AI-based Identity Fraud Detection: A Systematic Review

• URL: http://arxiv.org/abs/2501.09239v1
• Date: Thu, 16 Jan 2025 01:52:30 GMT
• Title: AI-based Identity Fraud Detection: A Systematic Review
• Authors: Chuo Jun Zhang, Asif Q. Gill, Bo Liu, Memoona J. Anwar,
• Abstract summary: This paper reviews a selected set of 43 papers across 4 major academic literature databases. Results highlight the two types of identity fraud prevention and detection methods, in-depth and open challenges. Overall, this paper provides a foundational knowledge base to researchers and practitioners for further research and development in this important area of digital identity fraud.
• Score: 1.8150583821390123
• License:
• Abstract: With the rapid development of digital services, a large volume of personally identifiable information (PII) is stored online and is subject to cyberattacks such as Identity fraud. Most recently, the use of Artificial Intelligence (AI) enabled deep fake technologies has significantly increased the complexity of identity fraud. Fraudsters may use these technologies to create highly sophisticated counterfeit personal identification documents, photos and videos. These advancements in the identity fraud landscape pose challenges for identity fraud detection and society at large. There is a pressing need to review and understand identity fraud detection methods, their limitations and potential solutions. This research aims to address this important need by using the well-known systematic literature review method. This paper reviewed a selected set of 43 papers across 4 major academic literature databases. In particular, the review results highlight the two types of identity fraud prevention and detection methods, in-depth and open challenges. The results were also consolidated into a taxonomy of AI-based identity fraud detection and prevention methods including key insights and trends. Overall, this paper provides a foundational knowledge base to researchers and practitioners for further research and development in this important area of digital identity fraud.

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