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

Dahee Choi, Kyung Ho Lee

Research output: Contribution to journalReview article

5 Citations (Scopus)

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.

Original languageEnglish
Article number5483472
JournalSecurity and Communication Networks
Volume2018
DOIs
Publication statusPublished - 2018 Jan 1

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'An Artificial Intelligence Approach to Financial Fraud Detection under IoT Environment: A Survey and Implementation'. Together they form a unique fingerprint.

  • Cite this