Machine Learning-Driven Digital Identity Verification for Fraud Prevention in Digital Payment Technologies

Authors

Lichen Qin Department of Computer Science, University of Rochester, Rochester, NY, USA
Yuqiang Zhong Department of Information and Computer Sciences, Henan Agricultural University, Shenzhen, Guangdong, China
Han Wang Department of Mathematics, University of Southern California, Alhambra, CA, USA
Qishuo Cheng Department of Economics, University of Chicago, Chicago, IL, USA
Jinxin Xu Department of Cox Business School, Southern Methodist University, Dallas, TX, USA

DOI:

https://doi.org/10.53469/wjimt.2024.07(03).01

Keywords:

Machine learning, Digital authentication, Fraud prevention, Digital payment

Abstract

This article explores how machine learning techniques can be used to drive digital authentication to prevent fraud in digital payment technologies. First, it introduces the development trend and fraud risk of digital payment technology, and then analyzes the limitations of traditional authentication methods, focusing on the potential of machine learning in digital authentication. It then explores specific application scenarios of machine learning in digital authentication, including data collection and preparation, feature engineering, model selection and training, as well as real-time monitoring and anti-fraud processing. Finally, current challenges and solutions are discussed, as well as the future of machine learning in digital payment technology. Through in-depth analysis of these contents, the article aims to provide readers with valuable insights to help them better use machine learning technology to improve the security and reliability of digital payments and promote the sustainable development of the digital economy.

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Machine Learning-Driven Digital Identity Verification for Fraud Prevention in Digital Payment Technologies