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Security and Communication Networks
Volume 2018, Article ID 5483472, 15 pages
https://doi.org/10.1155/2018/5483472
Research Article

An Artificial Intelligence Approach to Financial Fraud Detection under IoT Environment: A Survey and Implementation

Center for Information Security Technologies (CIST), Korea University, Seoul 02841, Republic of Korea

Correspondence should be addressed to Kyungho Lee; rk.ca.aerok@eelnivek

Received 7 March 2018; Revised 8 June 2018; Accepted 25 June 2018; Published 25 September 2018

Academic Editor: Ilsun You

Copyright © 2018 Dahee Choi and Kyungho Lee. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Financial fraud under IoT environment refers to the unauthorized use of mobile transaction using mobile platform through identity theft or credit card stealing to obtain money fraudulently. Financial fraud under IoT environment is the fast-growing issue through the emergence of smartphone and online transition services. In the real world, a highly accurate process of financial fraud detection under IoT environment is needed since financial fraud causes financial loss. Therefore, we have surveyed financial fraud methods using machine learning and deep learning methodology, mainly from 2016 to 2018, and proposed a process for accurate fraud detection based on the advantages and limitations of each research. Moreover, our approach proposed the overall process of detecting financial fraud based on machine learning and compared with artificial neural networks approach to detect fraud and process large amounts of financial data. To detect financial fraud and process large amounts of financial data, our proposed process includes feature selection, sampling, and applying supervised and unsupervised algorithms. The final model was validated by the actual financial transaction data occurring in Korea, 2015.