AI-based Identity Fraud Detection: A Systematic Review

TL;DR

This systematic review analyzes AI-based methods for detecting identity fraud, highlighting current techniques, challenges, and trends to guide future research and practical applications in digital security.

Contribution

The paper provides a comprehensive taxonomy and analysis of AI-driven identity fraud detection methods, identifying key insights and open challenges in the field.

Findings

Identified two main types of fraud detection methods.

Highlighted open challenges in AI-based identity fraud detection.

Consolidated trends and insights into current research directions.

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.