P. K. (2014). Voice Search Technology: An Overview. Engineering International, 2(2), 91-102. https://doi.org/10.18034/ei.v2i2.502
Neogy, T. K., & Paruchuri, H. (2014). Machine Learning as a New Search Engine Interface: An Overview. Engineering International, 2(2), 103-112. https://doi.org/10.18034/ei.v2i2.539
Paruchuri, H. (2015). Application of Artificial Neural Network to ANPR: An Overview. ABC Journal of Advanced Research, 4(2), 143-152. https://doi.org/10.18034/abcjar.v4i2.549
Taher-Uz-Zaman, M., Ahmed, M. S., Hossain, S., Hossain, S., & Jamal, G. R. A. (2014). Multipurpose Tactical Robot. Engineering International, 2(1), 21-27. https://doi.org/10.18034/ei.v2i1.204
Vadlamudi, S. (2015). Enabling Trustworthiness in Artificial Intelligence - A Detailed Discussion. Engineering International, 3(2), 105-114. https://doi.org/10.18034/ei.v3i2.519
Downloads
Published
Issue
Section
License
Engineering International follows a hybrid access and licensing model.
Standard publisher-controlled articles may not be reproduced, redistributed, modified, or reused without prior written permission from American Observer Press.
Articles published under the journal’s Open Access option may be distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0), as indicated on the individual article page.
