Machine Learning-Based Facial Recognition for Financial Fraud Prevention

Yong Wang; Xiaoan Zhan; Tong Zhan; Jiahao Xu; Xinzhu Bai

doi:10.5281/zenodo.11004115

Authors

Yong Wang University of Aberdeen
Xiaoan Zhan New York University
Tong Zhan Columbia University
Jiahao Xu University of Southern California
Xinzhu Bai Tianjin University of Finance and Economics

DOI:

https://doi.org/10.5281/zenodo.11004115

References:

30

Keywords:

Financial Fraud Prevention, Machine Learning Face Recognition, Deep Forgery Detection, Anti-deception Technology

Abstract

At present, face recognition theory and technology have achieved great success, and are widely used in key fields such as government, finance and military. Similar to other information systems, face recognition systems are also faced with various security problems, of which face spoofing (FS) is one of the most important security problems. The so-called face fraud refers to the attacker using printed photos, video playback and 3D masks and other attack methods to trick the face recognition system to make wrong judgments, so it is a key problem that the face recognition system must solve.This paper explores the application of machine learning face recognition technology in preventing financial fraud, aiming to provide effective fraud prevention solutions for the financial industry. It discusses the principles, methods, and practical cases of applying face recognition technology in various financial scenarios, such as transaction monitoring, identity verification, and payment security. The paper also delves into the challenges posed by face fraud forgery, highlighting the importance of adopting effective deep forgery detection technology. Additionally, it provides insights into machine learning face recognition models, their quantization methods, and the balance between recognition speed and precision. Finally, the paper emphasizes the significance of anti-deception technology in designing secure and reliable face recognition systems, focusing on sensor selection and algorithm optimization to enhance the system's resistance to deception attacks.

Author Biographies

Yong Wang, University of Aberdeen

Information Technology, University of Aberdeen, Aberdeen, United Kingdom.

Xiaoan Zhan, New York University

Electrical Engineering, New York University, NY, USA.

Tong Zhan, Columbia University

Computer Science, Columbia University, NY, USA.

Jiahao Xu, University of Southern California

Master of Science in Financial Engineering, University of Southern California, CA, USA.

Xinzhu Bai, Tianjin University of Finance and Economics

Tourism Management , Tianjin University of Finance and Economics, Tianjin, China.

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